Jorge A. Baier

SoCS Conference 2024 Conference Paper

A New Upper Bound for the Makespan of Cost-Optimal Solutions for Multi-Agent Path Finding (Extended Abstract)

• Rodrigo López
• Roberto Javier Asín Achá
• Jorge A. Baier

A well-known approach to solving Multi-Agent Path Finding (MAPF) optimally is compilation to Boolean Satisfiability or Answer Set Programming (ASP). Such compilation-based approaches are superior to other approaches on dense, relatively small instances and may invoke the solver multiple times, each with an encoding of the same instance for a different makespan. Critical to their performance is the runtime of the last solver invocation, whose input is the instance encoded with a theoretical upper bound of the makespan of the optimal solution. In this paper, we propose a new theoretical upper bound for such a last invocation. Unlike the previously known bound, when given a MAPF instance P, our bound requires a solution to P_1, a version of P where one of its agents is removed. We prove that our bound is correct and experimentally significantly tighter than the previously known bound. We propose a recursive parallel approach that allows us to exploit our new bound effectively. Our evaluation of warehouses and random MAPF benchmarks of varied sizes shows that our bound is, on average, 21. 2% smaller than the previous bound. This allows for generating grounded ASP formulas around 33. 45% smaller and solving 4. 9% more instances.

SoCS Conference 2024 Conference Paper

Finding a Small, Diverse Subset of the Pareto Solution Set in Bi-Objective Search (Extended Abstract)

• Pablo Araneda
• Carlos Hernández Ulloa
• Nicolás Rivera
• Jorge A. Baier

Bi-objective search requires computing a Pareto solution set which contains a set of paths. In real-world applications, Pareto solution sets may contain several tens or even hundreds of solutions. For a human user trying to commit to just one of these paths, navigating through a large solution set may become overwhelming, which motivates the problem of computing small, good-quality subsets of Pareto frontiers. This document presents two main contributions. First, we provide a simple formalization of good-quality subsets of a Pareto solution set. For this, we use measure of richness which has been employed in the study of Population Dynamics. Second, we propose Chebyshev BOA*, a variant of BOA* to compute good-quality subset approximations.

IJCAI Conference 2023 Conference Paper

Multi-objective Search via Lazy and Efficient Dominance Checks

• Carlos Hernández
• William Yeoh
• Jorge A. Baier
• Ariel Felner
• Oren Salzman
• Han Zhang
• Shao-Hung Chan
• Sven Koenig

Multi-objective search can be used to model many real-world problems that require finding Pareto optimal paths from a specified start state to a specified goal state, while considering different costmetrics such as distance, time, and fuel. The performance of multi-objective search can be improved by making dominance checking—an operation necessary to determine whether or not a path dominates another—more efficient. This was shown in practice by BOA*, a state-of-the-art bi-objective search algorithm, which outperforms previously existing bi-objective search algorithms in part because it adopts a lazy approach towards dominance checking. EMOA*, a recent multi-objective search algorithm, generalizes BOA* to more-than-two objectives using AVL trees for dominance checking. In this paper, we first propose Linear-Time Multi-Objective A* (LTMOA*), an multi-objective search algorithm that implements a more efficient dominance checking than EMOA* using simple data structures like arrays. We then propose an even lazier approach towards dominance checking, and the resulting algorithm, LazyLTMOA*, distinguishes from EMOA* and LTMOA* by removing the dominance checking during node generation. Our experimental results show that LazyLTMOA* outperforms EMOA* by up to an order of magnitude in terms of runtime.

AIJ Journal 2023 Journal Article

Simple and efficient bi-objective search algorithms via fast dominance checks

• Carlos Hernández
• William Yeoh
• Jorge A. Baier
• Han Zhang
• Luis Suazo
• Sven Koenig
• Oren Salzman

Many interesting search problems can be formulated as bi-objective search problems, that is, search problems where two kinds of costs have to be minimized, for example, travel distance and time for transportation problems. Instead of looking for a single optimal path, we compute a Pareto-optimal frontier in bi-objective search, which is a set of paths in which no two paths dominate each other. Bi-objective search algorithms perform dominance checks each time a new path is discovered. Thus, the efficiency of these checks is key to performance. In this article, we propose algorithms for two kinds of bi-objective search problems. First, we consider the problem of computing the Pareto-optimal frontier of the paths that connect a given start state with a given goal state. We propose Bi-Objective A* (BOA*), a heuristic search algorithm based on A*, for this problem. Second, we consider the problem of computing one Pareto-optimal frontier for each state s of the search graph, which contains the paths that connect a given start state with s. We propose Bi-Objective Dijkstra (BOD), which is based on BOA*, for this problem. A common feature of BOA* and BOD is that all dominance checks are performed in constant time, unlike the dominance checks of previous algorithms. We show in our experimental evaluation that both BOA* and BOD are substantially faster than state-of-the-art bi-objective search algorithms.

SoCS Conference 2022 Conference Paper

Avoiding Errors in Learned Heuristics in Bounded-Suboptimal Search

• Matias Greco
• Jorge A. Baier

Despite being very effective, learned heuristics in bounded-suboptimal search can produce heuristic plateaus or move the search to zones of the state space that do not lead to a solution. In addition, it produces inadmissible cost-to-go estimates; therefore, it cannot be exploited with classical algorithms like WA* to produce w-optimal solutions. In this paper, we present two ways in which Focal Search can be modified to exploit a learned heuristic in a bounded suboptimal search: Focal Discrepancy Search, which, to evaluate each state, uses a discrepancy score based on the best-predicted heuristic value; and K-Focal Search, which expands more than one node from the FOCAL list in each expansion cycle. Both algorithms return w-optimal solutions and explore different zones of the state space than the ones that focal search, using the learned heuristic to sort the FOCAL list, would explore.

SoCS Conference 2022 Conference Paper

Focal Discrepancy Search for Learned Heuristics (Extended Abstract)

• Matias Greco
• Pablo Araneda
• Jorge A. Baier

Machine learning allows learning accurate but inadmissible heuristics for hard combinatorial puzzles like the 15-puzzle, the 24-puzzle, and Rubik

SoCS Conference 2022 Conference Paper

K-Focal Search for Slow Learned Heuristics (Extended Abstract)

• Matias Greco
• Jorge Toro
• Carlos Hernández Ulloa
• Jorge A. Baier

Learned heuristics, though inadmissible, can provide very good guidance for bounded-suboptimal search. Given a single search state s and a learned heuristic h, evaluating h(s) is typically very slow relative to expansion time, since state-of-the-art learned heuristics are implemented as neural networks. However, by using a Graphics Processing Unit (GPU), it is possible to compute heuristics using batched computation. Existing approaches to batched heuristic computation are specific to satisficing search and have not studied the problem in the context of bounded-suboptimal search. In this paper, we present K-Focal Search, a bounded suboptimal search algorithm that in each iteration expands K nodes from the FOCAL list and computes the learned heuristic values of the successors using a GPU. We experiment over the Rubik

AIJ Journal 2022 Journal Article

Knowledge-based programs as building blocks for planning

• Jorge A. Baier
• Sheila A. McIlraith

Knowledge-based programs contain both world-altering actions, which upon execution change the state of the world, and sensing actions, which upon execution change the knowledge state of the agent. Knowledge-based programming has been proposed as an alternative to planning, since programs allow solving families of planning problems. Notwithstanding, agents equipped with a variety of knowledge-based procedures can compose these procedures to achieve goals, exhibiting greater degrees of flexibility. Optimized state-of-the-art planners, unfortunately, cannot be used directly to compose programs since they require operators (not programs), defined by preconditions and effects. In this article we study how to compute preconditions and effects of knowledge-based programs in order to allow state-of-the-art planners to construct plans with knowledge-based programs as building blocks. We study the problem in the language of the situation calculus, appealing to Golog to represent our programs. To this end, we propose an offline execution semantics for Golog programs with sensing. We then propose a compilation method that transforms our action theory with programs into a new theory where programs are replaced by primitive actions. This enables us to use state-of-the-art, operator-based planning techniques to plan with programs that sense for a restricted but compelling class of problems. Finally, we discuss the applicability of these results to existing operator-based planners that support sensing and illustrate the computational advantage of planning with programs that sense via an experiment.

JAIR Journal 2022 Journal Article

Multi-Agent Path Finding: A New Boolean Encoding

• Roberto Asín Achá
• Rodrigo López
• Sebastian Hagedorn
• Jorge A. Baier

Multi-agent pathfinding (MAPF) is an NP-hard problem. As such, dense maps may be very hard to solve optimally. In such scenarios, compilation-based approaches, via Boolean satisfiability (SAT) and answer set programming (ASP), have been shown to outperform heuristic-search-based approaches, such as conflict-based search (CBS). In this paper, we propose a new Boolean encoding for MAPF, and show how to implement it in ASP and MaxSAT. A feature that distinguishes our encoding from existing ones is that swap and follow conflicts are encoded using binary clauses, which can be exploited by current conflict-driven clause learning (CDCL) solvers. In addition, the number of clauses used to encode swap and follow conflicts do not depend on the number of agents, allowing us to scale better. For MaxSAT, we study different ways in which we may combine the MSU3 and LSU algorithms for maximum performance. In our experimental evaluation, we used square grids, ranging from 20 x 20 to 50 x 50 cells, and warehouse maps, with a varying number of agents and obstacles. We compared against representative solvers of the state-of-the-art, including the search-based algorithm CBS, the ASP-based solver ASP-MAPF, and the branch-and-cut-and-price hybrid solver, BCP. We observe that the ASP implementation of our encoding, ASP-MAPF2 outperforms other solvers in most of our experiments. The MaxSAT implementation of our encoding, MtMS shows best performance in relatively small warehouse maps when the number of agents is large, which are the instances with closer resemblance to hard puzzle-like problems.

PRL Workshop 2022 Workshop Paper

Scaling up ML-based Black-box Planning with Partial STRIPS Models

• Matias Greco
• Álvaro Torralba
• Jorge A. Baier
• Hector Palacios

A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like policy learning. On the other hand, modelrelaxation heuristics can guide the search effectively if a full declarative model is available. In this work, we consider how a practitioner can improve ML-based black-box planning on settings where a complete symbolic model is not available. We show that specifying an incomplete STRIPS model that describes only part of the problem enables the use of relaxation heuristics. Our findings on several planning domains suggest that this is an effective way to improve ML-based black-box planning beyond collecting more data or tuning ML architectures.

AAAI Conference 2022 Conference Paper

Subset Approximation of Pareto Regions with Bi-objective A*

• Nicolás Rivera
• Jorge A. Baier
• Carlos Hernández

In bi-objective search, we are given a graph in which each directed arc is associated with a pair of non-negative weights, and the objective is to find the Pareto-optimal solution set. Unfortunately, in many practical settings, this set is too large, and therefore its computation is very time-consuming. In addition, even though bi-objective search algorithms generate the Pareto set incrementally, they do so exhaustively. This means that early during search the solution set covered is not diverse, being concentrated in a small region. To address this issue, we present a new approach to subset approximation of the solution set, that can be used as the basis for an anytime bi-objective search algorithm. Our approach transforms the given task into a target bi-objective search task using two real parameters. For each particular parameter setting, the solutions to the target task is a subset of the solution set of the original task. Depending on the parameters used, the solution set of the target task may be computed very quickly. This allows us to obtain, in challenging road map benchmarks, a rich variety of solutions in times that may be orders of magnitude smaller than the time needed to compute the solution set. We show that by running the algorithm with an appropriate sequence of parameters, we obtain a growing sequence of solutions that converges to the full solution set. We prove that our approach is correct and that Bi-Objective A* prunes at least as many nodes when run over the target task.

SoCS Conference 2022 Conference Paper

Subset Approximation of Pareto Regions with Bi-Objective A* (Extended Abstract)

• Jorge A. Baier
• Nicolás Rivera
• Carlos Hernández Ulloa

In bi-objective search, we are given a graph in which each directed arc is associated with a pair of non-negative weights, and the objective is to find the Pareto-optimal solution set. Unfortunately, in many practical settings, this set is too large, and therefore its computation is very time-consuming. In addition, even though bi-objective search algorithms generate the Pareto set incrementally, they do so exhaustively. This means that early during search the solution set covered is not diverse, being concentrated in a small region. To address this issue, we present a new approach to subset approximation of the solution set, that can be used as the basis for an anytime bi-objective search algorithm. Our approach transforms the given task into a target bi-objective search task using two real parameters. For each particular parameter setting, the solutions to the target task is a subset of the solution set of the original task. Depending on the parameters used, the solution set of the target task may be computed very quickly. This allows us to obtain, in challenging road map benchmarks, a rich variety of solutions in times that may be orders of magnitude smaller than the time needed to compute the solution set. We show that by running the algorithm with an appropriate sequence of parameters, we obtain a growing sequence of solutions that converges to the full solution set. We prove that our approach is correct and that Bi-Objective A* prunes at least as many nodes when run over the target task.

SoCS Conference 2021 Conference Paper

A New Boolean Encoding for MAPF and its Performance with ASP and MaxSAT Solvers

• Roberto Javier Asín Achá
• Rodrigo López
• Sebastián Hagedorn
• Jorge A. Baier

Multi-agent pathfinding (MAPF) is an NP-hard problem. As such, dense maps may be very hard to solve optimally. In such scenarios, compilation-based approaches, via Boolean satisfiability (SAT) and answer set programming (ASP), have proven to be most effective. In this paper, we propose a new encoding for MAPF, which we implement and solve using both ASP and MaxSAT solvers. Our encoding builds on a recent ASP encoding for MAPF but changes the way agent moves are encoded. This allows to represent swap and follow conflicts with binary clauses, which are known to work well along with conflict-based clause learning. For MaxSAT, we study different ways in which we may combine the MSU3 and LSU algorithms for maximum performance. Our results, over grid and warehouse maps, show that the ASP solver scales better when the number of agents is increased on grids with few obstacles, while the MaxSAT solver performs better in scenarios with more obstacles and fewer agents.

PRL Workshop 2021 Workshop Paper

Bounded-Suboptimal Search with Learned Heuristics

• Matias Greco
• Jorge A. Baier

Reinforcement learning allows learning very accurate heuristics for hard combinatorial puzzles like the 15-puzzle, the 24puzzle, and Rubik’s cube. In this paper, we empirically investigate how to exploit these learned heuristics in the context of (deterministic) heuristic search with bounded suboptimality guarantees, using the learned heuristic for the 15 and 24puzzle of DeepCubeA. We show that Focal Search (FS), in its most straightforward form, that is, using the learned heuristic to sort the focal list, has poor performance when compared to Focal Discrepancy Search (FDS), a version of FS that we propose that uses a discrepancy function to sort the focal list. This is interesting the best performing algorithm does not use the heuristic values themselves but just the ranking between the successors of the node. In addition, we show FDS is competitive with satisficing search algorithms Weighted A* and Greedy Best-First Search.

SoCS Conference 2021 Conference Paper

Exploiting Learned Policies in Focal Search

• Pablo Araneda
• Matias Greco
• Jorge A. Baier

Recent machine-learning approaches to deterministic search and domain-independent planning employ policy learning to speed up search. Unfortunately, when attempting to solve a search problem by successively applying a policy, no guarantees can be given on solution quality. The problem of how to effectively use a learned policy within a bounded-suboptimal search algorithm remains largely as an open question. In this paper, we propose various ways in which such policies can be integrated into Focal Search, assuming that the policy is a neural network classifier. Furthermore, we provide mathematical foundations for some of the resulting algorithms. To evaluate the resulting algorithms over a number of policies with varying accuracy, we use synthetic policies which can be generated for a target accuracy for problems where the search space can be held in memory. We evaluate our focal search variants over three benchmark domains using our synthetic approach, and on the 15-puzzle using a neural network learned using 1. 5 million examples. We observe that Discrepancy Focal Search, which we show expands the node which maximizes an approximation of the probability that its corresponding path is a prefix of an optimal path, obtains, in general, the best results in terms of runtime and solution quality.

SoCS Conference 2020 Conference Paper

A Simple and Fast Bi-Objective Search Algorithm

• Carlos Hernández Ulloa
• William Yeoh 0001
• Jorge A. Baier
• Luis Suazo
• Han Zhang 0018
• Sven Koenig

Many interesting search problems can be formulated as bi-objective search problems; for example, transportation problems where both travel distance and time need to be minimized. Multi-objective best-first search algorithms need to maintain the set of undominated paths from the start state to each state to compute a set of paths from a given start state to a given goal state (the Pareto-optimal solutions) such that no path in the set is dominated by another path in the set. Each time they find a new path to a state n, they perform a dominance check to determine whether such a path dominates any of the previously found paths to n. Existing algorithms do not perform these checks efficiently, requiring at least a full iteration over the Open list per check. In this paper, we present the first multi-objective algorithm that performs these checks efficiently. Indeed, Bi-Objective A* (BOA*)—our algorithm—requires constant time to check for dominance. Our experimental evaluation shows that BOA*is orders-of-magnitude faster than state-of-the-art search algorithms, such as NAMOA*, Bi-Objective Dijkstra, and Bidirectional Bi-Objective Dijkstra.

ICAPS Conference 2020 Conference Paper

A Simple and Fast Bi-Objective Search Algorithm

• Carlos Hernández Ulloa
• William Yeoh 0001
• Jorge A. Baier
• Han Zhang 0018
• Luis Suazo
• Sven Koenig

Many interesting search problems can be formulated as bi-objective search problems, that is, search problems where two kinds of costs have to be minimized, for example, travel distance and time for transportation problems. Bi-objective search algorithms have to maintain the set of undominated paths from the start state to each state to compute the set of paths from the start state to the goal state that are not dominated by some other path from the start state to the goal state (called the Pareto-optimal solution set). Each time they find a new path to a state s, they perform a dominance check to determine whether this path dominates any of the previously found paths to s or whether any of the previously found paths to s dominates this path. Existing algorithms do not perform these checks efficiently. On the other hand, our Bi-Objective A* (BOA*) algorithm requires only constant time per check. In our experimental evaluation, we show that BOA* can run an order of magnitude (or more) faster than state-of-the-art bi-objective search algorithms, such as NAMOA*, NAMOA*dr, Bi-Objective Dijkstra, and Bidirectional Bi-Objective Dijkstra.

AAAI Conference 2020 Conference Paper

Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming

• Rodrigo N. Gómez
• Carlos Hernández
• Jorge A. Baier

Solving a Multi-Agent Pathfinding (MAPF) problem involves finding non-conflicting paths that lead a number of agents to their goal location. In the sum-of-costs variant of MAPF, one is also required to minimize the total number of moves performed by agents before stopping at the goal. Not surprisingly, since MAPF is combinatorial, a number of compilations to Satisfiability solving (SAT) and Answer Set Programming (ASP) exist. In this paper, we propose the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Unlike existing compilations to ASP that we are aware of, our encoding is the first that, after grounding, produces a number of clauses that is linear on the number of agents. In addition, the representation of the optimization objective is also carefully written, such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search- and SAT-based sum-of-costs MAPF solvers when grids are congested with agents.

SoCS Conference 2019 Conference Paper

A Learning-Based Framework for Memory-Bounded Heuristic Search: First Results

• Carlos Hernández Ulloa
• Jorge A. Baier
• William Yeoh 0001
• Vadim Bulitko
• Sven Koenig

Many existing boundedly-suboptimal heuristic search algorithms are variants of best-first search. Due to memory limitations, these algorithms are unable to solve problems with extremely large search spaces. In this paper, we present a framework that allows best-first search algorithms to solve problems with such large search spaces given a (reasonable) memory bound while also preserving optimality guarantees in tree-structured search spaces. In our framework, a given algorithm is run several times. In each search episode, the algorithm expands up to a user-defined number of states. After each episode, unless the goal has been found, the heuristic values of the generated states are updated using a linear-time algorithm that preserves consistency in tree-structured search spaces. In subsequent search episodes, only the heuristic values of the states generated in the previous episode need to be kept in memory. We present experimental results where we plug A*, GBFS, and wA* into our framework to solve traveling salesman problems and compare them against benchmark linear-memory algorithms like DFBnB and wDFBnB.

SoCS Conference 2019 Conference Paper

Compiling Cost-Optimal Multi-Agent Pathfinding to ASP

• Rodrigo N. Gómez
• Carlos Hernández Ulloa
• Jorge A. Baier

Multi-Agent Pathfinding (MAPF) over grids is the problem of finding n non-conflicting paths that lead n agents from a given initial cell to a given goal cell. Cost-optimal MAPF in addition minimizes the total number of actions performed by each agent before stopping at the goal. Being a combinatorial problem in nature, a number of compilations from MAPF to Answer Set Programming (ASP) exist. In this paper we propose a new one, which unlike existing ASP approaches (1) produces cost-optimal solutions, (2) exploits information that can be pre-computed quickly using Dijkstra

SoCS Conference 2018 Conference Paper

A Neural Network for Decision Making in Real-Time Heuristic Search

• Franco Muñoz
• Miguel Fadic
• Carlos Hernández Ulloa
• Jorge A. Baier

Most real-time heuristic search algorithms solve search problems by executing a series of episodes. During each episode the algorithm decides an action for execution. Such a decision is usually made using information gathered by running a bounded, heuristic-search algorithm. In this paper we report on a real-time search algorithm that does not use a search algorithm to choose the next action to be applied. Rather, it uses a neural network whose input is local information about the search graph, comparable to the information that would be used by a bounded search algorithm. We describe a supervised learning approach to training such a network. Our three types of maps from the Moving AI benchmarks, shows that our algorithm is, in some cases, substantially superior to algorithms that have access to the same information about the graph. One of our most important conclusions is that our extended set of features important: indeed, using features beyond the heuristic seems key to achieving good performance.

SoCS Conference 2018 Conference Paper

A Suboptimality Bound for 2 k Grid Path Planning

• Benjamín Kramm
• Nicolás Rivera
• Carlos Hernández Ulloa
• Jorge A. Baier

The 2k neighborhood has been recently proposed as an alternative to optimal any-angle path planning over grids. Even though it has been observed empirically that the quality of solutions approaches the cost of an optimal any-angle path as k is increased, no theoretical bounds were known. In this paper we study the ratio between the solutions obtained by an any-angle path and the optimal path in the 2kk, that generalizes previously known bounds for the 4- and 8-connected grids. We analyze two cases: when vertices of the search graph are placed (1) at the corners of grid cells, and (2) when they are located at their centers. For case (1) we obtain a suboptimality bound of 1 + 1/8k2 + O(1/k3), which is tight; for (2), however, worst-case suboptimality is a fixed value, for every k ≤ 3. Our results strongly suggests that vertices need to be placed in corners in order to obtain near-optimal solutions. In an empirical analysis, we compare theoretical and experimental suboptimality.

ICAPS Conference 2018 Conference Paper

Finite LTL Synthesis as Planning

• Alberto Camacho
• Jorge A. Baier
• Christian J. Muise
• Sheila A. McIlraith

LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic (LTL) specification interpreted over infinite traces. In this paper we examine the problem of LTLf synthesis, a variant of LTL synthesis where the specification of the behaviour of the strategy we generate is interpreted over finite traces -- similar to the assumption we make in many planning problems, and important for the synthesis of business processes and other system interactions of finite duration. Existing approaches to LTLf synthesis transform LTLf into deterministic finite-state automata (DFA) and reduce the synthesis problem to a DFA game. Unfortunately, the DFA transformation is worst-case double-exponential in the size of the formula, presenting a computational bottleneck. In contrast, our approach exploits non-deterministic automata, and we reduce the synthesis problem to a non-deterministic planning problem. We leverage our approach not only for strategy generation but also to generate certificates of unrealizability -- the first such method for LTLf. We employ a battery of techniques that exploit the structure of the LTLf specification to improve the efficiency of our transformation to automata. We combine these techniques with lazy determinization of automata and on-the-fly state abstraction. We illustrate the effectiveness of our approach on a set of established LTL synthesis benchmarks adapted to finite LTL.

IJCAI Conference 2018 Conference Paper

LTL Realizability via Safety and Reachability Games

• Alberto Camacho
• Christian Muise
• Jorge A. Baier
• Sheila A. McIlraith

In this paper, we address the problem of LTL realizability and synthesis. State of the art techniques rely on so-called bounded synthesis methods, which reduce the problem to a safety game. Realizability is determined by solving synthesis in a dual game. We provide a unified view of duality, and introduce novel bounded realizability methods via reductions to reachability games. Further, we introduce algorithms, based on AI automated planning, to solve these safety and reachability games. This is the the first complete approach to LTL realizability and synthesis via automated planning. Experiments illustrate that reductions to reachability games are an alternative to reductions to safety games, and show that planning can be a competitive approach to LTL realizability and synthesis.

IJCAI Conference 2018 Conference Paper

SynKit: LTL Synthesis as a Service

• Alberto Camacho
• Christian Muise
• Jorge A. Baier
• Sheila A. McIlraith

Automatic synthesis of software from specification is one of the classic problems in computer science. In the last decade, significant advances have been made in the synthesis of programs from specifications expressed in Linear Temporal Logic (LTL). LTL synthesis technology is central to a myriad of applications from the automated generation of controllers for Internet of Things devices, to the synthesis of control software for robotic applications. Unfortunately, the number of existing tools for LTL synthesis is limited, and using them requires specialized expertise. In this paper we present SynKit, a tool that offers LTL synthesis as a service. SynKit integrates a RESTful API and a web service with an editor, a solver, and a strategy visualizer.

SoCS Conference 2017 Conference Paper

Fast and Almost Optimal Any-Angle Pathfinding Using the 2 k Neighborhoods

• Nicolás Hormazábal
• Antonio Díaz
• Carlos Hernández Ulloa
• Jorge A. Baier

Any-angle path finding on grids is an important problem with applications in autonomous robot navigation. In this paper, we show that a well-known pre-processing technique, namely subgoal graphs, originally proposed for (non any-angle) 8-connected grids, can be straightforwardly adapted to the 2k neighborhoods, a family of neighborhoods that allow an increasing number of movements (and angles) as k is increased. This observation yields a pathfinder that computes 2k-optimal paths very quickly. Compared to ANYA, an optimal true any-angle planner, over a variety of benchmarks, our planner is one order of magnitude faster while being less than 0. 0005% suboptimal. Important to our planner

IJCAI Conference 2017 Conference Paper

How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval

• Rodrigo Toro Icarte
• Jorge A. Baier
• Cristian Ruz
• Alvaro Soto

The knowledge representation community has built general-purpose ontologies which contain large amounts of commonsense knowledge over relevant aspects of the world, including useful visual information, e. g. : "a ball is used by a football player", "a tennis player is located at a tennis court". Current state-of-the-art approaches for visual recognition do not exploit these rule-based knowledge sources. Instead, they learn recognition models directly from training examples. In this paper, we study how general-purpose ontologies—specifically, MIT's ConceptNet ontology—can improve the performance of state-of-the-art vision systems. As a testbed, we tackle the problem of sentence-based image retrieval. Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique. In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset. Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet. Consequently, a main conclusion of this work is that general-purpose commonsense ontologies improve performance on visual reasoning tasks when properly filtered to select meaningful visual relations.

ICAPS Conference 2017 Conference Paper

Improving MPGAA* for Extended Visibility Ranges

• Carlos Hernández Ulloa
• Jorge A. Baier

Multipath Generalized Adaptive A* (MPGAA*) is an A*-based incremental search algorithm for dynamic terrain that can outperform D* for the (realistic) case of limited visibility ranges. A first contribution of this paper is a brief analysis studying why MPGAA* has poor performance for extended visibility ranges, which concludes that MPGAA* carries out an excessive number of heuristic updates. Our second contribution is a method to reduce the number of heuristic updates that preserves optimality. Finally, a third contribution is a variant of MPGAA*, MPGAA*-back, which we show outperforms MPGAA* and D* on a wide range of dynamic grid pathfinding scenarios, and visibility ranges.

IJCAI Conference 2017 Conference Paper

Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads

• Carlos Hernandez
• Adi Botea
• Jorge A. Baier
• Vadim Bulitko

Real-time search algorithms are relevant to time-sensitive decision-making domains such as video games and robotics. In such settings, the agent is required to decide on each action under a constant time bound, regardless of the search space size. Despite recent progress, poor-quality solutions can be produced mainly due to state re-visitation. Different techniques have been developed to reduce such a re-visitation with state pruning showing promise. In this paper, we propose a novel pruning approach applicable to the wide class of real-time search algorithms. Given a local search space of arbitrary size, our technique aggressively prunes away all states in its interior, possibly adding new edges to maintain the connectivity of the search space frontier. An experimental evaluation shows that our pruning often improves the performance of a base real-time search algorithm by over an order of magnitude. This allows our implemented system to outperform state-of-the-art real-time search algorithms used in the evaluation.

IJCAI Conference 2016 Conference Paper

Incomplete Causal Laws in the Situation Calculus Using Free Fluents

• Marcelo Arenas
• Jorge A. Baier
• Juan S. Navarro
• Sebastian Sardina

We propose a simple relaxation of Reiter's basic action theories, based on fluents without successor state axioms, that accommodates incompleteness beyond the initial database. We prove that fundamental results about basic action theories can be fully recovered and that the generalized framework allows for natural specifications of various forms of incomplete causal laws. We illustrate this by showing how the evolution of incomplete databases, guarded action theories, and non-deterministic actions can be conveniently specified.

JAIR Journal 2016 Journal Article

Time-Bounded Best-First Search for Reversible and Non-reversible Search Graphs

• Carlos Hernández
• Jorge A. Baier
• Roberto Asín

Time-Bounded A* is a real-time, single-agent, deterministic search algorithm that expands states of a graph in the same order as A* does, but that unlike A* interleaves search and action execution. Known to outperform state-of-the-art real-time search algorithms based on Korf's Learning Real-Time A* (LRTA*) in some benchmarks, it has not been studied in detail and is sometimes not considered as a ``true'' real-time search algorithm since it fails in non-reversible problems even it the goal is still reachable from the current state. In this paper we propose and study Time-Bounded Best-First Search (TB(BFS)) a straightforward generalization of the time-bounded approach to any best-first search algorithm. Furthermore, we propose Restarting Time-Bounded Weighted A* (TB_R(WA*)), an algorithm that deals more adequately with non-reversible search graphs, eliminating ``backtracking moves'' and incorporating search restarts and heuristic learning. In non-reversible problems we prove that TB(BFS) terminates and we deduce cost bounds for the solutions returned by Time-Bounded Weighted A* (TB(WA*)), an instance of TB(BFS). Furthermore, we prove TB_R(WA*), under reasonable conditions, terminates. We evaluate TB(WA) in both grid pathfinding and the 15-puzzle. In addition, we evaluate TB_R(WA*) on the racetrack problem. We compare our algorithms to LSS-LRTWA*, a variant of LRTA* that can exploit lookahead search and a weighted heuristic. A general observation is that the performance of both TB(WA*) and TB_R(WA*) improves as the weight parameter is increased. In addition, our time-bounded algorithms almost always outperform LSS-LRTWA* by a significant margin.

AIJ Journal 2015 Journal Article

Incorporating weights into real-time heuristic search

• Nicolás Rivera
• Jorge A. Baier
• Carlos Hernández

Multiplying the heuristic function by a weight greater than one is a well-known technique in heuristic search. When this technique is applied to A* with an admissible heuristic it yields substantial runtime savings, at the expense of sacrificing solution optimality. Its applicability to real-time heuristic search, a search approach that builds upon heuristic search, however, has only been explored by a few studies. In this article we present two new approaches to using weights in real-time heuristic search, applicable to a wide range of algorithms. The first one, weighted lookahead, is a variant of an existing approach by Shimbo and Ishida, and utilizes the weight while the algorithm performs lookahead search. The second one, weighted update, incorporates the weight to the edges of the search graph during the learning phase. We implemented both techniques within LSS-LRTA* and evaluated them in path-planning benchmarks. We show that weighted lookahead outperforms an existing approach by Shimbo and Ishida but that it does not improve over existing approaches that do not use weights. Weighted update, on the other hand, yields performance improvements of up to one order of magnitude both in solution cost and total search time. To illustrate further the generality of weighted update, we incorporate the technique in two other well-known real-time heuristic search algorithms: LRTA*-LS and daLSS-LRTA*, and we empirically show significant improvements for LRTA*-LS and modest but still important improvements for daLSS-LRTA*. We analyze the properties of weighted update in depth, showing, among other things, that it guarantees termination. Convergence behavior of LSS-LRTA*, modified to use weighted update, is also analyzed. In such a setting, we prove solutions are w-optimal, and provide additional bounds on solution quality that in practice are tighter than w-optimality.

IJCAI Conference 2015 Conference Paper

Polynomial-Time Reformulations of LTL Temporally Extended Goals into Final-State Goals

• Jorge Torres
• Jorge A. Baier

Linear temporal logic (LTL) is an expressive language that allows specifying temporally extended goals and preferences. A general approach to dealing with general LTL properties in planning is by “compiling them away”; i. e. , in a pre-processing phase, all LTL formulas are converted into simple, non-temporal formulas that can be evaluated in a planning state. This is accomplished by first generating a finite-state automaton for the formula, and then by introducing new fluents that are used to capture all possible runs of the automaton. Unfortunately, current translation approaches are worstcase exponential on the size of the LTL formula. In this paper, we present a polynomial approach to compiling away LTL goals. Our method relies on the exploitation of alternating automata. Since alternating automata are different from nondeterministic automata, our translation technique does not capture all possible runs in a planning state and thus is very different from previous approaches. We prove that our translation is sound and complete, and evaluate it empirically showing that it has strengths and weaknesses. Specifically, we find classes of formulas in which it seems to outperform significantly the current state of the art.

ICAPS Conference 2014 Conference Paper

Making A* Run Faster than D*-Lite for Path-Planning in Partially Known Terrain

• Carlos Hernández Ulloa
• Jorge A. Baier
• Roberto Javier Asín Achá

Focused D* and D*-Lite are two popular incremental heuristic search algorithm amenable to goal-directed navigation in partially known terrain. Recently it has been shown that, unlike commonly believed, a version of A* is in many cases faster than D*-Lite, posing the question of whether or not there exist other variants of A* which could outperform algorithms in the D* family on most problems. In this paper we present Multipath Adaptive A* (MPAA*), a simple, easy-to-implement modification of Adaptive A* (AA*) that reuses paths found in previous searches to speed up subsequent searches, and that almost always outperforms D*Lite. We evaluate MPAA* against D*-Lite on random maps and standard game, room, and maze maps, assuming partially known terrain. In environments comparable to indoor and outdoor navigation (room and game maps) MPAA* is 35% faster than D*Lite on average, while on random maps MPAA* is over 3 times faster than D*Lite. D*Lite is faster than MPAA* only in mazes; notwithstanding, we show that if a small percentage of obstacle cells in a maze are made traversable, MPAA* outperforms D*Lite. In addition, we prove MPAA* is optimal and that it finds a solution if one exists. We conclude that for most real-life goal-directed navigation applications MPAA* should be preferred to D*Lite.

SoCS Conference 2014 Conference Paper

Time-Bounded Best-First Search

• Carlos Hernández Ulloa
• Roberto Javier Asín Achá
• Jorge A. Baier

Time-Bounded A* (TBA*) is a single-agent deterministic search algorithm that expands states of a graph in the same order as A* does, but that unlike A* interleaves search and action execution. Although the idea underlying TBA* can be generalized to other single-agent deterministic search algorithms, little is known about the impact on performance that would result from using algorithms other than A*. In this paper we propose Time-Bounded Best-First Search (TB-BFS) a generalization of the time-bounded approach to any best-first search algorithm. Furthermore, we propose restarting strategies that allow TB-BFS to solve search problems in dynamic environments. In static environments, we prove that the resulting framework allows agents to always find a solution if such a solution exists, and prove cost bounds for the solutions returned by Time-Bounded Weighted A* (TB-WA*). We evaluate the performance of TB-WA* and Time-Bounded Greedy Best-First Search (TB-GBFS). We show that in pathfinding applications in static domains, TB-WA* and TB-GBFS are not only faster than TBA* but also find significantly better solutions in terms of cost. In the context of videogame pathfinding, TB-WA* and TB-GBFS perform fewer undesired movements than TBA*. Restarting TB-WA* was also evaluated in dynamic pathfinding random maps, where we also observed improved performance compared to restarting TBA*. Our experimental results seem consistent with theoretical bounds.

SoCS Conference 2014 Conference Paper

Toward a Search Strategy for Anytime Search in Linear Space Using Depth-First Branch and Bound

• Carlos Hernández Ulloa
• Jorge A. Baier

Depth-First Branch and Bound (DFBnB) is an anytime algorithm for solving combinatorial optimization problems. In this paper we present a weighted version of DFBnB, wDFBnB, which incorporates standard techniques for using weights in heuristic search and offers suboptimality guarantees. Our main contribution drawn from a preliminary evaluation is the observation that wDFBnB, used along with automated or hand-crafted weight schedules, can significantly outperform DFBnB both in terms of anytime behavior and convergence to the optimal. We think this small study calls for more research on the design of automated weight schedules that could provide superior anytime performance across a wider range of domains.

ICAPS Conference 2013 Conference Paper

Moving Target Search with Compressed Path Databases

• Adi Botea
• Jorge A. Baier
• Daniel Harabor
• Carlos Hernández Ulloa

Moving target search, where the goal state changes during a search, has recently seen a revived interest. Incremental Anytime Repairing A* (I-ARA*) is a very recent, state-ofthe-art algorithm for moving target search in a known terrain. In this work, we address the problem using compressed path databases (CPDs) in moving target search. CPDs have previously been used in standard, fixed-target pathfinding. They encode all-pairs shortest paths in a compressed form and require preprocessing and memory to store the database. In moving-target search, our speed results are orders of magnitude better than state of the art. The time per individual move is improved, which is important in real-time search scenarios, where the time available to make a move is limited. The number of hunter moves is very good, since CPDs provide optimal moves along shortest paths. Compared to previous successful methods, such as I-ARA*, our method is simple to understand and implement.

SoCS Conference 2013 Conference Paper

Reconnecting with the Ideal Tree: An Alternative to Heuristic Learning in Real-Time Search

• Nicolás Rivera
• Leon Illanes
• Jorge A. Baier
• Carlos Hernández Ulloa

In this paper, we present a conceptually simple, easy-to-implement real-time search algorithm suitable for a priori partially known environments. Instead of performing a series of searches towards the goal, like most Real-Time Heuristic Search Algorithms do, our algorithm follows the arcs of a tree T rooted in the goal state that is built initially using the heuristic h. When the agent observes that an arc in the tree cannot be traversed in the actual environment, it removes such an arc from T and our algorithm carries out a reconnection search whose objective is to find a path between the current state and any node in T. The reconnection search need not be guided by $h$, since the search objective is not to encounter the goal. Furthermore, h need not be updated. We implemented versions of our algorithm that utilize various blind search algorithms for reconnection. We show experimentally that these implementations significantly outperform state-of-the-art real-time heuristic search algorithms for the task of pathfinding in grids. In grids, our algorithms, which do not incorporate any geometrical knowledge, naturally behaves similarly to a bug algorithm, moving around obstacles, and never returning to areas that have been visited in the past. In addition, we prove theoretical properties of the algorithm.

SoCS Conference 2012 Conference Paper

Paper Summary: Time-Bounded Adaptive A

• Carlos Hernández Ulloa
• Jorge A. Baier
• Tansel Uras
• Sven Koenig

This paper summarizes our AAMAS 2012 paper on "Time-Bounded Adaptive A*, " which introduces the game time model to evaluate search algorithms in real-time settings, such as video games. It then extends the existing real-time search algorithm TBA* to path planning with the freespace assumption in initially partially or completely unknown terrain, resulting in Time-Bounded Adaptive A* (TBAA*). TBAA* needs fewer time intervals in the game time model than several state-of-the-art complete and real-time search algorithms and about the same number of time intervals as the best compared complete search algorithm, even though it has the advantage over complete search algorithms that the agent starts to move right away.

SoCS Conference 2012 Conference Paper

Position Paper: Incremental Search Algorithms Considered Poorly Understood

• Carlos Hernández Ulloa
• Jorge A. Baier
• Tansel Uras
• Sven Koenig

Incremental search algorithms, such as D* Lite, reuse information from previous searches to speed up the current search and can thus solve sequences of similar search problems faster than Repeated A*, which performs repeated A* searches. In this position paper, we study goal-directed navigation in initially unknown terrain and point out that it is currently not well understood when D* Lite runs faster than Repeated A*. In general, it appears that Repeated A* runs faster than D* Lite for easy navigation problems (where the agent reaches the goal with only a small number of searches), which means that it runs faster than D* Lite quite often in practice. We draw two conclusions, namely that incremental search algorithms need to be evaluated in more diverse testbeds to improve our understanding of their properties and that they can be improved to be more competitive for easy navigation problems.

AAMAS Conference 2011 Conference Paper

Escaping Heuristic Depressions in Real-Time Heuristic Search

• Carlos Hern
• aacute; ndez
• Jorge A. Baier

Heuristic depressions are local minima of heuristic functions. While visiting one them, real-time (RT) search algorithms like LRTA will update the heuristic value for most of their states several times before escaping, resulting in costly solutions. Existing RT search algorithm tackle this problem by doing more search and/or lookahead but do not guide search towards leaving depressions. We present eLSS-LRTA, a new RT search algorithm based on LSSLRTA that actively guides search towards exiting regions with heuristic depressions. We show that our algorithm produces better quality solutions than LSS-LRTA for equal values of lookahead in standard RT benchmarks.

ICAPS Conference 2011 Conference Paper

Fast Subgoaling for Pathfinding via Real-Time Search

• Carlos Hernández Ulloa
• Jorge A. Baier

Real-time heuristic search is a standard approach to pathfind- ing when agents are required to make decisions in a bounded, very short period of time. An assumption usually made in the development and evaluation of real-time algorithms is that the environment is unknown. Nevertheless, in many interesting applications such as pathfinding for automnomous characters in video games, the environment is known in advance. Recent real-time search algorithms such as D LRTA* and kNN LRTA* exploit knowledge about the environment while pathfinding under real-time constraints. Key to those algorithms is the computation of subgoals in a preprocessing step. Subgoals are subsequently used in the online planning phase to obtain high-quality solutions. Preprocessing in those algorithms, however, requires significant computation. In this paper we propose a novel preprocessing algorithm that generates subgoals using a series of backward search episodes carried out from potential goals. The result of a single backward search episode is a tree of subgoals that we then use while planning online. We show the advantages of our approach over state-of-the-art algorithms by carrying out experiments on standard real-time search benchmarks.

SoCS Conference 2011 Conference Paper

Real-Time Adaptive A* with Depression Avoidance

• Carlos Hernández Ulloa
• Jorge A. Baier

Real-time search is a well known approach to solving search problems under tight time constraints. Recently, it has been shown that LSS-LRTA∗ , a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA∗ can be improved in the same manner. We propose aRTAA∗ and daRTAA∗ , two algorithms based on RTAA∗ that avoid heuristic depressions. Both algorithms outperform RTAA∗ on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the planning episode. We prove, in addition, that both algorithms have good theoretical properties.

IJCAI Conference 2011 Conference Paper

Real-Time Heuristic Search with Depression Avoidance

• Carlos Hern
• aacute; ndez
• Jorge A. Baier

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is exceedingly low compared to the actual cost to reach a solution. Real-time search algorithms easily become trapped in those regions since the heuristic values of states in them may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms like LSS-LRTA*, LRTA*(k), etc. , improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding or escaping depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We apply the principle to LSS-LRTA* producing aLSS-LRTA*, a new real-time search algorithm whose search is guided towards exiting regions with heuristic depressions. We show our algorithm outperforms LSS-LRTA* in standard real-time benchmarks. In addition we prove aLSS-LRTA* has most of the good theoretical properties of LSS-LRTA*.

KR Conference 2010 Conference Paper

Diagnosis as Planning Revisited

• Shirin Sohrabi
• Jorge A. Baier
• Sheila A. McIlraith

In discrete dynamical systems change results from actions. As such, given a set of observations, diagnoses often take the form of posited events that result in the observed behaviour. In this paper we revisit formal characterizations of diagnosis, and their relationship to planning. We do so from both a theoretical and a computational perspective. In particular, we extend the characterization of diagnosis to deal with the case of incomplete information, and rich preferences. We also explore the use of state-of-the-art planning technology for the automated generation of diagnoses. Examining several classes of diagnosis problems, we provide both proof of concept and benchmark experiments, the latter showing superior performance to a leading diagnosis engine. Our findings help support the hypothesis that planning technology holds great promise for efficient generation of diagnoses.

IJCAI Conference 2009 Conference Paper

• Shirin Sohrabi
• Jorge A. Baier
• Sheila A. McIlraith

In this paper we address the problem of generating preferred plans by combining the procedural control knowledge specified by Hierarchical Task Networks (HTNs) with rich user preferences. To this end, we extend the popular Planning Domain Definition Language, PDDL3, to support specification of simple and temporally extended preferences over HTN constructs. To compute preferred HTN plans, we propose a branch-and-bound algorithm, together with a set of heuristics that, leveraging HTN structure, measure progress towards satisfaction of preferences. Our preference-based planner, HTNPLAN-P, is implemented as an extension of the SHOP2 planner. We compared our planner with SGPlan5 and HPLAN-P– the top performers in the 2006 International Planning Competition preference tracks. HTNPLAN-P generated plans that in all but a few cases equalled or exceeded the quality of plans returned by HPLAN-P and SGPlan5. While our implementation builds on SHOP2, the language and techniques proposed here are relevant to a broad range of HTN planners.

AIJ Journal 2009 Journal Article

A heuristic search approach to planning with temporally extended preferences

• Jorge A. Baier
• Fahiem Bacchus
• Sheila A. McIlraith

Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In this paper we provide a technique for accomplishing this objective. Our technique can deal with a rich class of preferences, including so-called temporally extended preferences (TEPs). Unlike simple preferences which express desired properties of the final state achieved by a plan, TEPs can express desired properties of the entire sequence of states traversed by a plan, allowing the user to express a much richer set of preferences. Our technique involves converting a planning problem with TEPs into an equivalent planning problem containing only simple preferences. This conversion is accomplished by augmenting the inputed planning domain with a new set of predicates and actions for updating these predicates. We then provide a collection of new heuristics and a specialized search algorithm that can guide the planner towards preferred plans. Under some fairly general conditions our method is able to find a most preferred plan—i. e. , an optimal plan. It can accomplish this without having to resort to admissible heuristics, which often perform poorly in practice. Nor does our technique require an assumption of restricted plan length or make-span. We have implemented our approach in the HPlan-P planning system and used it to compete in the 5th International Planning Competition, where it achieved distinguished performance in the Qualitative Preferences track.

ICAPS Conference 2009 Conference Paper

Exploiting N-Gram Analysis to Predict Operator Sequences

• Christian J. Muise
• Sheila A. McIlraith
• Jorge A. Baier
• Michael Reimer

N-gram analysis provides a means of probabilistically predicting the next item in a sequence. Due originally to Shannon, it has proven an effective technique for word prediction in natural language processing and for gene sequence analysis. In this paper, we investigate the utility of n-gram analysis in predicting operator sequences in plans. Given a set of sample plans, we perform n-gram analysis to predict the likelihood of subsequent operators, relative to a partial plan. We identify several ways in which this information might be integrated into a planner. In this paper, we investigate one of these directions in further detail. Preliminary results demonstrate the promise of n-gram analysis as a tool for improving planning performance.

ICAPS Conference 2009 Conference Paper

Improving Planning Performance Using Low-Conflict Relaxed Plans

• Jorge A. Baier
• Adi Botea

The FF relaxed plan heuristic is one of the most effective techniques in domain-independent satisficing planning and is used by many state-of-the-art heuristic-search planners. However, it may sometimes provide quite inaccurate information, since its relaxation strategy, which ignores the delete effects of actions, may oversimplify a problem's structure. In this paper, we propose a novel algorithm for computing relaxed plans which — although still relaxed — aim at respecting much of the structure of the original problem. We accomplish this by generating relaxed plans with a reduced number of conflicts. An action a will add a conflict when added to a relaxed plan if the resulting plan is provably illegal (i. e, not executable) in the un-relaxed problem. As a second contribution, we propose a new lookahead strategy, in the spirit of Vidal's YAHSP lookahead, that can better exploit the contents of relaxed plans. In our experimental analysis, we show that the resulting heuristic improves over the FF heuristic in a number of domains, most notably when lookahead is enabled. Moreover, the resulting system, which uses our new lookahead, is competitive with state-of-the-art planners, and even better in terms of the number of solved problems.

AAAI Conference 2008 Conference Paper

Beyond Classical Planning: Procedural Control Knowledge and Preferences in State-of-the-Art Planners

• Jorge A. Baier

Real-world planning problems can require search over thousands of actions and may yield a multitude of plans of differing quality. To solve such real-world planning problems, we need to exploit domain control knowledge that will prune the search space to a manageable size. And to ensure that the plans we generate are of high quality, we need to guide search towards generating plans in accordance with user preferences. Unfortunately, most state-of-the-art planners cannot exploit control knowledge, and most of those that can exploit user preferences require those preferences to only talk about the final state. Here, we report on a body of work that extends classical planning to incorporate procedural control knowledge and rich, temporally extended user preferences into the specification of the planning problem. Then to address the ensuing nonclassical planning problem, we propose a broadlyapplicable compilation technique that enables a diversity of state-of-the-art planners to generate such plans without additional machinery. While our work is firmly rooted in AI planning it has broad applicability to a variety of computer science problems relating to dynamical systems.

KR Conference 2008 Conference Paper

ConGolog, Sin Trans: Compiling ConGolog into Basic Action Theories for Planning and Beyond

• Christian Fritz
• Jorge A. Baier
• Sheila A. McIlraith

ConGolog is a logical programming language for agents that is defined in the situation calculus. ConGolog agent control programs were originally proposed as an alternative to planning, but have also more recently been proposed as a means of providing domain control knowledge for planning. In this paper, we present a compiler that takes a ConGolog program and produces a new basic action theory of the situation calculus whose executable situations are all and only those that are permitted by the program. The size of the resulting theory is quadratic in the size of the original program -- even in the face of unbounded loops, recursion, and concurrency. The compilation is of both theoretical and practical interest. From a theoretical perspective, proving properties of programs is simplified because reification of programs is no longer required, and the compiled theory contains fewer second-order axioms. Further, in some cases, properties can be proven by regressing the program to the initial situation, eliminating the need for second-order axioms altogether. From a practical perspective, the compilation provides the mathematical foundation for compiling ConGolog programs into classical planning problems, including, with minor restrictions, into the Plan Domain Definition Language (PDDL), which is used as the input language for most state-of-the-art planners. Moreover, Hierarchical Task Networks (HTNs), a popular planning paradigm for industrial applications can be represented as ConGolog programs and can thus now also be compiled to a classical planning problem. Such compilations are significant because they allow the best state-of-the-art planners to exploit ConGolog and HTN search control, without the need for special-purpose machinery.

IJCAI Conference 2007 Conference Paper

• Jorge A. Baier
• Fahiem Bacchus
• Sheila McIlraith

In this paper we propose a suite of techniques for planning with temporally extended preferences (TEPs). To this end, we propose a method for compiling TEP planning problems into simpler domains containing only final-state (simple) preferences and metric functions. With this simplified problem in hand, we propose a variety of heuristic functions for planning with final-state preferences, together with an incremental best-first planning algorithm. A key feature of the planning algorithm is its ability to prune the search space. We identify conditions under which our planning algorithm will generate optimal plans. We implemented our algorithm as an extension to the \tlplan ~planning system and report on extensive testing performed to evaluate the effectiveness of our heuristics and algorithm. Our planner, \hplanp{}, competed in the 5th International Planning Competition, achieving distinguished performance in the \emph{qualitative preferences} track.

ICAPS Conference 2007 Conference Paper

Exploiting Procedural Domain Control Knowledge in State-of-the-Art Planners

• Jorge A. Baier
• Christian Fritz 0001
• Sheila A. McIlraith

Domain control knowledge (DCK) has proven effective in improving the efficiency of plan generation by reducing the search space for a plan. Procedural DCK is a compelling type of DCK that supports a natural specification of the skeleton of a plan. Unfortunately, most state-of-the-art planners do not have the machinery necessary to exploit procedural DCK. To resolve this deficiency, we propose to compile procedural DCK directly into PDDL2. 1, thus enabling any PDDL2. 1- compatible planner to exploit it. The contribution of this paper is threefold. First, we propose a PDDL-based semantics for an Algol-like, procedural language that can be used to specify DCK in planning. Second, we provide a polynomial algorithm that translates an ADL planning instance and a DCK program, into an equivalent, program-free PDDL2. 1 instance whose plans are only those that adhere to the program. Third, we argue that the resulting planning instance is well-suited to being solved by domain-independent heuristic planners. To this end, we propose three approaches to computing domain-independent heuristics for our translated instances, sometimes leveraging properties of our translation to guide search. In our experiments on familiar PDDL planning benchmarks we show that the proposed compilation of procedural DCK can significantly speed up the performance of a heuristic search planner. Our translators are implemented and available on the web.

AAAI Conference 2006 Conference Paper

Planning with First-Order Temporally Extended Goals using Heuristic Search

• Jorge A. Baier

Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. The problem of planning with TEGs is of renewed interest because it is at the core of planning with temporal preferences. Currently, the fastest domain-independent classical planners employ some kind of heuristic search. However, existing planners for TEGs are not heuristic and are only able to prune the search space by progressing the TEG. In this paper we propose a method for planning with TEGs using heuristic search. We represent TEGs using a rich and compelling subset of a first-order linear temporal logic. We translate a planning problem with TEGs to a classical planning problem. With this translation in hand, we exploit heuristic search to determine a plan. Our translation relies on the construction of a parameterized nondeterministic finite automaton for the TEG. We have proven the correctness of our algorithm and analyzed the complexity of the resulting representation. The translator is fully implemented and available. Our approach consistently outperforms TLPLAN on standard benchmark domains, often by orders of magnitude.

ICAPS Conference 2006 Conference Paper

Planning with Temporally Extended Goals Using Heuristic Search

• Jorge A. Baier
• Sheila A. McIlraith

Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. Current planners for TEGs prune the search space during planning via goal progression. However, the fastest classical domain-independent planners rely on heuristic search. In this paper we propose a method for planning with propositional TEGs using heuristic search. To this end, we translate an instance of a planning problem with TEGs into an equivalent classical planning problem. With this translation in hand, we exploit heuristic search to determine a plan. We represent TEGs using propositional linear temporal logic which is interpreted over finite sequences of states. Our translation is based on the construction of a nondeterministic finite automaton for the TEG. We prove the correctness of our algorithm and analyze the complexity of the resulting representation. The translator is fully implemented and available. Our approach consistently outperforms existing approaches to planning with TEGs, often by orders of magnitute.