Miquel Ramírez

SoCS Conference 2022 Conference Paper

Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning (Extended Abstract)

• Stefan O'Toole
• Miquel Ramírez
• Nir Lipovetzky
• Adrian R. Pearce

We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems. RSL uses regression to select relevant sets of states at a range of different distances from the goal. RSL then formulates a Supervised Learning problem to obtain the parameters that define the NN heuristic, using the selected states labeled with exact or estimated distances to goal states. Our experimental study shows that RSL outperforms, in terms of coverage, previous classical planning NN heuristics functions while requiring a fraction of the training time.

ICAPS Conference 2021 Conference Paper

Approximate Novelty Search

• Anubhav Singh
• Nir Lipovetzky
• Miquel Ramírez
• Javier Segovia-Aguas

Width-based search algorithms seek plans by prioritizing states according to a suitably defined measure of novelty, that maps states into a set of novelty categories. Space and time complexity to evaluate state novelty is known to be exponential on the cardinality of the set. We present novel methods to obtain polynomial approximations of novelty and width-based search. First, we approximate novelty computation via random sampling and Bloom filters, reducing the runtime and memory footprint. Second, we approximate the best-first search using an adaptive policy that decides whether to forgo the expansion of nodes in the open list. These two techniques are integrated into existing width-based algorithms, resulting in new planners that perform significantly better than other state-of-the-art planners over benchmarks from the International Planning Competitions.

ICAPS Conference 2019 Conference Paper

Model Recognition as Planning

• Diego Aineto
• Sergio Jiménez Celorrio
• Eva Onaindia
• Miquel Ramírez

Given a partially observed plan execution, and a set of possible planning models (models that share the same state variables but different action schemata), model recognition is the task of identifying the model that explains the observation. The paper formalizes this task and introduces a novel method that estimates the probability of a STRIPS model to produce an observation of a plan execution. This method builds on top of off-the-shelf classical planning algorithms and it is robust to missing actions and intermediate states in the observation. The effectiveness of the method is tested in three experiments, each encoding a set of different STRIPS models and all using empty-action observations: (1) a classical string classification task; (2) identification of the model that encodes a failure present in an observation; and (3) recognition of a robot navigation policy.

IJCAI Conference 2019 Conference Paper

Online Probabilistic Goal Recognition over Nominal Models

• Ramon Fraga Pereira
• Mor Vered
• Felipe Meneguzzi
• Miquel Ramírez

This paper revisits probabilistic, model-based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model-based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via learning for Hybrid Planning we adapt and evaluate existing goal recognition approaches to analyze how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.

IJCAI Conference 2017 Conference Paper

Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators

• Guillem Francès
• Miquel Ramírez
• Nir Lipovetzky
• Hector Geffner

Classical planning is concerned with problems where a goal needs to be reached from a known initial state by doing actions with deterministic, known effects. Classical planners, however, deal only with classical problems that can be expressed in declarative planning languages such as STRIPS or PDDL. This prevents their use on problems that are not easy to model declaratively or whose dynamics are given via simulations. Simulators do not provide a declarative representation of actions, but simply return successor states. The question we address in this paper is: can a planner that has access to the structure of states and goals only, approach the performance of planners that also have access to the structure of actions expressed in PDDL? To answer this, we develop domain-independent, black box planning algorithms that completely ignore action structure, and show that they match the performance of state-of-the-art classical planners on the standard planning benchmarks. Effective black box algorithms open up new possibilities for modeling and for expressing control knowledge, which we also illustrate.

IJCAI Conference 2017 Conference Paper

Real--Time UAV Maneuvering via Automated Planning in Simulations

• Miquel Ramírez
• Michael Papasimeon
• Lyndon Behnke
• Nir Lipovetzky
• Tim Miller
• Adrian R. Pearce

The automatic generation of realistic behavior such as tactical intercepts for Unmanned Aerial Vehicles (UAV) in air combat is a challenging problem. State-of-the-art solutions propose hand-crafted algorithms and heuristics whose performance depends heavily on the initial conditions and specific aerodynamic characteristics of the UAVs involved. This demo shows the ability of domain-independent planners, embedded into simulators, to generate on-line, feed-forward, control signals that steer simulated aircraft as best suits the situation.

ECAI Conference 2016 Conference Paper

Interval-Based Relaxation for General Numeric Planning

• Enrico Scala
• Patrik Haslum
• Sylvie Thiébaux
• Miquel Ramírez

We generalise the interval-based relaxation to sequential numeric planning problems with non-linear conditions and effects, and cyclic dependencies. This effectively removes all the limitations on the problem placed in previous work on numeric planning heuristics, and even allows us to extend the planning language with a wider set of mathematical functions. Heuristics obtained from the generalised relaxation are pruning-safe. We derive one such heuristic and use it to solve discrete-time control-like planning problems with autonomous processes. Few planners can solve such problems, and search with our new heuristic compares favourably with them.

ICAPS Conference 2016 Conference Paper

Numeric Planning with Disjunctive Global Constraints via SMT

• Enrico Scala
• Miquel Ramírez
• Patrik Haslum
• Sylvie Thiébaux

This paper describes a novel encoding for sequential numeric planning into the problem of determining the satisfiability of a logical theory T. We introduce a novel technique, orthogonal to existing work aiming at producing more succinct encodings that enables the theory solver to roll up an unbounded yet finite number of instances of an action into a single plan step, greatly reducing the horizon at which T models valid plans. The technique is then extended to deal with problems featuring disjunctive global constraints, in which the state space becomes a non-convex n dimensional polytope. In order to empirically evaluate the encoding, we build a planner, SPRINGROLL, around a state–of–the–art off– the–shelf SMT solver. Experiments on a diverse set of domains are finally reported, and results show the generality and efficiency of the approach.

ICAPS Conference 2014 Conference Paper

Directed Fixed-Point Regression-Based Planning for Non-Deterministic Domains

• Miquel Ramírez
• Sebastian Sardiña

We present a novel approach to fully-observable nondeterministic planning (FOND) that attempts to bridge the gap between symbolic fix-point computation and recent approaches based on forward heuristic search. Concretely, we formalize the relationship between symbolic and dynamic programming nondeterministic planners, and then exploit such connection to propose a novel familyof planning algorithms that reasons over symbolic policies in a directed manner. By doing so, our proposal reasons over sets of states and executions in a succinct way (as done by symbolic planners) while biasing the reasoning with respect to the initial and goal states of the specific planning problem at hand (as done by heuristic planners). We show empirical results that prove this approach promising in settings where there is an intrinsic tension between plan efficiency and plan "robustness, " a feature to be expected in nondeterministic domains.

ICAPS Conference 2013 Conference Paper

Behavior Composition as Fully Observable Non-Deterministic Planning

• Miquel Ramírez
• Nitin Yadav
• Sebastian Sardiña

The behavior composition problem involves the automatic synthesis of a controller able to “realize” (i. e. , implement) a target behavior module by suitably coordinating a collection of partially controllable available behaviors. In this paper, we show that the existence of a composition solution amounts to finding a strong cyclic plan for a special non-deterministic planning problem, thus establishing the formal link between the two synthesis tasks. Importantly, our results support the use of non-deterministic planing systemsfor solving composition problems in an off-the-shelf manner. We then empirically evaluate three state-of-the-art synthesis systems (a domain-independent automated planner and two game solvers based on model checking techniques) on various non-trivial composition instances. Our experiments show that while behavior composition is EXPTIME-complete, the current technology is already able to handle instances of significant complexity. Our work is, as far as we know, the first serious experimental work on behavior composition.

ICAPS Conference 2011 Conference Paper

Effective Heuristics and Belief Tracking for Planning with Incomplete Information

• Alexandre Albore
• Miquel Ramírez
• Hector Geffner

Conformant planning can be formulated as a path-finding problem in belief space where the two main challenges are the heuristics to guide the search, and the representation and update of beliefs. In the translation-based approach recently introduced by Palacios and Geffner, the two aspects are handled together by translating conformant problems into classical ones that are solved with classical planners. While competitive with state-of-the-art methods, the translation-based approach runs however into three difficulties. First, complete translations are expensive for problems with high width; second, incomplete translations can generate infinite heuristic values for problems that are solvable; and third, aspects that are specific to the conformant setting, such as the cardinality of beliefs, are not accounted for. In this work, we build on the translation-based approach but not for solving conformant problems with a classical planner but for deriving heuristics and computing beliefs in the context of a standard belief-space planner. For this, a novel translation KSi is introduced that is always complete, but which is sound for problems with width bounded by i. A new conformant planner, called T1, builds then on this translation for i=1, extending the heuristic that results with a second heuristic obtained from invariant "oneof expressions". A number of experiments is performed to compare T1 with state-of-the-art conformant planners.

IJCAI Conference 2009 Conference Paper

• Miquel Ramírez
• Héctor Geffner

In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G∗ of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goal G belongs to G∗ if there is an action sequence π that is an optimal plan for both the goal G and the goal G extended with extra goals representing the observations. Exploiting this result, we show how the set G∗ can be computed exactly and approximately by minor modifications of existing optimal and suboptimal planning algorithms, and existing polynomial heuristics. Experiments over several domains show that the suboptimal planning algorithms and the polynomial heuristics provide good approximations of the optimal goal set G∗ while scaling up as well as state-of-the-art planning algorithms and heuristics.