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Angelo Oddi

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23 papers
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23

ECAI Conference 2024 Conference Paper

An Empirical Study of Grounding PPDDL Plans for AI-Driven Robots in Social Environment

  • Gloria Beraldo
  • Angelo Oddi
  • Riccardo Rasconi

Autonomous robots are agents that interact with the environment and perform tasks using their own abilities (i. e. , skills) without continuous human intervention. However, in real-life scenarios, intelligent robots also need to discover the effects of their actions and understand how to save them for future use. This task appears time-consuming and very challenging, especially in a social environment populated by people who typically modify their behaviors based on the context and can dynamically impact the robot’s decision-making process. This paper aims to investigate the feasibility of autonomously creating an abstract representation of the domain knowledge from the data acquired during the robot’s exploration, inferring causal-effect relations between the executed actions, and learning context-aware symbols that describe the environment states at high level, ultimately producing a PDDL-based description of the domain. With this purpose, a new framework that relies on ROS, the standard de-facto in robotics, and ROSPlan has been developed to facilitate the transfer into several robotic platforms. Preliminary results suggest the possibility of describing the robot’s experience per option via context-based symbols that are consistently learned by the system from a few data samples.

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ECAI Conference 2020 Conference Paper

Integrating Open-Ended Learning in the Sense-Plan-Act Robot Control Paradigm

  • Angelo Oddi
  • Riccardo Rasconi
  • Vieri Giuliano Santucci
  • Gabriele Sartor
  • Emilio Cartoni
  • Francesco Mannella
  • Gianluca Baldassarre

This paper presents the achievements obtained from a study performed within the IMPACT (Intrinsically Motivated Planning Architecture for Curiosity-driven roboTs) Project funded by the European Space Agency (ESA). The main contribution of the work is the realization of an innovative robotic architecture in which the well-known three-layered architectural paradigm (decisional executive and functional) for controlling robotic systems is enhanced with autonomous learning capabilities. The architecture is the outcome of the application of an interdisciplinary approach integrating Artificial Intelligence (AI) Autonomous Robotics and Machine Learning (ML) techniques. In particular state-of-the-art AI planning systems and algorithms were integrated with Reinforcement Learning (RL) algorithms guided by intrinsic motivations (curiosity exploration novelty and surprise). The aim of this integration was to: (i) develop a software system that allows a robotic platform to autonomously represent in symbolic form the skills autonomously learned through intrinsic motivations; (ii) show that the symbolic representation can be profitably used for automated planning purposes thus improving the robot’s exploration and knowledge acquisition capabilities. The proposed solution is validated in a test scenario inspired by a typical space exploration mission involving a rover.

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AAAI Conference 2019 Conference Paper

An Innovative Genetic Algorithm for the Quantum Circuit Compilation Problem

  • Riccardo Rasconi
  • Angelo Oddi

Quantum Computing represents the next big step towards speed boost in computation, which promises major breakthroughs in several disciplines including Artificial Intelligence. This paper investigates the performance of a genetic algorithm to optimize the realization (compilation) of nearest-neighbor compliant quantum circuits. Currrent technological limitations (e. g. , decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized, and therefore the makespanminimization problem of compiling quantum algorithms on present or future quantum machines is dragging increasing attention in the AI community. In our genetic algorithm, a solution is built utilizing a novel chromosome encoding where each gene controls the iterative selection of a quantum gate to be inserted in the solution, over a lexicographic double-key ranking returned by a heuristic function recently published in the literature. Our algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that our genetic approach obtains very encouraging results that outperform the solutions obtained in previous research against the same benchmark, succeeding in significantly improving the makespan values for a great number of instances.

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ICAPS Conference 2017 Conference Paper

Multi-Objective Optimization in a Job Shop with Energy Costs through Hybrid Evolutionary Techniques

  • Miguel Ángel González 0001
  • Angelo Oddi
  • Riccardo Rasconi

Energy costs are an increasingly important issue in real-world scheduling, for both economic and environmental reasons. This paper deals with a variant of the well-known job shop scheduling problem, where we consider a bi-objective optimization of both the weighted tardiness and the energy costs. To this end, we design a hybrid metaheuristic that combines a genetic algorithm with a novel local search method and a linear programming approach. We also propose an efficient procedure for improving the energy cost of a given schedule. In the experimental study we analyse our proposal and compare it with the state of the art and also with a constraint programming approach, obtaining competitive results.

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ICAPS Conference 2012 Conference Paper

Iterative Improvement Algorithms for the Blocking Job Shop

  • Angelo Oddi
  • Riccardo Rasconi
  • Amedeo Cesta
  • Stephen F. Smith

This paper provides an analysis of the efficacy of a known iterative improvement meta-heuristic approach from the AI area in solving the Blocking Job Shop Scheduling Problem (BJSSP) class of problems. The BJSSP is known to have significant fallouts on practical domains, and differs from the classical Job Shop Scheduling Problem (JSSP) in that it assumes that there are no intermediate buffers for storing a job as it moves from one machine to another; according to the BJSSP definition, each job has to wait on a machine until it can be processed on the next machine. In our analysis, two specific variants of the iterative improvement meta-heuristic are evaluated: (1) an adaptation of an existing scheduling algorithm based on the Iterative Flattening Search and (2) an off-the-shelf optimization tool, the IBM ILOG CP Optimizer, which implements Self-Adapting Large Neighborhood Search. Both are applied to a reference benchmark problem set and comparative performance results are presented. The results confirm the effectiveness of the iterative improvement approach in solving the BJSSP; both variants perform well individually and together succeed in improving the entire set of benchmark instances.

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IJCAI Conference 2011 Conference Paper

Iterative Flattening Search for the Flexible Job Shop Scheduling Problem

  • Angelo Oddi
  • Riccardo Rasconi
  • Amedeo Cesta
  • Stephen F. Smith

This paper presents a meta-heuristic algorithm for solving the Flexible Job Shop Scheduling Problem (FJSSP). This strategy, known as Iterative Flattening Search (IFS), iteratively applies a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and a solving-step, in which a new solution is incrementally recomputed from this partial schedule. This work contributes two separate results: (1) it proposes a constraint-based procedure extending an existing approach previously used for classical Job Shop Scheduling Problem; (2) it proposes an original relaxation strategy on feasible FJSSP solutions based on the idea of randomly breaking the execution orders of the activities on the machines and opening the resource options for some activities selected at random. The efficacy of the overall heuristic optimization algorithm is demonstrated on a set of well-known benchmarks.

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ECAI Conference 2010 Conference Paper

Project Scheduling as a Disjunctive Temporal Problem

  • Angelo Oddi
  • Riccardo Rasconi
  • Amedeo Cesta

The main result of the paper is the reduction of the RCPSP/max problem to a Disjunctive Temporal Problem that allows customization of specific properties within a backtracking search procedure for makespan optimization. In addition, a branching strategy is proposed able to deduce new constraints which explicitly represent infeasible or useless search paths. A new variable ordering heuristic (called clustering) is also used which provides a further boosting to the algorithm's effectiveness.

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ICAPS Conference 2009 Conference Paper

Solving Resource-Constrained Project Scheduling Problems with Time-Windows Using Iterative Improvement Algorithms

  • Angelo Oddi
  • Riccardo Rasconi

This paper proposes an iterative improvement approach for solving the Resource Constraint Project Scheduling Problem with Time-Windows (RCPSP/max), a well-known and challenging NP-hard scheduling problem. The algorithm is based on Iterative Flattening Search (IFS), an effective heuristic strategy for solving multi-capacity optimization scheduling problems. Given an initial solution, IFS iteratively performs two-steps: a relaxation-step, that randomly removes a subset of solution constraints and a solving-step, that incrementally recomputes a new solution. At the end, the best solution found is returned. The main contribution of this paper is the extension to RCPSP/max of the IFS optimization procedures developed for solving scheduling problems without time-windows. An experimental evaluation performed on medium-large size and web-available benchmark sets confirms the effectiveness of the proposed procedures. In particular, we have improved the average quality w. r. t. the current bests, while discovering three new optimal solutions, thus demonstrating the general efficacy of IFS.

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ECAI Conference 2008 Conference Paper

Continuous Plan Management Support for Space Missions: the RAXEM Case

  • Amedeo Cesta
  • Gabriella Cortellessa
  • Michel Denis
  • Alessandro Donati
  • Simone Fratini
  • Angelo Oddi
  • Nicola Policella
  • Erhard Rabenau

This paper describes RAXEM, an AI-based system developed to support human mission planners in the daily task to plan uplink commands for an interplanetary spacecraft. The intelligent environment of RAXEM has been designed to support the users in analyzing the problem and taking planning decisions as a result of an interactive process. The system combines different ingredients like integrating flexible automated algorithms, promoting user active participation during problem solving, and guaranteeing continuity of work practice. The paper touches upon all these aspects and comments on how a key factor for success has been the integration of intelligent technology to continuously support mission plan management.

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ICAPS Conference 2007 Conference Paper

An Innovative Product for Space Mission Planning: An A Posteriori Evaluation

  • Amedeo Cesta
  • Gabriella Cortellessa
  • Simone Fratini
  • Angelo Oddi
  • Nicola Policella

This paper describes MEXAR2, a software tool that is currently used to synthesize the operational commands for data downlink from the on-board memory of an interplanetary space mission spacecraft to the ground stations. The tool has been in daily use by the Mission Planning Team of MARS EXPRESS at the European Space Agency since early 2005. Goal of this paper is to present a quick overview of how the planning and scheduling problem has been addressed, a complete application customized and put into context in the application environment. Then it concentrates on describing more in detail how a core solver has been enriched to create a tool that easily allows users to generate diversified plans for the same problem by handling a set of control parameters, called heuristic modifiers, that insert heuristic bias on the generated solutions. A set of experiments is presented that describes how such modifiers affect the solving process.

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IS Journal 2007 Journal Article

Mexar2: AI Solves Mission Planner Problems

  • Amedeo Cesta
  • Gabriella Cortellessa
  • Michel Denis
  • Alessandro Donati
  • Simone Fratini
  • Angelo Oddi
  • Nicola Policella
  • Erhard Rabenau

Deep-space missions carry an ever larger set of different and complementary onboard payloads. Each payload generates data, and synthesizing it for optimized downlinking is one way to reduce the ratio of mission costs to science return. This is the main role of the Mars-Express scheduling architecture (Mexar2), an Al-based tool in daily use on the Mars-Express mission since February 2005. Mexar2 supports space mission planners continuously as they plan data downlinks from the spacecraft to Earth. The tool lets planners work at a higher abstraction level while it performs low-level, often-repetitive tasks. It also helps them produce a plan rapidly, explore alternative solutions, and choose the most robust plan for execution. Additionally, planners can analyze any problems over multiple days and identify payload overcommitments that cause resource bottlenecks and increase the risk of data losses. Mexar2 has significantly increased the data return over the whole Mars-Express mission duration. It's effectively become a work companion for mission planners at the European Space Agency's European Space Operations Center (ESOC) in Darmstadt, Germany.

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ECAI Conference 2006 Conference Paper

Software Companion: The Mexar2 Support to Space Mission Planners

  • Amedeo Cesta
  • Gabriella Cortellessa
  • Simone Fratini
  • Angelo Oddi
  • Nicola Policella

This paper describes a fielded AI system in daily use at the European Space Agency (ESA-ESOC) since February 2005. The tool, named MEXAR2, provides continuous support to human mission planners in synthesizing plans for downlinking on-board memory data from the MARS EXPRESS spacecraft to Earth. The introduction of the tool in the mission planning workflow significantly decreased the time spent in producing plans. Moreover MEXAR2 improves the quality of the produced plans thus guaranteeing a strong reliability in data return enabling a more intensive science activity on board. The introduction of MEXAR2 has modified the role of the human mission planners who can now evaluate and compare different solutions rather than dedicating their time exclusively to computing single solutions (a tedious and repetitive task which does not capitalize on the mission planners' decision-making expertise). These characteristics have effectively made MEXAR2 a fundamental work companion for the human mission planners.

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ICAPS Conference 2004 Conference Paper

Generating Robust Schedules through Temporal Flexibility

  • Nicola Policella
  • Stephen F. Smith
  • Amedeo Cesta
  • Angelo Oddi

This paper considers the problem of generating partial order schedules (POS), that is, schedules which retain temporal flexibility and thus provide some degree of robustness the face of unpredictable execution circumstances. We begin by proposing a set of measures for assessing and comparing the robustness properties of alternative POSs. Then, using common solving framework, we develop two orthogonal procedures for constructing a POS. The first, which we call the resource envelope based approach, uses computed bounds on cumulative resource usage (i. e., a resource envelope) to identify potential resource conflicts, and progressively winnows the total set of temporally feasible solutions into a smaller set of resource feasible solutions by resolving detected conflicts. The second, referred to as the earliest start time approach, instead uses conflict analysis of a specific (i. e., earliest start time) solution to generate an initial fixed-time schedule, and then expands this solution to a set of resource feasible solutions in a post-processing step. We evaluate the relative effectiveness of these two procedures on a set of project scheduling benchmark problems. As might be expected, the second approach, by virtue of its more focused analysis, is found be a more efficient POS generator. Somewhat counterintuitively, however, it is also found to produce POSs that are more robust.

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ICAPS Conference 1998 Conference Paper

Profile-Based Algorithms to Solve Multiple Capacitated Metric Scheduling Problems

  • Amedeo Cesta
  • Angelo Oddi
  • Stephen F. Smith

Though CSP scheduling models have tended to assumefairly general representations of temporal constraints, most work has restricted attention to problems that require allocation of simple, unit-capacity r~=, ources. This paper considers an extendedclass of scheduling problems where resources have capacity to simultaneously support more than one activity, and resource availability at any point in time is consequently a function of whether sufficient unallocated capacity remains. Wepresent a progression of algorithms for solving such multiple-capacitated scheduling problems, and evaluate the performance of each with respect to problemsolving ability and quality of solutions generated. A previously reported algorithm, namedtheConflict FreeSolution Algorithm (CFSA), is first evaluatedagainst a set of problemsof increasing dimensionand is shownto be of limited effectiveness. Twovariations of this algorithm are then introduced which incorporate measuresof temporal flexibility as an alternative heuristic basis for directing the search, and the variant makingbroadest use of these search heuristics is showntoyieldsignificant performance improvement. Observations aboutthetendency of theCFSAsolution approach to produce unnecessarilyoverconstrained solutions thenleadtodevelopment of a second heuristic algorithm, namedEarliest Start Time Algorithm (ESTA). ESTAis shown to be the most effective of the set, both in terms of its ability to efficiently solve problemsof increasing scale and its ability to produceschedules that minimizeoverall completiontime while retaining solution robustness.

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TIME Conference 1996 Conference Paper

Gaining Efficiency and Flexibility in the Simple Temporal Problem

  • Amedeo Cesta
  • Angelo Oddi

Deals with the problem of managing quantitative temporal networks without disjunctive constraints. This problem is known as the "simple temporal problem". Dynamic management algorithms are considered to be coupled with incremental constraint posting approaches for planning and scheduling. A basic algorithm for incremental propagation of a new time constraint is presented which is a modification of the Bellman-Ford algorithm for the single-source shortest-path problem. For this algorithm, a sufficient condition for inconsistency is given, based on cycle detection in the shortest-paths graph. Moreover, the problem of constraint retraction from a consistent situation is considered, and properties for repropagating the network locally are exploited. Some experiments are also presented that show the usefulness of these properties.

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ICAPS Conference 1994 Conference Paper

Managing Dynamic Temporal Constraint Networks

  • Roberto Cervoni
  • Amedeo Cesta
  • Angelo Oddi

This paper concerns the specialization of arcconsistency algorithms for constraint satisfaction in the managementof quantitative temporal constraint networks. Attention is devoted to the design of algorithms that support an incremental style of building solutions allowing both constraint posting and constraint retraction. In particular, the AC-3 algorithm for constraint propagation, customized to temporal networkswithout disjunctions, is presented, and the concept of dependencybetween constraints described. The dependencyinformation is useful to dynamically maintain a trace of the more relevant constraints in a network. The concept of dependency is used to integrate the basic AC-3algorithm with a sufficient condition for inconsistency detection that speeds up its performance, and to design an effective incrementaloperator for constraint retraction.

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