Towards learning domain-independent planning heuristics

Automated planning remains one of the most general paradigms in Artificial Intelligence, providing means of solving problems coming from a wide variety of domains. One of the key factors restricting the applicability of planning is its computational complexity resulting from exponentially large search spaces. Heuristic approaches are necessary to solve all but the simplest problems. In this work, we explore the possibility of obtaining domain-independent heuristic functions using machine learning. This is a part of a wider research program whose objective is to improve practical applicability of planning in systems for which the planning domains evolve at run time. The challenge is therefore the learning of (corrections of) domain-independent heuristics that can be reused across different planning domains.Comment: Accepted for the IJCAI-17 Workshop on Architectures for Generality and Autonom

Optimal Planning Modulo Theories

Planning for real-world applications requires algorithms and tools with the ability to handle the complexity such scenarios entail. However, meeting the needs of such applications poses substantial challenges, both representational and algorithmic. On the one hand, expressive languages are needed to build faithful models. On the other hand, efficient solving techniques that can support these languages need to be devised. A response to this challenge is underway, and the past few years witnessed a community effort towards more expressive languages, including decidable fragments of first-order theories. In this work we focus on planning with arithmetic theories and propose Optimal Planning Modulo Theories, a framework that attempts to provide efficient means of dealing with such problems. Leveraging generic Optimization Modulo Theories (OMT) solvers, we first present domain-specific encodings for optimal planning in complex logistic domains. We then present a more general, domain- independent formulation that allows to extend OMT planning to a broader class of well-studied numeric problems in planning. To the best of our knowledge, this is the first time OMT procedures are employed in domain-independent planning

Efficient Open World Reasoning for Planning

We consider the problem of reasoning and planning with incomplete knowledge and deterministic actions. We introduce a knowledge representation scheme called PSIPLAN that can effectively represent incompleteness of an agent's knowledge while allowing for sound, complete and tractable entailment in domains where the set of all objects is either unknown or infinite. We present a procedure for state update resulting from taking an action in PSIPLAN that is correct, complete and has only polynomial complexity. State update is performed without considering the set of all possible worlds corresponding to the knowledge state. As a result, planning with PSIPLAN is done without direct manipulation of possible worlds. PSIPLAN representation underlies the PSIPOP planning algorithm that handles quantified goals with or without exceptions that no other domain independent planner has been shown to achieve. PSIPLAN has been implemented in Common Lisp and used in an application on planning in a collaborative interface.Comment: 39 pages, 13 figures. to appear in Logical Methods in Computer Scienc

Merge-and-Shrink Task Reformulation for Classical Planning

The performance of domain-independent planning systems heavily depends on how the planning task has been modeled. This makes task reformulation an important tool to get rid of unnecessary complexity and increase the robustness of planners with respect to the model chosen by the user. In this paper, we represent tasks as factored transition systems (FTS), and use the merge-and-shrink (M&S) framework for task reformulation for optimal and satisficing planning. We prove that the flexibility of the underlying representation makes the M&S reformulation methods more powerful than the counterparts based on the more popular finite-domain representation. We adapt delete-relaxation and M&S heuristics to work on the FTS representation and evaluate the impact of our reformulation

An AI planning-based tool for scheduling satellite nominal operations

Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. The purpose of this article is to present CONSAT, a real application that plans and schedules the performance of nominal operations in four satellites during the course of a year for a commercial Spanish satellite company, HISPASAT. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search. We also abstract the approach in order to generalize it to other domains that need an integrated approach to planning and scheduling.Publicad

Complexity, Decidability and Undecidability Results for Domain-Independent Planning: A Detailed Analysis

In this paper, we examine how the complexity of domain-independent planning with STRIPS-like operators depends on the nature of the planning operators. We show conditions under which plannning is decidable and undecidable. Our results on this topic solve an open problem posed by Chapman [4], and clear up some difficulties with his undecidability theorems. For those cases where planning is decidable, we show how the time complexity varies depending on a wide variety of conditions: . whether or not function symbols are allowed; . whether or not delete lists are a]]owed; . whether or not negative preconditions are allowed; . whether or not the predicates are restricted to be propositional(i.e., 0-ary); . whether the planning operators are given as part of the input to the planning prob]em, or instead are fixed in advance. (Also cross-referenced as UMIACS-TR-91-154

Graph-Based Factorization of Classical Planning Problems

In domain-independent planning, dependencies of operators and variables often prevent the effective application of planning techniques that rely on loosely coupled problems (like factored planning or partial order reduction). In this paper, we propose a generic approach for factorizing a classical planning problem into an equivalent problem with fewer operator and variable dependencies. Our approach is based on variable factorization, which can be reduced to the well-studied problem of graph factorization. While the state spaces of the original and the factorized problems are isomorphic, the factorization offers the potential to exponentially reduce the complexity of planning techniques like factored planning and partial order reduction

Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)

We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graph-theoretic notion of "cube-like graphs", thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.Comment: Accepted in IJCAI 2020 main track (accept ratio 12.6%). The prequel of this paper, "The Search for STRIPS", can be found here: arXiv:1912.05492 . (update, 2020/08/11) We expanded the related work sectio

Using cognitive work analysis to explore activity allocation within military domains

Cognitive Work Analysis (CWA) is frequently advocated as an approach for the analysis of complex sociotechnical systems. Much of the current CWA literature within the military domain pays particular attention to its initial phases; Work Domain Analysis and Contextual Task Analysis. Comparably, the analysis of the social and organisational constraints receives much less attention. Through the study of a helicopter Mission Planning System (MPS) software tool, this paper describes an approach for investigating the constraints affecting the distribution of work. The paper uses this model to evaluate the potential benefits of the social and organisational analysis phase within a military context. The analysis shows that, through its focus on constraints the approach provides a unique description of the factors influencing the social organisation within a complex domain. This approach appears to be compatible with existing approaches and serves as a validation of more established social analysis techniques

On the Complexity of Case-Based Planning

We analyze the computational complexity of problems related to case-based planning: planning when a plan for a similar instance is known, and planning from a library of plans. We prove that planning from a single case has the same complexity than generative planning (i.e., planning "from scratch"); using an extended definition of cases, complexity is reduced if the domain stored in the case is similar to the one to search plans for. Planning from a library of cases is shown to have the same complexity. In both cases, the complexity of planning remains, in the worst case, PSPACE-complete