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

Learning to solve planning problems efficiently by means of genetic programming

Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.Publicad

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

FLECS: Planning with a Flexible Commitment Strategy

There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that delayed-commitment is the "best" possible planning strategy. However, we recently found evidence that eager-commitment planners can handle a variety of planning problems more efficiently, in particular those with difficult operator choices. Resigned to the futility of trying to find a universally successful planning strategy, we devised a planner that can be used to study which domains and problems are best for which planning strategies. In this article we introduce this new planning algorithm, FLECS, which uses a FLExible Commitment Strategy with respect to plan-step orderings. It is able to use any strategy from delayed-commitment to eager-commitment. The combination of delayed and eager operator-ordering commitments allows FLECS to take advantage of the benefits of explicitly using a simulated execution state and reasoning about planning constraints. FLECS can vary its commitment strategy across different problems and domains, and also during the course of a single planning problem. FLECS represents a novel contribution to planning in that it explicitly provides the choice of which commitment strategy to use while planning. FLECS provides a framework to investigate the mapping from planning domains and problems to efficient planning strategies.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

Extending the use of plateau-escaping macro-actions in planning

Many fully automated planning systems use a single, domain independent heuristic to guide search and no other problem specific guidance. While these systems exhibit excellent performance, they are often out-performed by systems which are either given extra human-encoded search information, or spend time learning additional search control information offline. The benefit of systems which do not require human intervention is that they are much closer to the ideal of autonomy. This document discusses a system which learns additional control knowledge, in the form of macro-actions, during planning, without the additional time required for an online learning step. The results of various techniques for managing the collection of macro-actions generated are also discussed. Finally, an explanation of the extension of the techniques to other planning systems is presented

Critical thinking and systems thinking: towards a critical literacy for systems thinking in practice

About the book: In reflective problem solving and thoughtful decision making using critical thinking one considers evidence, the context of judgment, the relevant criteria for making the judgment well, the applicable methods or techniques for forming the judgment, and the applicable theoretical constructs for understanding the problem and the question at hand. In this book, the authors present topical research in the study of critical thinking. Topics discussed include developing critical thinking through probability models; the promotion of critical thinking skills through argument mapping; an instructional model for teacher training in critical thinking; advanced academic literacy and critical thinking and critical thinking and higher education

Evaluation based on critical systems heuristics

Introduction: Critical systems heuristics (CSH) draws on the substantive work and philosophy of C. West Churchman, a systems engineer who, along with Russell Ackoff during the 1950s and 1960s, defined operations research in the United States. Churchman later pioneered developments in the 1970s of what is now known as 'soft' and 'critical' systemic thinking and practice in the domain of social or human activity systems. Churchman died in 2004. His legacy lies in signalling the importance of being alert to value-laden boundary judgements when making evaluations. Boundaries are what we socially construct in designing and evaluating any human activity system of interest (e.g., any situation of concern from a kinship group, an organisation, or a larger entity such as a national health system). The primary boundary of any human activity systems is defined by 'purpose'. Churchman's work is characterised by a continual ethical commitment to the overarching purpose of improved human well-being. In order to fulfil such purposeful activity, there is always a need to broaden inquiry from the particular system of focus so as to appreciate what Churchman calls the total relevant system. The effectiveness and efficiency of a system of interest depends on the actual boundary judgements associated with that system of interest. Churchman first identified 9 conditions or categories (including the category 'purpose�) associated with any purposeful system of interest in his book The Design of Inquiring Systems [1, 2]. He later extended these to 12 categories in a book provocatively entitled The Systems Approach and Its Enemies, significantly taking into account 3 extra factors (�enemies�) that lie outside the actual system of interest but which can be affected by, and therein have an effect on, the performance of the system [1, 2]. In the early 1980s a doctorate student of Churchman from Switzerland, Werner Ulrich, translated Churchman's 12 categories into an operational set of 12 questions which he called critical systems heuristics [3]. Ulrich returned to Switzerland and worked with CSH as a public health and social welfare policy analyst and program evaluator [4]. Section 2 introduces the basic toolbox of CSH, along with suggestions on when to use it and the benefits of its use. Section 3 will guide you through a suggested operational use of CSH questions in a process of evaluation. Section 4 provides a summary of an extensive case study in which CSH was used for evaluating the role of public participation in natural resource-use planning. Section 5 provides some advice for the practitioner in developing skills on using CSH for evaluation