Patrik Haslum

Admissible heuristics for optimal planning

The invited speakers at the conference presented some of their latest research and ideas on intelligent planning and execution: Drew McDermott from Yale University gave the first talk, entitled “Bottom-Up Knowledge Representation,” and David Smith from In recent years, AI planning and scheduling has emerged as a technology critical to production management, space systems, the internet, and military applications. The Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS2000) was held on 14–17 April 2000 at Breckenridge, Colorado;1 it was colocated with the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000). This conference brought together researchers working in all aspects of problems in planning, scheduling, planning and learning, and plan execution for dealing with complex problems. The format of the conference included paper presentations, invited speakers, panel discussions, workshops, and a planning competition. The conference was cochaired by Steve Chien of the Jet Propulsion Laboratory (JPL) at the California Institute of Technology, Subbarao Kambhampati of Arizona State University, and Craig Knoblock of the University of Southern California Information Sciences Institute, with the proceedings published by AAAI Press (Chien, Kambhampati, and Knoblock 2000). The three workshops were “Analyzing and Exploiting Domain Knowledge for Efficient Planning,” chaired by Maria Fox from University of Durham; “DecisionTheoretic Planning,” chaired by Richard Goodwin from IBM’s T. J. Watson Research Center and Sven Koenig from Georgia Institute of Technology; and “Model-Theoretic Approaches to Planning” by Paolo Traverso from information describing the content of documents or the behavior of programs. Because the described objects need to be processed by a wide variety of programs, designed by many different parties, finding a representation system to describe any content seems to be a daunting challenge. This challenge is similar to the well-known problems in trying to find a “formal theory of everything.” This talk described a more modest bottom-up approach that involves incrementally building small-specialized knowledge representation frameworks for immediate payoffs and facilitates greater payoffs as these small frameworks are linked together. This approach succeeds even if the process never converges to a general-purpose representation. Making it work involves carefully defining notions of when a framework is strictly more expressive than another and what it means to translate expressions within and between frameworks.

Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners

The international planning competition (IPC) is an important driver for planning research. The general goals of the IPC include pushing the state of the art in planning technology by posing new scientific challenges, encouraging direct comparison of planning systems and techniques, developing and improving a common planning domain definition language, and designing new planning domains and problems for the research community. This paper focuses on the deterministic part of the fifth international planning competition (IPC5), presenting the language and benchmark domains that we developed for the competition, as well as a detailed experimental evaluation of the deterministic planners that entered IPC5, which helps to understand the state of the art in the field. We present an extension of pddl, called pddl3, allowing the user to express strong and soft constraints about the structure of the desired plans, as well as strong and soft problem goals. We discuss the expressive power of the new language focusing on the restricted version that was used in IPC5, for which we give some basic results about its compilability into pddl2. Moreover, we study the relative performance of the IPC5 planners in terms of solved problems, CPU time, and plan quality; we analyse their behaviour with respect to the winners of the previous competition; and we evaluate them in terms of their capability of dealing with soft goals and constraints, and of finding good quality plans in general. Overall, the results indicate significant progress in the field, but they also reveal that some important issues remain open and require further research, such as dealing with strong constraints and computing high quality plans in metric-time domains and domains involving soft goals or constraints.

A Distributed Architecture for Autonomous Unmanned Aerial Vehicle Experimentation

The emerging area of intelligent unmanned aerial vehicle (UAV) research has shown rapid development in recent years and offers a great number of research challenges for distributed autonomous robotics systems. In this article, a prototype distributed architecture for autonomous unmanned aerial vehicle experimentation is presented which supports the development of intelligent capabilities and their integration in a robust, scalable, plug-and-play hardware/software architecture. The architecture itself uses CORBA to support its infrastructure and it is based on a reactive concentric software control philosophy. A research prototype UAV system has been built, is operational and is being tested in actual missions over urban environments.

Some Results on the Complexity of Planning with Incomplete Information

Planning with incomplete information may mean a number of different things; that certain facts of the initial state are not known, that operators can have random or nondeterministic effects, or that the plans created contain sensing operations and are branching. Study of the complexity of incomplete information planning has so far been concentrated on probabilistic domains, where a number of results have been found. We examine the complexity of planning in nondeterministic propositional domains. This differs from domains involving randomness, which has been well studied, in that for a nondeterministic choice, not even a probability distribution over the possible outcomes is known. The main result of this paper is that the non-branching plan existence problem in unobservable domains with an expressive operator formalism is EXPSPACE-complete. We also discuss several restrictions, which bring the complexity of the problem down to PSPACE-complete, and extensions to the fully and partially observable cases.

Merge-and-Shrink Abstraction: A Method for Generating Lower Bounds in Factored State Spaces

Many areas of computer science require answering questions about reachability in compactly described discrete transition systems. Answering such questions effectively requires techniques to be able to do so without building the entire system. In particular, heuristic search uses lower-bounding (“admissible”) heuristic functions to prune parts of the system known to not contain an optimal solution. A prominent technique for deriving such bounds is to consider abstract transition systems that aggregate groups of states into one. The key question is how to design and represent such abstractions. The most successful answer to this question are pattern databases, which aggregate states if and only if they agree on a subset of the state variables. Merge-and-shrink abstraction is a new paradigm that, as we show, allows to compactly represent a more general class of abstractions, strictly dominating pattern databases in theory. We identify the maximal class of transition systems, which we call factored transition systems, to which merge-and-shrink applies naturally, and we show that the well-known notion of bisimilarity can be adapted to this framework in a way that still guarantees perfect heuristic functions, while potentially reducing abstraction size exponentially. Applying these ideas to planning, one of the foundational subareas of artificial intelligence, we show that in some benchmarks this size reduction leads to the computation of perfect heuristic functions in polynomial time and that more approximate merge-and-shrink strategies yield heuristic functions competitive with the state of the art.

Directed Unfolding of Petri Nets

The key to efficient on-the-fly reachability analysis based on unfolding is to focus the expansion of the finite prefix towards the desired marking. However, current unfolding strategies typically equate to blind (breadth-first) search. They do not exploit the knowledge of the marking that is sought, merely entertaining the hope that the road to it will be short. This paper investigates directed unfolding , which exploits problem-specific information in the form of a heuristic function to guide the unfolding towards the desired marking. In the unfolding context, heuristic values are estimates of the distance between configurations. We show that suitable heuristics can be automatically extracted from the original net. We prove that unfolding can rely on heuristic search strategies while preserving the finiteness and completeness of the generated prefix, and in some cases, the optimality of the firing sequence produced. We also establish that the size of the prefix obtained with a useful class of heuristics is never worse than that obtained by blind unfolding. Experimental results demonstrate that directed unfolding scales up to problems that were previously out of reach of the unfolding technique.

LTL Goal Specifications Revisited

The language of linear temporal logic (LTL) has been proposed as a formalism for specifying temporally extended goals and search control constraints in planning. However, the semantics of LTL is defined wrt. infinite state sequences, while a finite plan generates only a finite trace. This necessitates the use of a finite trace semantics for LTL. A common approach is to evaluate LTL formulae on an infinite extension of the finite trace, obtained by infinitely repeating the last state. We study several aspects of this finite LTL se mantics: we show its satisfiability problem is PSpace-complete (same as normal LTL), show that it complies with all equivalence laws that hold under standard (infinite) LTL semantics, and compare it with other finite trace semantics for LTL proposed in planning and in runtime verification. We also examine different mechanisms for determining whether or not a finite trace satisfies or violates an LTL formula, interpreted using the infinite extension semantics.

Improving heuristics through relaxed search: an analysis of TP4 and HSP a * in the 2004 planning competition

The hm admissible heuristics for (sequential and temporal) regression planning are defined by a parameterized relaxation of the optimal cost function in the regression search space, where the parameter m offers a trade-off between the accuracy and computational cost of the heuristic. Existing methods for computing the hm heuristic require time exponential in m, limiting them to small values (m ≤ 2). The hm heuristic can also be viewed as the optimal cost funciton in a relaxation of the search space: this paper presents relaxed search, a method for computing this function partially by searching in the relaxed space. The relaxed search method, because it computes hm only partially, is computationally cheaper and therefore usable for higher values of m. The (complete) h2 heuristic is combined with partial hm heuristics, for m = 3,..., computed by relaxed search, resulting in a more accurate heuristic. This use of the relaxed search method to improve on the h2 heuristic is evaluated by comparing two optimal temporal planners: TP4, which does not use it, and HSP*a, which uses it but is otherwise identical to TP4. The comparison is made on the domains used in the 2004 International Planning Competition, in which both planners participated. Relaxed search is found to be cost effective in some of these domains, but not all. Analysis reveals a characterization of the domains in which relaxed search can be expected to be cost effective, in terms of two measures on the original and relaxed search spaces. In the domains where relaxed search is cost effective, expanding small states is computationally cheaper than expanding large states and small states tend to have small successor states.

Improving delete relaxation heuristics through explicitly represented conjunctions

Heuristic functions based on the delete relaxation compute upper and lower bounds on the optimal delete-relaxation heuristic h+, and are of paramount importance in both optimal and satisficing planning. Here we introduce a principled and flexible technique for improving h+, by augmenting delete-relaxed planning tasks with a limited amount of delete information. This is done by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task, rendering h+ the perfect heuristic h* in the limit. Previous work has introduced a method in which the growth of the task is potentially exponential in the number of conjunctions introduced. We formulate an alternative technique relying on conditional effects, limiting the growth of the task to be linear in this number. We show that this method still renders h+ the perfect heuristic h* in the limit. We propose techniques to find an informative set of conjunctions to be introduced in different settings, and analyze and extend existing methods for lower-bounding and upperbounding h+ in the presence of conditional effects. We evaluate the resulting heuristic functions empirically on a set of IPC benchmarks, and show that they are sometimes much more informative than standard delete-relaxation heuristics.

Recent advances in unfolding technique

We propose a new, and to date the most general, framework for Petri net unfolding, which broadens its applicability, makes it easier to use, and increases its efficiency. In particular: (i) we propose a user-oriented view of the unfolding technique, which simply tells which information will be preserved in the final prefix and how to declare an event a cut-off in the algorithm, while hiding the technical parameters like the adequate order; (ii) the notion of the adequate order is generalised to a well-founded relation, and the requirement that it must refine ? is replaced by a weaker one; and (iii) the order in which the unfolding algorithm selects the possible extensions of the prefix is entirely disentangled from the cut-off condition. We demonstrate the usefulness of the developed theory on some case studies.