Over the years increasingly sophisticated planning algorithms have been developed. These have made for more efficient planners, but unfortunately these planners still suffer from combinatorial complexity even in simple domains. Theoretical results demonstrate that planning is in the worst case intractable. Nevertheless, planning in particular domains can often be made tractable by utilizing additional domain structure. In fact, it has long been acknowledged that domain independent planners need domain dependent information to help them plan effectively. In this

. We present a framework for encoding planning problems in logic programs with negation as failure, having computational efficiency as our major consideration. In order to accomplish our goal, we bring together ideas from logic programming and the planning systems graphplan and satplan. We discuss different representations of planning problems in logic programs, point out issues related to their performance, and show ways to exploit the structure of the domains in these representations. For our experimentation we use an existing implementation of the stable models semantics called smodels. It turns out that for careful and compact encodings, the performance of the method across a number of different domains, is comparable to that of planners like graphplan and satplan. 1 Introduction Nonmonotonic reasoning was originally motivated by the need to capture in a formal logical system aspects of human commonsense reasoning that enable us to withdraw previous conclusions when new informat...

The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as Graphplan and compilers that convert planning problems into propositional CNF formulae for solution via systematic or stochastic SAT methods. Related work on the Deep Space One spacecraft control algorithms advances our understanding of interleaved planning and execution. In this survey,we explain the latest techniques and suggest areas for future research.

We describe some new preprocessing techniques that enable faster domain-independent planning. The first set of techniques is aimed at inferring state constraints from the structure of planning operators and the initial state. Our methods consist of generating hypothetical state constraints by inspection of operator effects and preconditions, and checking each hypothesis against all operators and the initial conditions. Another technique extracts (supersets of) predicate domains from sets of ground literals obtained by Graphplan-like forward propagation from the initial state. Our various techniques are implemented in a package called DISCOPLAN. We show preliminary results on the effectiveness of adding computed state constraints and predicate domains to the specification of problems for SAT-based planners such as SATPLAN or MEDIC. The results suggest that large speedups in planning can be obtained by such automated methods, potentially obviating the need for adding h...

It is traditional wisdom that one should start from the goals when generating a plan in order to focus the plan generation process on potentially relevant actions. The graphplan system, however, which is the most efficient planning system nowadays, builds a "planning graph" in a forward-chaining manner. Although this strategy seems to work well, it may possibly lead to problems if the planning task description contains irrelevant information. Although some irrelevant information can be filtered out by graphplan, most cases of irrelevance are not noticed. In this paper, we analyze the effects arising from "irrelevant" information to planning task descriptions for different types of planners. Based on that, we propose a family of heuristics that select relevant information by minimizing the number of initial facts that are used when approximating a plan by backchaining from the goals ignoring any conflicts. These heuristics, although not solution-preserving, turn out to be v...

Classical planning is the problem of finding a sequence of actions to achieve a goal given an exact characterization of a domain. An algorithm to solve this problem is presented, which searches a space of plan prefixes, trying to extend one of them to a complete sequence of actions. It is guided by a heuristic estimator based on regression-match graphs, which attempt to characterize the entire subgoal structure of the remaining part of the problem. These graphs simplify the structure by neglecting goal interactions and by assuming that variables in goal conjunctions should be bound in such a way as to make as many conjuncts as possible true without further work. In some domains, these approximations work very well, and experiments show that many classical-planning problems can solved with very little search. 1 Definition of the Problem The classical planning problem is to generate a sequence of actions that make a given proposition true, in a domain in which there is perfect informati...

This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners--viz., distance based heuristics, reachability analysis and disjunctive constraint handling--can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP # . Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several "parallel" domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility. 1

Several recent studies have compared the relative efficiency of alternative flaw selection strategies for partial-order causal link (POCL) planning. We review this literature, and present new experimental results that generalize the earlier work and explain some of the discrepancies in it. In particular, we describe the Least-Cost Flaw Repair (LCFR) strategy developed and analyzed by Joslin and Pollack (1994), and compare it with other strategies, including Gerevini and Schubert's (1996) ZLIFO strategy. LCFR and ZLIFO make very different, and apparently conflicting claims about the most effective way to reduce searchspace size in POCL planning. We resolve this conflict, arguing that much of the benefit that Gerevini and Schubert ascribe to the LIFO component of their ZLIFO strategy is better attributed to other causes. We show that for many problems, a strategy that combines least-cost flaw selection with the delay of separable threats will be effective in reducing search-space size, a...

VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP. It draws from the experience gained in the early to mid 1990’s on flaw selection strategies for POCL planning, and combines this with more recent developments in the field of domain independent planning such as distance based heuristics and reachability analysis. We present an adaptation of the additive heuristic for plan space planning, and modify it to account for possible reuse of existing actions in a plan. We also propose a large set of novel flaw selection strategies, and show how these can help us solve more problems than previously possible by POCL planners. VHPOP also supports planning with durative actions by incorporating standard techniques for temporal constraint reasoning. We demonstrate that the same heuristic techniques used to boost the performance of classical POCL planning can be effective in domains with durative actions as well. The result is a versatile heuristic POCL planner competitive with established CSP-based and heuristic state space planners.

The paper introduces an approach to derive a total ordering between increasing sets of subgoals by defining a relation over atomic goals. The ordering is represented in a so-called goal agenda that is used by the planner to incrementally plan for the increasing sets of subgoals. This can lead to an exponential complexity reduction because the solution to a complex planning problem is found by solving easier subproblems. Since only a polynomial overhead is caused by the goal agenda computation, a potential exists to dramatically speed up planning algorithms as we demonstrate in the empirical evaluation. Introduction How to effectively plan for interdependent subgoals has been in the focus of AI planning research for a very long time (Chapman 1987). But until today planners have made only some progress to solve larger sets of subgoals and scalability of classical planning systems is still a problem. Previous approaches fell into two categories: On one hand, one can focus on...