This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly as lifted procedures. Our ground procedure is a ground version of Tate's NONLIN procedure. In Tate's procedure one is not required to determine whether a prerequisite of a step in an unfinished plan is guaranteed to hold in all linearizations. This allows Tate's procedure to avoid the use of Chapman's modal truth criterion. Systematicity is the property that the same plan, or partial plan, is never examined more than once. Systematicity is achieved through a simple modification of Tate's procedure.

I present several computational complexity results for propositional STRIPS planning, i.e., STRIPS planning restricted to ground formulas. Different planning problems can be defined by restricting the type of formulas, placing limits on the number of pre- and postconditions, by restricting negation in pre- and postconditions, and by requiring optimal plans. For these types of restrictions, I show when planning is tractable (polynomial) and intractable (NPhard) . In general, it is PSPACE-complete to determine if a given planning instance has any solutions. Extremely severe restrictions on both the operators and the formulas are required to guarantee polynomial time or even NP-completeness. For example, when only ground literals are permitted, determining plan existence is PSPACE-complete even if operators are limited to two preconditions and two postconditions. When definite Horn ground formulas are permitted, determining plan existence is PSPACE-complete even if operators are limited t...

The Robot World Cup Initiative (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multiagent collaboration, strategy acquisition, realtime reasoning, robotics, and sensor-fusion. Unlike AAAI robot competition, which is tuned for a single heavy-duty slow-moving robot, RoboCup is a task for a team of multiple fastmoving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment. 1 Introduction: RoboCup as a Standard AI Problem We propose a Robot World Cup (RoboCup), as a new standard problem for AI an...

This article presents a completely automated approach to generating abstractions for planning. The abstractions are generated using a tractable, domain-independent algorithm whose only input is the definition of a problem to be solved and whose output is an abstraction hierarchy that is tailored to the particular problem. The algorithm generates abstraction hierarchies by dropping literals from the original problem definition. It forms abstractions that satisfy the ordered monotonicity property, which guarantees that the structure of an abstract solution is not changed in the process of refining it. The algorithm for generating abstractions is implemented in a system called alpine, which generates abstractions for a hierarchical version of the prodigy problem solver. The abstractions generated by alpine are tested in multiple domains on large problem sets and are shown to produce shorter solutions with significantly less search than planning without using abstraction. 1 1 ...

We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be Combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations, that is, representations that involve an arbitrarily complex feature extraction stage and a relatively simple approximation architecture. We prove the convergence of these algorithms and provide bounds on the approximation error. We also apply one of these algorithms to pro- duce a computer program that plays Tetris at a respectable skill level. Furthermore, we provide a counterexample illustrating the difficulties of integrating compact representations and dynamic programming: which exemplifies the shortcomings of several methods in current practice, including Q-learning and temporal-difference learning.

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward’s approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving

We have previously reported a number of tractable planning problems defined in the SAS+ formalism. This report complements these results by providing a complete map over the complexity of SAS+ planning under all combinations of the previously considered restrictions. We analyze the complexity both of finding a minimal plan and of finding any plan. In contrast to other complexity surveys of planning we study not only the complexity of the decision problems but also of the generation problems. We prove that the SAS+-PUS problem is the maximal tractable problem under the restrictions we have considered if we want to generate minimal plans. If we are satisfied with any plan, then we can generalize further to the SAS+-US problem, which we prove to be the maximal tractable problem in this case.

In this paper, we examine how the complexity of domain-independent planning with STRIPS-style operators depends on the nature of the planning operators. We show

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.

The use of abstraction in problem solving is an effective approach to reducing search, but finding good abstractions is a difficult problem, even for people. This paper identifies a criterion for selecting useful abstractions, describes a tractable algorithm for generating them, and empirically demonstrates that the abstractions reduce search. The abstraction learner, called alpine,isinte- grated with the prodigy problem solver [Minton et al., 1989b, Carbonell et al.,1991] and has been tested on large problem sets in multiple domains.