Abstract not found

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 ...

Current problem solving systems are constrained in their applicability by inadequate world models. We suggest a world model based on a temporal logic. This approach allows the problem solver to gather constraints on the ordering of actions without having to commit to an ordering when a conflict is detected. As such, it generalizes the work on nonlinear planning by Sacerdoti and Tate. In addition, it allows more general descriptions of actions that may occur simultaneously or overlap, and appears promising in supporting reasoning about external events and actions caused by other agents. 1.

The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. 1 Introduction People make judgments of rationality all the time, usually in criticizing someone else's thoughts or deeds as irrational, or in defending their own as rational. Artificial intelligence researchers construct systems and theories to perform or describe rational thought and action, criticizing and defending these systems and theories in terms similar to but more formal than those of the man or woman on the street. Judgments of human rationality commonly involve several different conceptions of rationality, including a logical conception used to judge thoughts, and an economic one used to judge actions or...

A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency plans, i.e., plans in which different actions are performed in different circumstances. In this paper we discuss some issues that arise in the representation and construction of contingency plans and describe Cassandra, a partial-order contingency planner. Cassandra uses explicit decision-steps that enable the agent executing the plan to decide which plan branch to follow. The decision-steps in a plan result in subgoals to acquire knowledge, which are planned for in the same way as any other subgoals. Cassandra thus distinguishes the process of gathering information from the process of making decisions. The explicit representation of decisions in Cassandra allows a coherent approach to...

Complex modern-day artifacts are designed cooperatively by groups of experts, each with their own areas of expertise. The interaction of such experts inevitably involves conflict. This paper presents an implemented computational model, based on studies of human cooperative design, for supporting the resolution of such conflicts. This model is based centrally on the insights that general conflict resolution expertise exists separately from domain-level design expertise, and that this expertise can be instantiated in the context of particular conflicts into specific advice for resolving those conflicts. Conflict resolution expertise consists of a taxonomy of design conflict classes in addition to associated general advice suitable for resolving conflicts in these classes. The abstract nature of conflict resolution expertise makes it applicable to a wide variety of design domains. This paper describes this conflict resolution model and provides examples of its operation from an implemente...

It has long been recognized that hierarchical problem solving can be used to reduce search. Yet, there has been little analysis of the problemsolving method and few experimental results. This paper provides the first comprehensive analytical and empirical demonstrations of the effectiveness of hierarchical problem solving. First, the paper shows analytically that hierarchical problem solving can reduce the size of the searchspace from exponential to linear in the solution length and identifies a sufficient set of assumptions for such reductions in search. Second, it presents empirical results both in a domain that meets all of these assumptions as well as in domains in which these assumptions do not strictly hold. Third, the paper explores the conditions under which hierarchical problem solving will be effective in practice.

Although most people believe that planners that delay step-ordering decisions as long as possible are more efficient than those that manipulate totally ordered sequences of actions, this intuition has received little formal justification or empirical validation. In this paper we do both, characterizing the types of domains that offer performance differentiation and the features that distinguish the relative overhead of three planning algorithms. As expected, the partial-order (nonlinear) planner often has an advantage when confronted with problems in which the specific order of the plan steps is critical. We argue that the observed performance differences are best understood with an extension of Korf's taxonomy of subgoal collections. Each planner quickly solved problems whose subgoals were independent or trivially serializable, but problems with laboriously serializable or nonserializable subgoals were intractable for all planners. Since different plan representations induce distinct ...

Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate exp...

. One of the strengths of Partial-Order Causal Link (POCL) planning is the ability to postpone decisions. But because postponed decisions play no role in reasoning about the plan until they are eventually acted upon, the penalty for postponing some decisions can be quite high. We call this style of decision postponement passive postponement, and present experimental results that quantify the efficiency penalty it incurs. We also suggest an alternate approach, active postponement, that allows postponed decisions to impose constraints on the generation of a plan. This constraint-based approach to decision postponement has been implemented in the Descartes planning system. In Descartes, every planning decision is represented by a variable, with constraints on each variable representing criteria that must be satisfied by the corresponding decision. These constraints are managed by a general-purpose constraint engine, so that even postponed decisions play a role in reasoning about the plan....