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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 ...

This paper postulates a rigorous method for the construction of classical planning domain models. We describe, with the help of a non-trivial example, a tool supported method for encoding such models. The method results in an `object-centred' specification of the domain that lifts the representation from the level of the literal to the level of the object. Thus, for example, operators are defined in terms of how they change the state of objects, and planning states are defined as amalgams of the objects' states. The method features two classes of tools: for initial capture and validation of the domain model; and for operationalising the domain model (a process we call compilation) for later planning. Here we focus on compilation tools used to generate macros and goal orders to be utilised at plan generation time. We describe them in depth, and evaluate empirically their combined benefits in plan-generation speed-up. The method's main benefit is in helping the modeller to pro...

In cooperative man-machine interaction, it is necessary but not sufficient for a system to respond truthfully and informatively to a user's question. In particular, if the system has reason to believe that its planned response might mislead the user, then it must block that conclusion by modifying its response. This paper focusses on identifying and avoiding potentially misleading responses by acknowledging types of "informing behavior " usually expected of an expert. We attempt to give a formal account of several-types of assertions that should be included in response to questions concerning the achievement of some goal (in addition to the simple answer), lest the questioner otherwise be misled. 1. Introduction] In cooperative man-machine interaction, it is necessary but not sufficient for a system to respond truthfully and informatively to a user's question. In particular, if the system has reason to believe that its planned response might mislead the user to draw a false conclusion, then it must block that conclusion by modifying or adding to its response.

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This thesis investigates problem-solving in domains featuring incomplete information and multiple agents with opposing goals. In particular, we describe Finesse --- a system that forms plans for the problem of declarer play in the game of Bridge. We begin by examining the problem of search. We formalise a best defence model of incomplete information games in which equilibrium point strategies can be identified, and identify two specific problems that can affect algorithms in such domains. In Bridge, we show that the best defence model corresponds to the typical model analysed in expert texts, and examine search algorithms which overcome the problems we have identified. Next, we look at how planning algorithms can be made to cope with the difficulties of such domains. This calls for the development of new techniques for representing uncertainty and actions with disjunctive effects, for coping with an opposition, and for reasoning about compound actions. We tackle these problems with a...

Andorra-I is an experimental parallel Prolog system based on the Basic Andorra model. This model supports both dependent and-parallelism, by executing determinate goals in parallel, and or-parallelism, stemming from the nondeterminate goals. In this paper, we present a new compiler-based version of the Andorra-I system and discuss its performance. We study the system with a wide set of real-life, non-trivial logic programming applications. As Andorra-I provides a powerful programming model, we are able to include Prolog programs, committed-choice programs, and Andorra-style programs. The results show that the system is very effective at exploiting both forms of parallelism, that it compares well with exclusively or-parallel and exclusively and-parallel systems, and that the extra complexity of the model is manageable. Basic performance compares quite well with other compiler-based systems such as SICStus and JAM.

Planning is one of the most important and oldest fields of AI. However, there is no consensus on how to compare and classify planning systems and methods. Neither the traditional view of planning as search nor the formalization efforts have been able to provide a basis for a classification scheme. This article explores the idea that a perspective based on Newell's knowledge level can be useful for this task. We present a knowledge-level analysis of classical planning systems in terms of models of the problem-solving methods they used. Rather than reengineering these systems in detail, however, our goal is to show how this type of analysis can help define which roles knowledge may play in planning tasks, and how these roles can be used to compare planning methods in terms of (i) which types of knowledge are used, (ii) how they are structured in what we call domain models. As a tool to analyze and represent planning methods we use the KADS methodology. 1 Introduction ...

We extend the hierarchical, precondition-elimination abstraction of Abstrips to nonlinear, least-commitment planners such as Tweak. Speci cally, we show that the combined planning system, AbTweak, satis es the monotonic property, whereby the existence of a lowest level solution implies the existence of a highest level solution that is structurally similar to. This property enables one to prune a considerable amount of the search space without loss of completeness. In addition, we develop a criteria for good abstraction hierarchies, and develop a novel, complete search strategy called Left-Wedge that is optimized for good abstraction hierarchies. We demonstrate the utility of both the monotonic property and the Left-Wedge strategy through a series of empirical tests. Abbreviated Title: Same as the title.

First, we describe PVM-Prolog, a Prolog core extended by an interface to PVM, the Parallel Virtual Machine, a standard software which allows to view a network of heterogeneous machines as a single parallel computer. Besides