Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description -- as used in most hierarchical planners -- has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of Paris (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.

The Association for the Advancement of Artificial Intelligence held its 1994 Fall Symposium Series on November 4-6 at the Monteleone Hotel in New Orleans, Louisiana. This article contains summaries of the five symposia that were conducted: (1) Control of the Physical World by Intelligent Agents, (2) Improving Instruction of Introductory AI, (3) Knowledge Representation for Natural Language Processing in Implemented Systems, (4) Planning and Learning: On to Real Applications, and (5) Relevance.

Recent developments have clarified the process of generating partially ordered, partially specified sequences of actions whose execution will achieve an agent's goal. This article summarizes a progression of least commitment planners, starting with one that handles the simple STRIPS representation and ending with UCPOP, a planner that manages actions with disjunctive precondition, conditional effects, and universal quantification over dynamic universes. Along the way, I explain how Chapman's formulation of the modal truth criterion is misleading and why his NP-completeness result for reasoning about plans with conditional effects does not apply to UCPOP.

Fall 1994, 63-75 Abbott, Kathy, see Orlando, Nancy AI and NP-Hard Problems: 1993 Spring Alterman, Richard, see Hendler, James Abhyankar, R. B. Review of Computing Symposium Report.

This program weaves together three threads: (1) AI, areas of AI.

One should not throw out the baby with the bathwater, according to an old aphorism. Some popular recent positions in AI thinking have done just this, we suggest, by rejecting the useful idea of mental representations in their overenthusiastic zeal to correct some simplifications and naiveties in the way traditional AI ideas have sometimes been understood. These "situated" perspectives correctly emphasize that agents live in a social world, using their environments to help guide their actions without needing to always plan their futures in detail; but they incorrectly conclude that the very idea of mental representation is mistaken. This perspective has its intellectual roots in parts of recent sociological thinking which reject the entire fabric of western science. We discuss these ideas and disputes in the form of an illustrated fable concerning nannies and babies.

This report stems from an April 1994 meeting, organized by AAAI at the suggestion of Steve Cross and Gio Wiederhold.1 The purpose of the meeting was to assist ARPA in defining an agenda for foundational AI research. Prior to the meeting, the fellows and officers of AAAI, as well as the report committee members, were asked to recommend areas in which major research thrusts could yield significant scientific gain -- with high potential impact on DOD applications -- over the next ten years. At the meeting, these suggestions and their relevance to current national needs and challenges in computing were discussed and debated. An initial draft of this report was circulated to the fellows and officers. The final report has benefited greatly from their comments and from textual revisions contributed by Joseph Halpern, Fernando Pereira, and Dana Nau.

Information extraction is the task of automaticallypicking up information of interest from an unconstrained text. Informationof interest is usually extracted in two steps. First, sentence level processing locates relevant pieces of information scatteredthroughout the text; second, discourse processing merges coreferential information to generate the output. In the first step, pieces of information are locally identified without recognizing any relationships among them. A key word search or simple patternsearch can achieve this purpose. The second step requires deeperknowledge in order to understand relationships among separately identified pieces of information. Previous information extraction systems focused on the first step, partly because they were not required to link up each piece of information with other pieces. To link the extracted pieces of information and map them onto a structuredoutput format, complex discourse processing is essential. This paperreports on a Japanese information extraction system that merges information using a pattern matcher and discourse processor. Evaluationresults show a high level of system performance which approaches human performance.

Real-time problem solving is not only reasoning about time, it is also reasoning in time. Many techniques, mostly ad hoc, have been developed in both the real-time community and the AI community for solving problems within time constraints. This article is an attempt to step back from the details and examine the entire issue of real-time problem solving from first principles. We examine the degrees of freedom available in structuring the problem space and the search process to reduce problem-solving variations and produce satisficing solutions within the time available.

Real-time problem solving is not only reasoning about time, it is also reasoning in time. This ability is becoming increasingly critical in systems that monitor and control complex processes in semiautonomous, ill-structured, real-world environments. Many techniques, mostly ad hoc, have been developed in both the real-time community and the AI community for solving problems within time constraints. However, a coherent, holistic picture does not exist. This article is an attempt to step back from the details and examine the entire issue of real-time problem solving from first principles. We examine the degrees of freedom available in structuring the problem space and the search process to reduce problem-solving variations and produce satisficing solutions within the time available. This structured approach aids in understanding and sorting out the relevance and utility of different real-time problem-solving techniques.