Defines principles for organizing semantic relations represented by slots in frame-structured knowledge bases. We organize slots based on the knowledge-level semantics of relations and the symbol-level function of slots that implement the representation language. The symbol-level organization of slots depends on the inferencing and expressive capabilities of the knowledge representation system. At the knowledge level, two entirely different organizational schemes are identified: one based on linguistic similarities and differences, and another based on the types of concepts being related.

This paper explores the specification and semantics of multiagent problem-solving systems, focusing on the representations that agents have of each other. It provides a declarative representation for such systems. Several procedural solutions to a well-known test-bed problem are considered, and the requirements they impose on different agents are identified. A study of these requirements yields a representational scheme based on temporal logic for specifying the acting, perceiving, communicating, and reasoning abilities of computational agents. A formal semantics is provided for this scheme. The resulting representation is highly declarative, and useful for describing systems of agents solving problems reactively.>

MINDS (Multiple Intelligent Node Document Servers) is a distributed system of knowledge-based query engines for efficiently retrieving multimedia documents in an office environment of distributed workstations. By learning document distribution patterns, as well as user interests and preferences during system usage, it customizes document retrievals for each user. A two-layer learning system has been implemented for MINDS. The knowledge base used by the query engine is learned at the lower level with the help of heuristics for assigning credit and recommending adjustments; these heuristics are incrementally refined at the upper level. © 1986 John Wiley & Sons, Inc.

An architecture and implementation for a distributed artificial intelligence (DAI) system are presented, with emphasis given to the control and communication aspects. Problem solving by this system occurs as an iterative refinement of several mechanisms, including problem decomposition, kernel-subproblem solving, and result synthesis. In order for all related nodes to make optimum use of the information obtained from these problem-solving mechanisms, the system dynamically reconfigures itself, thereby improving its performance during operation. This approach offers the possibilities of increased real-time response, improved reliability and flexibility, and lower processing costs. A major component in the node architecture is a database of metaknowledge about the expertise of a node's own expert systems and those of the other processing nodes. This information is gradually accumulated during problem solving. Each node also has a dynamic-planning ability, which guides the problem-solving process in the most promising direction and a focus-control mechanism, which restricts the size of the explored solution space at the task level while reducing the communication bandwidths required. It also has a question-and-answer mechanism, which handles internode communications. Examples in the domain of digital-logic design are given to demonstrate the operation of the system.