M. V. Nagendra Prasad

Cooperative information-gathering: a distributed problem-solving approach

Two approaches to the problem of information-gathering, that may be characterised as distributed processing and distributed problem-solving, are contrasted. The former is characteristic of most existing information-gathering systems, and the latter is central to research in multi-agent systems. The features of complex information-carrying environments and the information-gathering task are examined, demonstrating both the utility of viewing information-gathering as distributed problem-solving and difficulties with viewing it as distributed processing. A new approach is proposed to information-gathering based on the distributed problem-solving paradigm and its attendant body of research in multi-agent systems and distributed artificial intelligence. This approach, called cooperative information-gathering, involves concurrent, asynchronous discovery and composition of information spread across a network of information servers. Top-level queries drive the creation of partially elaborated information-gathering plans, resulting in the employment of multiple semi-autonomous cooperative agents for the purpose of achieving goals and subgoals within those plans. The system as a whole satisfies, trading off solution quality and search cost while respecting user-imposed deadlines. Current work on distributed and agent-based approaches to information-gathering is also surveyed.

Retrieval and Reasoning in Distributed Case Bases1

Abstract The proliferation of electronically available networked information has led researchers to examine the issues involved in developing automated methods for gathering information in response to a query from a user. However, most of this literature deals with locating, gathering, and selecting the best response to a query from among a multitude of responses from different repositories or digital libraries. This paper deals with a different model of response to a query, involving composition of mutually related partial responses spread across a network of information repositories. We present a system for cooperative retrieval and composition of a case in which subcases are distributed across different agents in a multiagent system. From a Gestalt perspective, a good overall case may not be the one derived from the summation of best subcases. Each agents local view may result in best local cases, which when assembled may not result in the best overall case in terms of global measures. We propose a negotiation-driven case retrieval algorithm as an approach to dynamically resolving inconsistencies between different case pieces during the retrieval process.

The role of Learning in systems of reusable heterogeneous design agents

In this abstract, we discuss the use of learning techniques to improve performance and solution quality in multiagent parametric design. We have implemented the L-TEAM testbed for empirical evaluation of two forms of learning (described in detail below):