Les Gasser

AAMAS Conference 2007 Conference Paper

A Unified Framework for Multi-Agent Agreement

• Kiran Lakkaraju
• Les Gasser

Multi-Agent Agreement Problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from distributed sensor networks, to meeting scheduling, to development of norms, conventions, and language. While much work has been done on particular agreement problems no unifying framework exists for comparing MAPs that vary in, e. g. , strategy space complexity, inter-agent accessibility, and solution type, and understanding their relative complexities. We present such a unification, the Distributed Optimal Agreement (DOA) framework, and show how it captures a wide variety of agreement problems. To demonstrate DOA and its power we apply it to convention evolution.

AAMAS Conference 2006 Conference Paper

Convergence Analysis for Collective Vocabulary Development

• Jun Wang
• Les Gasser
• Jim Houk

AAMAS Conference 2006 Conference Paper

Learning a Common Language Through an Emergent Interaction Topology

• Samarth Swarup
• Kiran Lakkaraju
• Les Gasser

AAMAS Conference 2002 Conference Paper

MACE3J: Fast, Flexible Distributed Simulation of Large, Large-Grain Multi-Agent Systems

• Les Gasser
• Kelvin Kakugawa

AAMAS Conference 2002 Conference Paper

Mutual online concept learning for multiple agents

• Jun Wang
• Les Gasser

IJCAI Conference 1993 Conference Paper

Social Knowledge and Social Action: Heterogeneity in Practice

• Les Gasser
• University of Southern California

AAAI Conference 1991 Conference Paper

A Dynamic Organizational Architecture for Adaptive Problem Solving

• Les Gasser

IJCAI Conference 1991 Conference Paper

Multiple Approaches to Multiple Agent Problem Solving James Hendler (Chair)

• Danny Bobrow
• Les Gasser
• Carl Hewitt
• Marvin

AIJ Journal 1991 Journal Article

Social conceptions of knowledge and action: DAI foundations and open systems semantics

• Les Gasser

This article discusses foundations for Distributed Artificial Intelligence (DAI), with a particular critical analysis of Hewitt's Open Information Systems Semantics (OISS). The article sets out to do five things: • • It presents a brief overview of current DAI research including motivations and concepts, and discusses some of the basic problems in DAI. • • It introduces several principles that underly a fundamentally multi-agent (i. e. , social) conception of action and knowledge for DAI research. These principles are introduced to provide definitions, to delimit the discussion of OISS and as background against which to assess its contributions. • • It analyzes the main points of OISS in relation to these principles. • • It shows how attention to these principles can strengthen OISS approach to foundations for DAI. • • It traces some of the implications of this synthesis for theorizing and system-building in AI. The OISS approach productively challenges some conceptions of knowledge, reasoning, and action in classical AI research. However, it sometimes ignores the sophistication and richness of contemporary DAI research. Several of the key concepts of OISS are not clearly enough defined or operationalized, and the article points out several ways to strengthen the OISS approach.