Computational Negotiation

We are exploring various ways to provide a computational framework for negotiation. In our view, negotiation is a general problem-solving framework which a group, or even individual, can apply. The goal is to seek an ideal solution while balancing conflicting goals and/or beliefs.

Basic Elements of Computational Negotiation

Below we characterize some of the basic elements involved in a computation model of negotiation. This model extends current human-oriented negotiation models by providing explicit knowledge representations and process descriptions which are amenable to computerization.
Perspectives
Representations of varied agent knowledge, values, or beliefs. Agent perspectives may be described in one language (e.g., predicate calculus), or varied representations which require translations before analysis.
Decision-making
A method for determining which alternative, from a given set, is deemed best. Basic theories include utility theory and argumentation theory.
Architecture
Ideally, multiple independent agents asynchronously communicate continuously as they: refine their individual and group preferences, and cooperatively seek a group ideal solution. However, architectural variations include: a centralized arbiter, non-networked agents, and human agents.
Integration
We place the actual "negotiation dance" within a more general framework involving the pre- and post-negotiation steps. The basic steps of the framework are listed below.
Conflict Detection
Find differences between perspectives and characterize them into syntactic and semantic categories.
Resolution Search
Search for a solution acceptable to all perspectives. Using the characterization of the initial conflicts as a starting point, new alternatives are generated under the guidance of all perspectives.
Resolution Generation
A variety of methods can be applied to generate new alternatives given an alternative abstraction space and agent goal hierarchies. Methods include: compromise, substitution, compensation, and dissolution.
Resolution Choice
After resolutions are generated, the "best" must be selected. If the search is incremental, then resolution search consists of a series of resolution generations followed by resolution choices until an acceptable resolution is reached.
Resolution Implementation
Typically, resolutions are sought in an abstraction space which is separate from the implementation space. Hence, once an abstract resolution is reached, it must be mapped into the implementation space.

Applications of Computational Negotiation

Currently, our Computational Negotiation Group is exploring a number of computational negotiation projects.
Negotiated Analysis and Design
Augmenting traditional analysis and design with a focus on the acquisition and transformation of goal sets with the aim of maximizing multi-agent satisfaction.
Negotiation Agencies
Providing automated negotiation-based mediators to facilitate transactions among agents and proxy agents.
Collaborative Interactive Negotiation
Providing interactive negotiation support to facilitate collaborative human problem-solving.

1994, William N. Robinson. All rights reserved. Reproduction of all or part of this work is permitted for educational or research use provided that this copyright notice is included in any copy.

ciswnr@gsusgi2.gsu.edu