Abstract of FKI-232-99

Document-Name:  fki-232-99.ps.gz
Title:		Achieving Coordination through Combining Joint Planning
                and Joint Learning
Authors:	Gerhard Weiss 
Revision-Date:	1999/07/15 
Category:	Technical Report (Forschungsberichte Künstliche Intelligenz)
Abstract:	There are two major approaches to activity coordination
                in multiagent systems. First, by endowing the agents with
                the capability to jointly plan, that is, to jointly generate 
                hypothetical activity sequences. Second, by endowing the 
                agents with the capability to jointly learn, that is, to 
                jointly choose the actions to be executed on the basis of what
                they know from experience about the interdependencies of their
                actions. This paper describes a new algorithm called JPJL 
                (``Joint Planning and Joint Learning'') that combines both 
                approaches. The primary motivation behind this algorithm is
                to bring together the advantages of joint planning and joint 
                learning while avoiding their disadvantages. Initial 
                experimental results are provided that illustrate
                the potential benefits and shortcomings of the JPJL algorithm.
Keywords:	Multiagent systems, joint planning, joint learning,
                JPJL algorithm
Size:		15 pages
Language:	English
ISSN:		0941-6358
Copyright:	The ``Forschungsberichte Künstliche Intelligenz''
		series includes primarily preliminary publications,
		specialized partial results, and supplementary
		material. In the interest of a subsequent final
		publication these reports should not be copied. All
		rights and the responsability for the contents of the
		report are with the authors, who would appreciate
		critical comments.

Gerhard Weiss