Cooperation and Learning among Case-based Reasoning Agents
"Communication and Learning in a Wide World"
||Currently multiagent systems are implemented using the Noos Agent
Platform, an agent programming environment that supports agents designed
representation language to communicate, cooperate, and negotiate across
the network in a FIPA-compliant way.
case bartering for multiagent learning
A Bartering Approach to Improve
- (Available online
- Santiago Ontañon
Multiagent systems offer a new paradigm to organize AI applications.
We focus on the application of Case-Based Reasoning to multiagent
systems. CBR offers the individual agents the capability of
autonomously learn from experience. In this paper we present a
framework for collaboration among agents that use CBR. We present
explicit strategies for case bartering that address the issue of
agents having a biased view of the data. The outcome of bartering is
an improvement of individual agent performance and of overall multiagent
system performance that equals the ideal situation where all agents
have an unbiased view of the data. We also present empirical results
illustrating the robustness of the case bartering process for several
configurations of the multiagent system and for three different CBR
- To be published in Int. Conf. Autonomous Agents and Multiagent systems
AAMAS'02. ACM Press.
case retention policies for multiagent learning
In this paper we present a framework for collaboration among agents
that use CBR. We present explicit strategies for case retain where the
agents take in consideration that they are not learning in isolation but in
a multiagent system. We also present case bartering as an effective
strategy when the agents have a biased view of the data. The outcome of
both case retain and bartering is an improvement of individual agent
performance and overall multiagent system performance. We also present
empirical results comparing all the strategies proposed.
- Collaboration Strategies to Improve
- (Available online [PDF file])
To be published in Machine Learning ECML'02
(to appear in Lecture Notes on Artificial Intelligence, Springer
Learning When to
Collaborate among Learning Agents.
- (Available online
- Santiago Ontañon
Multiagent systems offer a new paradigm where learning techniques can
be useful. We focus on the application of lazy learning to multiagent
systems where each agents learns individually and also learns when to
cooperate in order to improve its performance. We show some
experiments in which CBR agents use an adapted version of LID (Lazy
Induction of Descriptions), a CBR method for classification. We show
that a collaboration policy (called Bounded Counsel) among agents that
improve the agents performance with respect to their isolated
performance. Later, we use decision tree induction and discretization techniques to
learn how to tune the Bounded Counsel policy to a specific multiagent
system---preserving always the individual autonomy of agents and the
privacy of their case-bases. Empirical results concerning accuracy,
cost, and robustness with respect to number of agents and case base
size are presented. Moreover, comparisons with the Committee collaboration
policy (where all agents collaborate always) are also presented.
- Published in L. De Raedt, P. Flach (Eds.)
Machine Learning: EMCL 2001. Lecture Notes in Artificial Intelligence
2167, p. 394-405. Springer-Verlag.
Cooperation Policies for Case-Based Reasoning Agents
- Ensemble Case-based Reasoning:
Collaboration Policies for Multiagent Cooperative CBR.
- (Available online [PDF
- Enric Plaza, and
- Multiagent systems offer a new paradigm to organize AI applications.
Our goal is to develop techniques to integrate CBR into applications
that are developed as multiagent systems. CBR offers the multiagent
systems paradigm the capability of autonomously learning from
experience. In this paper we present a framework for collaboration
among agents that use CBR and some experiments illustrating the
framework. We focus on three collaboration policies for CBR agents:
Peer Counsel, Bounded Counsel and Committee policies. The experiments
show that the CBR agents improve their individual performance
collaborating with other agents without compromising the privacy of
their own cases. We analyze the three policies concerning
accuracy, cost, and robustness with respect to number of agents and
case base size.
- Published in Case-Based Reasoning Research and Development: ICCBR 2001,.
Lecture Notes in Artificial Intelligence 2080, p. 437-451.