Conference proceeding
Arguments and counterexamples in case-based joint deliberation
ARGUMENTATION IN MULTI-AGENT SYSTEMS, v 4766, pp 36-53
01 Jan 2007
Abstract
Multiagent learning can be seen as applying ML techniques to the core issues of multiagent systems, like communication, coordination, and competition. In this paper, we address the issue of learning from communication arnong agents circumscribed to a scenario with two agents that (1) work in the same domain using a shared ontology, (2) are capable of learning from examples, and (3) communicate using an argumentative framework. We will present a two fold approach consisting of (1) an argumentation framework for learning agents, and (2) an individual policy for agents to generate arguments and counterarguinents (including counterexamples). We focus on argumentation between two agents, presenting (1) an interaction protocol (AMAL2) that allows agents to learn from counterexamples and (2) a preference relation to determine the joint outcome when individual predictions are in contradiction. We present several experiment to asses how joint predictions based on argumentation improve over individual agents prediction.
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Details
- Title
- Arguments and counterexamples in case-based joint deliberation
- Creators
- Santiago Ontanon - Georgia Institute of TechnologyEnric Plaza - Artificial Intelligence Research Institute
- Contributors
- N Maudet (Editor)
- Publication Details
- ARGUMENTATION IN MULTI-AGENT SYSTEMS, v 4766, pp 36-53
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 3
- Grant note
- TIC2006-15140-C03-01 / MID-CBR
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000250752700003
- Scopus ID
- 2-s2.0-38549089499
- Other Identifier
- 991021869009604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods