Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Computational conflicts
Conflicting agents
Argumentation as distributed constraint satisfaction: applications and results
Proceedings of the fifth international conference on Autonomous agents
Persuasion as a Form of Inter-Agent Negotiation
Revised Papers from the Second Australian Workshop on Distributed Artificial Intelligence: Multi-Agent Systems: Methodologies and Applications
A Framework for Argumentation-Based Negotiation
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Distributed Constraint Satisfaction Algorithm for Complex Local Problems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
A Dialogue Game Protocol for Agent Purchase Negotiations
Autonomous Agents and Multi-Agent Systems
Towards interest-based negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Argumentation-based negotiation
The Knowledge Engineering Review
Advantages of a leveled commitment contracting protocol
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Argument-based negotiation in a social context
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Managing social influences through argumentation-based negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Classification Structure for Automated Negotiations
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Hypotheses refinement under topological communication constraints
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
STRATUM: A METHODOLOGY FOR DESIGNING HEURISTIC AGENT NEGOTIATION STRATEGIES
Applied Artificial Intelligence
Dialogue games that agents play within a society
Artificial Intelligence
On the Formalization of an Argumentation System for Software Agents
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Negotiation among autonomous computational agents: principles, analysis and challenges
Artificial Intelligence Review
Arguing and negotiating in the presence of social influences
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Argument-Based negotiation in a social context
ArgMAS'05 Proceedings of the Second international conference on Argumentation in Multi-Agent Systems
When agents communicate hypotheses in critical situations
DALT'06 Proceedings of the 4th international conference on Declarative Agent Languages and Technologies
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Argumentation-based negotiation (ABN) is an effective means of resolving conflicts in a multi-agent society. However, it consumes both time and computational resources for agents to generate, select and evaluate arguments. Furthermore, in many cases, argumentation is not the only means of resolving conflicts. Thus, some could be avoided either by finding an alternative means (evading the conflict) or by modifying the intended course of action (re-planning). Therefore, it would be advantageous for agents to identify those situations and weigh the costs and the benefits of arguing before using it to resolve conflicts. To this end, we present a preliminary empirical analysis to evaluate the performance of a simple ABN system, with respect to other non-arguing approaches, in a particular task allocation scenario. In our experiments, we simulate a multi-agent community and allow the agents to use a combination of ABN, evasion and re-planning techniques to overcome conflicts that arise within the community. Analysing the observed results, we show that, in our domain, ABN presents an effective means of resolving conflicts when the resources are constrained. However, we also show it is a more costly and less effective means, compared to evasion and re-planning methods, when resources are more abundant.