Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
A Framework for Argumentation-Based Negotiation
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
Argumentation-based negotiation
The Knowledge Engineering Review
Handling threats, rewards, and explanatory arguments in a unified setting: Research Articles
International Journal of Intelligent Systems
Predicting opponent's moves in electronic negotiations using neural networks
Expert Systems with Applications: An International Journal
JADE: A software framework for developing multi-agent applications. Lessons learned
Information and Software Technology
A unified and general framework for argumentation-based negotiation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A Generic Framework for Argumentation-Based Negotiation
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Simulation of sequential data: An enhanced reinforcement learning approach
Expert Systems with Applications: An International Journal
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Building user argumentative models
Applied Intelligence
Architectures for negotiating agents
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Automatic price negotiation on the web: An agent-based web application using fuzzy expert system
Expert Systems with Applications: An International Journal
A hybrid agent architecture integrating desire, intention and reinforcement learning
Expert Systems with Applications: An International Journal
Bargaining and argument-based negotiation: some preliminary comparisons
ArgMAS'04 Proceedings of the First international conference on Argumentation in Multi-Agent Systems
Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Deciding what argument to utter during a negotiation is a key part of the strategy to reach an expected agreement. An agent, which is arguing during a negotiation, must decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection policy. By following this policy, the agent observes some factors of the negotiation context (for instance, trust in the opponent and expected utility of the negotiated agreement). Usually, argument selection policies are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection policy is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the different negotiation contexts as the agent gains experience. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection effectiveness by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment. We obtained promising results in both.