Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
A flexible negotiation model for an agent-based software process modelling
International Journal of Computer Applications in Technology
Knowledge discovery for adaptive negotiation agents in e-marketplaces
Decision Support Systems
Multistage Fuzzy Decision Making in Bilateral Negotiation with Finite Termination Times
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Negotiation in electronic commerce: a study in the Latin-American market
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
On the formulation of competitive negotiations in Web applications: The Latin-American market case
Expert Systems with Applications: An International Journal
Buyer behavior adaptation based on a fuzzy logic controller and prediction techniques
Fuzzy Sets and Systems
Learning to negotiate optimally in non-stationary environments
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Electronic Commerce Research and Applications
On the use of particle swarm optimization and Kernel density estimator in concurrent negotiations
Information Sciences: an International Journal
Bilateral single-issue negotiation model considering nonlinear utility and time constraint
Decision Support Systems
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This paper studies adaptive bilateral negotiation between software agents in e-commerce environments. Specifically, we assume that the agents are self-interested, the environment is dynamic, and both agents have deadlines. Such dynamism means that the agents驴 negotiation parameters (such as deadlines and reservation prices) are functions of both the state of the encounter and the environment. Given this, we develop an algorithm that the negotiating agents can use to adapt their strategies to changes in the environment in order to reach an agreement within their specific deadlines and before the resources available for negotiation are exhausted. In more detail, we formally define an adaptive negotiation model and cast it as a Markov Decision Process. Using a value iteration algorithm, we then indicate a novel solution technique for determining optimal policies for the negotiation problem without explicit knowledge of the dynamics of the system. We also solve a representative negotiation decision problem using this technique and show that it is a promising approach for analyzing negotiations in dynamic settings. Finally, through empirical evaluation, we show that the agents using our algorithm learn a negotiation strategy that adapts to the environment and enables them to reach agreements in a timely manner.