Dialogue Based Decision Making in Online Trading
Journal Title: Transactions on Machine Learning and Artificial Intelligence - Year 2017, Vol 5, Issue 3
Abstract
Software agents, acting on behalf of humans, have been identified as an important solution for future electronic markets. Such agents can make their own decisions based on given prior preferences and the market environment. These preferences can be described using Web Ontology Languages (OWL), while the market mechanism can be represented in a machine-understandable way by utilizing the technique of Semantic Web Services (SWS). Besides, SWS enables agents to automatically discover, select, compose and invoke services. To extend the dependability and interactivity of SWS, we have utilized dialogue games and the Proof-Carrying Code (PCC) to enable buyers to interact with sellers, so that desirable properties for an online auction market can be automatically certified. Our decision-making framework combines formal proofs with informal evidence collected by web services in a dialogue game between a seller and a buyer. We have implemented our approach and experimental results have demonstrated the feasibility as well as the validity of this framework as an enabler for a buyer agent to enter or not an online auction.
Authors and Affiliations
Wei Bai, Emmanuel M. Tadjouddine, Terry R. Payne, Gangmin Li
Keywords
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- EP ID EP275524
- DOI 10.14738/tmlai.53.3390
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