Towards the Construction of a Multi-agent Approach for Discovering the Meaning of Natural Language Collaborative Conversations

On the one hand, natural language is the main communication media for humans. It has a complex construction, based on the diversity of meaning for words and expressions according to the context. On the other hand, computers are not prepared to handle this ambiguity. The present work aims at presenting a multi-agent approach for dealing with the problem of discovering the meaning of expressions written in Spanish, based on a flexible recovery system and Bayesian principles. At a first stage, agents are supposed to identify the role of the words composing a sentence. At a second stage, a second set of agents is supposed to coordinate among them in order to assemble a meaning. Our research forms part and contributes to the analysis of collaborative conversations among participants in a web-based collaborative learning environment. © 2008 IEEE

AN ARCHITECTURAL FRAMEWORK FOR NATURAL LANGUAGE INTERFACES TO AGENT SYSTEMS

In this paper, we describe an architectural framework for the development of natural language interfaces to agent systems. Since the communication between human and artificial agents is mostly task-related, the focus of the suggested architecture is on action representations as core structure and thread in the overall processing. The architectural framework we suggest is based on various forms of action representations and consists of a sequence of transformations, which converts the user’s verbal input into a suitable set of agent actions to produce a response to the input. This process reduces stepwise the complexity and ambiguity of the natural language input by using predefined sets of interim actions at different levels, and thus increases the robustness and reliability of the natural language interface. The architecture was employed in the design of several natural language interfaces to agent systems