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Intelligent agents as a modelling paradigm

University of British Columbia

Intelligent software agents have been used in many applications because they provide useful integrated features that are not available in "traditional" types of software (e.g., abilities to sense the environment, reason, and interact with other agents). Although the usefulness of agents is in having such capabilities, methods and tools for developing them have focused on practical physical representation rather than accurate conceptualizations of these functions. Like other computer systems, intelligent agents usually represent some real world phenomena or environments. Consequently, intelligent agents should closely mimic aspects of the environment in which they operate. In the physical sciences, a conceptual model of a problem can lead to better theories and explanations about the area. Therefore, we ask how can an intelligent agent conceptual framework, properly defined, be used to model complex interactions in various social science disciplines? The constructs used in the implementation of intelligent agents may not be appropriate at the conceptual level, as they refer to software concepts rather than to application domain concepts. Therefore we propose to use a combination of the systems approach and Bunge's ontology as adapted to information systems, to guide us in defining intelligent agent concepts. The systems approach will be used to define the components of the intelligent agents. Once the components have been identified we will use ontology to understand the configurations, transitions, and interrelationships between the components. We will then provide a graphical representation of these concepts for modelling purposes. As a proof of concept for the proposed conceptual model, we apply it to a marketing problem and implement it in an agent-based programming environment called Netlogo. With the aid of the conceptual model, the user was able to quickly visualize the complex interactions of different agents. The use of the conceptual representation even sparked an investigation of previously neglected causal factors which led to a better understanding of the problem. The implications of these findings, and further research avenues, are also discussed. Since the proof of concept was successful, it can be said that we provide an intelligent agent framework that can graphically model phenomena in the social sciences. However, there are other contributions derived from the work, including; a theoretically driven concept of intelligent agent components, a way of showing the interrelationships between these concepts, and the foundation for an ontologically complete model of intelligent agents.

Text

2009-12-23

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http://hdl.handle.net/2429/17238

Business Administration - Management Information Systems

University of British Columbia

UBCV

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