Intelligent Agents in Granular Worlds

In this study, we introduce a concept of granular agents and elaborate on various representation, communication and learning issues arising in this framework. A granular world, in which the granular agents interact, embodies a collection of information granules being regarded as generic conceptual entities used to represent knowledge and handle problem solving. On the other hand, granular computing is a paradigm supporting knowledge representation, coping with complexity, and facilitating interpretation of processing. In this sense, it is crucial to all man-machine pursuits, data mining and intelligent data analysis, in particular. There are three essential facets that are inherently associated with any agent, that is formalism used to describe and manipulate information granules and the granularity of the granules themselves as well as the internal structure of the agents. There are numerous formal models of granular worlds ranging from set-theoretic developments (including sets, fuzzy sets, and rough sets) to probabilistic counterparts (random sets, random variables and alike). In light of the evident diversity of granular world (occurring both in terms of the underlying formal settings as well as levels of granularity), we elaborate on their possible interaction and identify implications of such communication. More specifically, we have cast these in the form of the interoperability problem, that is associated with the representation of information granules. Moreover, we explore various internal models of agents including the concepts stemming from fuzzy state machines and discuss pertinent models of learning.