A causal multiagent system approach for automating processes in intelligent organizations
Hector G. Ceballos
HECTOR GIBRAN CEBALLOS CANCINO;223871
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The current competitive environment motivated Knowledge Management (KM) theorists to propose the notion of Organizational Intelligence (OI) for enabling a rapid response of large organizations to changing conditions. KM practitioners consider that OI resides in both processes and members of the organization, and recommend implementing learning mechanisms and empowering participants with knowledge and decision making for improving organization competitiveness. In that sense, have being provided some theoretical definitions and practical approaches (e.g. Electronic Institutions and Autonomic Computing), as well as commercial platforms (e.g. Whitestein Technologies), that implement OI to a certain extent. Some of these approaches have already taken advantage of tools and formalisms developed in Artificial Intelligence (e.g. Knowledge Representation, Data Mining, and Intelligent Agents). In this research, I propose the use of Aristotelian Causality for modeling organizations, as well as its members, as intelligent entities through the Causal Artificial Intelligence Design (CAID) theory, and present the Causal Multi-Agent System (CMAS) framework for automating organizational processes. Bayesian Causal Networks are extended to Semantic Causal Networks (SCN) for providing an explicit representation of the goals, participants, resources and knowledge involved in these processes. The CAID principles and the SCN formalism are used for providing a probabilistic extension of the goal-driven Belief-Desire-Intention agent architecture, called Causal Agent. Lastly, the capabilities of this framework are demonstrated through the specification and automation of an information auditing process.