By Paolo Bresciani, Paolo Giorgini, Brian Henderson-Sellers, Graham Low, Michael Winikoff

This ebook constitutes the completely refereed post-proceedings of the sixth foreign Bi-Conference Workshop on Agent-Oriented details platforms, AOIS 2004, held in Riga, Latvia in June and in big apple, big apple, united states in July 2004.

The 15 revised complete papers offered have passed through rounds of reviewing and development and have been chosen from an preliminary overall of 36 submissions. The papers are geared up in topical sections on details structures, research and modeling, methodologies, and applications.

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Extra info for Agent-Oriented Information Systems II: 6th International Bi-Conference Workshop, AOIS 2004, Riga, Latvia, June 8, 2004 and New York, NY, USA, July 20,

Example text

S. Decker nation points within an input plan; Branch figures out the task branch of the execution path that contains a selected target coordination point; ApplyMechanism applies a mechanism to a selected coordination point. Further explanation can be found in [17]. Given: (P, M, R) Input: P , an uncoordinated plan with uncertainty represented using EHTN; M , the extended set of GPGP coordination mechanisms; R, the role of this agent involved in a particular coordination process. Output: S, a coordinated schedule with uncertainty removed.

An EMS system information flow chart mation flow of the EMS model. First, incidents are reported to the dispatches. Then, the incidents are responded to by police departments, ambulance companies and hospitals, each made up of equipment and personnel trained for this purpose. In the model, a police car agent represents the role of a police department. Similarly, an ambulance agent represents an ambulance company. The police department and ambulance company may have multiple vehicles. There are also hospitals and corresponding dispatches: ambulance dispatch, police dispatch and 911 dispatch.

Using this mechanism, all the service components are defined as mental state classes, and the input/output specifications of the framework are made to correspond to preconditions/postconditions of Bee-gent. The planner of Bee-gent is used to implement the SMAs operations. For each mental state class, this planner mechanism can set goals for which the class is activated and for the goals to be executed next. The number of state classes that can be executed simultaneously is set to 1, so that state classes having the same precondition are selected one after another by changing the priority (utility value) for goal selection.

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