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Coalition formation in social networks consisting of a graph of interdependent agents allows many choices of which task to select and with whom to partner in the social network. Agents communicate with agents within n network links in their surrounding network. These agents are considered part of an agent's local neighborhood. Agents maintain a database of skills possessed by agents in their local neighborhood. We introduce agents who use the information gathered from their local neighborhood to modify their network links to discover effective network structures. Egalitarian agents seek equal distribution of connections among agents, resulting in a dense network structure. Inventory agents seek to connect to agents who possess a skill not found in their current local neighborhood.

Replanning, which discards all alternatives found in previous execution, and step-by-step backtracking, which iterates back through all previous selection decisions, are two extreme ends of exception handling in agent systems. We describe a new "jump" approach to maintaining a tradeoff between them. Choice points, which are maintained in a stack, are introduced to record all plans found by the agent's planning procedures. By iterating on the stack, and reasoning about plan characteristics, an agent can find and launch a suitable plan prior to or in parallel with a compensation process for the failed path, thus achieving its goals efficiently in the presence of exceptions. Our approach combines and utilizes several beneficial features of a BDI agent, such as its data structure and deliberation cycle, together with an open nested transaction system which supports architectural-level concurrency control and distribution management.

In an agent system, the ability to handle problems and recover from them is important in sustaining stability and providing robustness. We claim that execution logging is essential to support agent system robustness, and that agents should have architectural-level support for logging and recovery methods. We describe an infrastructure-level, default methodology for agent problem-handling, based on logging, and supported by declaratively encoding domain-specific knowledge related to changes in goal status and semantic compensations. Via logging, the approach allows repair of already-completed as well as current goals. We define a language, APLR, to support and constrain incremental specification of problem-handling information, with the agents' problem-handling behaviour increasing in so phistication as more knowledge is added to the system. The approach is implemented by mapping the methodology and domain knowledge to 3APL-like plan rules extended to support logging.

In agent systems, an agent's recovery from, execution problems is often complicated by constraints that are not present in a more traditional distributed, database systems environment. An analysis of agent-related crash recovery issues is presented, and requirements for achieving 'acceptable' agent crash recovery are discussed. Motivated by this analysis, a novel approach to managing agent recovery is presented. It utilises an event-and task-driven model for employing semantic compensation; task retries, and checkpointing. The compensation/retry model requires a situated model of action and failure, and provides the agent with an emergent unified, treatment of both crash recovery and run-time failure-handling. This approach helps the agent to recover acceptably from crashes and execution problems; improve system predictability; manage inter-task dependencies; and address the way in which exogenous events or crashes can trigger the need for a re-decomposition of a task. Agent architecture is then presented, which uses pair processing to leverage these recovery techniques and increase the agent's availability on crash restart.

Increasing expense of developing microelectronic manufacturing technology threatens to slow the growth of the electronics industry. This paper describes the progress we have made in developing methodologies and techniques to reduce the cost of designing microelectronic manufacturing flows. Our approach is to partition the task of process flow design into a number of abstraction levels and provide mechanisms to translate between these levels. This approach results in a top-down design methodology where requirements from higher levels of abstraction are successively reduced to lower abstraction levels, while meeting the constraints imposed by the lower levels. The paper enumerates the abstraction levels we have identified so far, and describes the translation mechanisms for a class of process design tasks: modification of an existing flow in response to change in performance requirements. Finally, we briefly describe a design environment that incorporates these ideas.

The MCC InfoSleuth/sup TM/ Project is an agent-based system for information gathering and analysis tasks performed over networks of autonomous information sources. A key motivation of the InfoSleuth system is that real information gathering applications require long-running monitoring and integration of information at various levels of abstraction. To this end, InfoSleuth agents enable a loose integration of technologies allowing: (1) extraction of semantic concepts from autonomous information sources; (2) registration and integration of semantically annotated information from diverse sources; and (3) temporal monitoring, information routing, and identification of trends appearing across sources in the information network. In this paper we discuss the agents in InfoSleuth applications and the goal-driven interaction patterns that enable them to dynamically organize and cooperate to perform integrated and temporal information-gathering tasks.