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Although feature-based process planning plays a vital role in automating and integrating design and manufacturing for e cient production, its oOE -line properties prevent the shop¯oor controller from rapidly coping with dynamic shop¯oor status such as unexpected production errors and rush orders. This paper proposes a conceptual framework of the adaptive and dynamic process planning system that can rapidly and dynamically generate the needed process plans based on shop oor status. In particular, the generic schemes for constructing dynamic planning models are suggested. The dynamic planning models are constructed as neural network forms, and then embedded into each process feature in the process plan. The shop¯oor controller will execute them to determine machine, cutting tools, cutting parameters, tool paths and NC codes just before the associated process feature is machined. The dynamic nature of process planning enables the shop oor controller to increase¯exibility and e ciency in unexpected situations.

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We propose a predictive modeling framework for human-involved complex systems in which humans play controlling roles. Affordance theory provides definitions of human actions and their associated properties, and the affordance-based Finite State Automata (FSA) model is capable of mapping the nondeterministic human actions into computable components in modeling formalism. In this paper, we further investigate the role of perception in human actions and examine the representation of perceptual elements in affordance-based modeling formalism. We also propose necessary and sufficient conditions for mapping perception-based human actions into systems theory to develop a predictive modeling formalism in the context of prospective control. A driving example is used to show how to build a formal model of humaninvolved complex system for prospective control. The suggested modeling frameworks will increase the soundness and completeness of a modeling formalism as well as can be used as guide to model human activities in a complex system.

In this paper, we propose a novel agent-based simulation modeling of human behaviors. A conceptual framework of human behavioral simulation is suggested using the ecological definition of affordances in order to mimic perception-based human actions interacting with the environment. A simulation example of a 'warehouse fire evacuation' is illustrated to demonstrate the applicability of the proposed framework. The perception-based human behaviors and planning algorithms are adapted and embedded within human agent models using the Static and Dynamic Floor Field Indicators, which represent the evacuee's prior knowledge of the floor layout and perceivable information of dynamic environmental changes, respectively. The proposed framework is expected to capture the natural manners in which humans participate in systems and enhance the simulation fidelity by incorporating cognitive intent into human behavior simulations.