A BDI-based continuous-time modelling approach for intracellular dynamics is presented. It is shown how temporalised BDI-models make it possible to model intracellular biochemical processes as decision processes. By abstracting from some of the details of the biochemical pathways, the model achieves understanding in nearly intuitive terms, without losing veracity: classical intentional state properties such as Beliefs, Desires and Intentions, are founded in reality through precise biochemical relations. In an extensive example, the complex regulation of Escherichia coli vis-à-vis lactose, glucose and oxygen is simulated as a discrete-state, continuous-time temporal decision manager. Thus a bridge is introduced between two different scientific areas: the area of BDI-modelling and the area of intracellular dynamics.

This paper addresses the design of an ambient agent model that incorporates model-based reasoning methods for the analysis of internal causes of observed undesired behaviours of a human, and for determination of actions that remedy such causes. The models used are based on causal and dynamical relations and integrate numerical aspects. By the model-based reasoning methods hypotheses, observations and actions are generated. Control parameters within these processes are described that allow the ambient agent to focus the reasoning. These control parameters are related to each other and to specific domain and situation characteristics, such as time pressure, or criticality of a situation. 1.

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Abstract. Ambient agents react on humans on the basis of their information obtained by sensoring and their knowledge about human functioning. Appropriate types of reactions depend on in how far an ambient agent understands the human. On the one hand, such an understanding requires that the agent has knowledge to a certain depth about the human’s physiological and mental processes in the form of an explicitly represented model of the causal and dynamic relations describing these processes. On the other hand, given such a model representation, the agent needs reasoning methods to derive conclusions from the model and the information available by sensoring. This paper presents a number of such model-based reasoning methods. They have been formally specified in an executable temporal format, which allows for simulation of reasoning traces and automated verification in a dedicated software environment. A number of such simulation experiments and their formal analysis are described. 1

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