Online agent supervision in the situation calculus

Agent supervision is a form of control / customization where a supervisor restricts the behavior of an agent to enforce certain requirements, while leaving the agent as much autonomy as possible. In this work, we investigate supervision of an agent that may acquire new knowledge about her environment during execution, for example, by sensing. Thus we consider an agent's online executions, where, as she executes the program, at each time point she must make decisions on what to do next based on what her current knowledge is. This is done in a setting based on the situation calculus and a variant of the ConGolog programming language. The main results of this paper are (i) a formalization of the online maximally permissive supervisor, (ii) a sound and complete technique for execution of the agent as constrained by such a supervisor, and (iii) a new type of lookahead search construct that ensures nonblockingness over such online executions.