Diagrammatic reasoning for planning and intelligent control

2001 IEEE Control Systems  
C ontrol has to do with the intelligent, adaptive execution of a piece of a task, or an action, and with its interaction with the environment; at the same time, it copes with the disturbances coming from the external world (i.e., with the "struggle of the world" against our intentions). Planning has to do with the definition of sequences of actions, or tasks, to attain complex goals. The relation between planning and control is traditionally considered hierarchical: planning is performed at a
more » ... gher level of abstraction as compared with control. Artificial intelligence (AI) planning systems are calculi that operate on explicit, declarative representations of both actions and states of the world. Their primitive terms denote actions, constraints, events, situations, scheduling relations, temporal entities and their relations, and so on. Paradigmatic of this approach is traditional, symbolic AI, according to which planning consists of a specific form of logical inference. Similar in this respect are other approaches to planning based on forms of representation such as graphs, Bayesian networks, Petri nets, and so on (see [1] for a review). In most cases, however, planning and control run concurrently, influencing each other on the same time scale. As a consequence, the problem arises of devising models of reasoning and kinds of representations that 34 IEEE Control Systems Magazine
doi:10.1109/37.915400 fatcat:dsfyq4epfnazzn4oc6vtkuu7uy