KDI: Learning, Adaptation and Layered Intelligent Systems

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9873451MitterOur approach to building intelligent systems is based on the theoretical ideas of Bayesian statistical signal analysis and control-theoretic motion guidance. These theories have achieved major successes in many widely used systems. However, it has remained a challenge to create systems that integrate the analysis of low-level concrete sensor data and motor control with the use of high-level, more abstract representations of perception and action. The investigators on our team propose to attack this central issue with a variety of approaches. We believe that a very promising tool is the systematic study of hierarchical compositional data structures, in which larger scale more abstract objects are built up in stages from small-scale concrete objects. This compositional approach applies to making decisions as well as to perception: subtasks may be composed into larger tasks and reasoned with as units. Doing reasoning in such hybrid systems, it is essential that ambiguity and multiple alternatives be maintained as long as possible. For example, this means not choosing between inconsistent high-level interpretations of a signal until larger scale context is available; or replacing a local 'greedy algorithm' decision by a dynamic programming-style list of optimal conditional decisions. We discuss the role of learning in such hierarchical layered systems. Understanding, the interaction between information and control in such layered systems is a critical research issueWe plan to concentrate our research on the following aspects of layered intelligent systems:(i) the role of the compositional and hierarchical approach in language and speech recognition; (ii) hierarchical decision making in layered systems; (iii) learning of hierarchical concepts; and (iv) interaction information and control in distributed systems. We emphasize that these topics are highly inter-related. ***