Learning Racquetball by Constrained Example Generation

A model of learning in a highly active and competitive environment is presented here. This paper posits that learning, under these conditions, can be characterized as accommodating examples produced by the Constrained Example Generation (CEG) process. The role of the"CEG process in the LRCEG system is demonstrated by a typical learning scenario. I INTRODUCTION This paper Introduces an emerging system designed to learn racquetball by a process of constrained example generation (LRCEG). Having the capacity to generate a set of constrained examples has been shown to be essential in many domains [1]. [2]. Furthermore, learning as a form of accommodating and assimilating knowledge has been demonstrated to be useful to a number of systems [6]. .The implication here is that some forms of learning can be modeled, in part, as a process of retrieval, modification, or construction of domain specific knowledge, represented as examples C33.C53. Exploring a variety of learnirg issues within the RB domain is made possible by the generality and robustness of the CEG paradigm [4], Racquetball (RB) as a learning domain has some advantages. The speed and competitive nature of the aport facilitates investigating learning under rapidly changing and strenous conditions. Many important occupations require learning the appropriate behavior for unpredictable, fast-paced, and demanding environments (e.g., medicine, athletics, aircraft piloting). The learning required to perform tasks in these domains can be very different (although not mutually exclusive) from a typical classroom education. The generality and robustness of the CEG paradigm allows exploring a variety of learning-RB issues [4]. Some knowledge about RB must be obtained and represented before the LRCEG system can attempt learning. Knowledge for the initial set of six examples was extracted from interviews of a RB expert. The syntax of the expert's information was "IF A PLAYER ENCOUNTERS object-of-encounters THEN TRY AND MEET object-of-meet AND RESPOND object-of-respond.". The context within which learning occurs is the 'object-of-encounters', and the appropriate behavior for meeting and hitting the ball is 'object-of-meet' and'object-of-respond' respectively. An example of the expert's advice is "IF A PLAYER ENCOUNTERS a fast cross-court shot THEN TRY AND MEET the ball waist high AND RESPOND with a ceiling shot." Both the context and behavior knowledge is translated into velocity and RB court X,Y,2 coordinates (i.e., 'coordinate-velocity' information). This knowledge is represented in a frame-like data structure [Fig 1a,b3. (CONTEXT (SEMANTICS (X dX) (Y dY) (Z dZ) (V dV)) (OBJECTIVE objective-function-definition) (SITUATION situation-function-definition) (RESPONSE response-info-4-furction-definition)) …