Doran, J.E.

Planning and robots


Another substantial body of work on general problem-solving is that associated with the Graph Traverser program (Doran and Michie 1966, Doran 1967, Michie 1967, Doran 1968, Michie, Fleming and Oldfield 1968, Michie and Ross 1970). In this section and the next we shall consider the transition from heuristic problem-solving as exemplified by the Graph Traverser, to planning by a robot as exemplified by my own work and that of Marsh (Doran 1967, 1967a, 1968a, 1969; Marsh 1970; Michie 1967, 1968a; Popplestone 1967). In order to do this efficiently the program uses, in general, a heuristic state evaluation function and heuristic operator selection techniques to grow the search tree in the most promising direction. The following types of learning occurred in the system: (a) learning of the relationship between acts and perceptions by noting the effects of individual acts, by making generalizations about the effects of acts, and by noting that certain complicated transitions from one perceived state to another can always be achieved, (b) learning which acts to employ in particular situations and the benefits to be expected -- a kind of habit formation.