Abstract
Robotics research has produced economically and organizationally satisfactory tools for industry, and exploration of and manipulation in outer space, under the ocean and other dangerous or difficult-to-access places. Intelligent robots, however, are still largely a promising possiblity around the horizon. The adaptation of Artificial Intelligence methodology for robots seems to be a difficult and lengthy process. Both general-purpose and domain-specific techniques are needed. In this paper, we investigate some fairly universal concepts within the block world context.
Analogical reasoning (AR) has long been recognized as an important component of problem solving. In general, AR involves applying the (possibly modified) solution of one problem to a second problem which is in some sense analogous to the first. The prerequisite the two problems have to satisfy is that they have the necessary number and type of important features in common. The task is to discover automatically what the important features are. We discuss at length some general ideas, two basic models and a few advanced processes relating to AR.
Our program generates specific solutions to a number of similar problems that share several properties. The problems are to build certain three-dimensional bodies which satisfy a number of geometrical requirements and constraints. Problem situations are then generalized in the manner of concept formation. Those problems that have similar solutions are replaced with a single concept -- the type definition of a class of problems. Our program, itself, identifies new (hidden or “chunked”) properties it has determined to be essential.
Frames are used to describe problem situations. Four conceptual levels of frames are distinguished: (i) The situation level contains slots for situation properties, the types of available objects, the goal and the eventual solution. (ii) The object level has slots for specific object properties and for lists of possible components that can make up the object. (iii) An unlimited number of component levels look like the object level and represent the components of components…of the objects. (iv) Finally, the property level can contain properties of situations, objects or components.
The underlying learning is a three-stage process. In the first, shapinv stage, heuristic search techniques are used to find a solution to a particular problem. The resulting plan is an action sequence which is then associated with the problem situation. In the second, AR stage, problems with similar action sequences are grouped under a single situation class. A class definition is established which is sufficient to distinguish its members from all other situations. Rules are generated which connect the situation classes and action sequences to be performed in them. The final, consolidation stage compiles the rules into a decision graph. The variables determining the situation class are re-ordered on the decision graph so that the action plans can be retrieved the most efficiently.
“...no such thing as a false analogy exits: An analogy can be more or less
detailed and hence more or less informative.“ (Nobel Prize lecture by K.Z. Lorenz, 1973)
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Findler, N.V., Ihrig, L.H. (1987). Analogical Reasoning by Intelligent Robots. In: Wong, A.K.C., Pugh, A. (eds) Machine Intelligence and Knowledge Engineering for Robotic Applications. NATO ASI Series, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-87387-4_10
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