Masaki Suwa

What architects see in their sketches: implications for design tools

Freehand sketches are essential for crystallizing ideas in the early stages of design. Through the act of putting ideas down on paper and inspecting them, designers see new relations and features that suggest ways to refine and revise their ideas. We claim that seeing different types of information in sketches is the driving force in revising design ideas. Our retrospective protocol analysis revealed that sketches make apparent to designers not only perceptual features but also inherently non-visual functional relations, allowing them to extract function from perception in sketches. This has implications for ways that future sketching tools can stimulate designers to come up with creative ideas.

Acquisition of associative knowledge by the frustration-based learning method in an auxiliary-line problem

We have developed a learner, AUXIL, which has the ability to solve auxiliary-line problems in geometry in an intelligent way. First, we show that a basic mechanism for producing auxiliary-lines is to associate a certain condition or subgoal in the problem with an appropriate figure-pattern and that AUXIL can produce a successful auxiliary-line by making use of associative strategies, which we call figure-pattern strategies . Secondly, we proposed a new method, frustration-based learning , which can acquire associative strategies through experiences of solving a variety of auxiliary-line problems. AUXIL simulates the following expert behavior. When an expert tries to solve such a problem, he feels frustrated because enough information is not given in a problem space for him to proceed an inference and to find a correct path from given conditions to the goal. Here, he concentrates himself on the conditions or subgoals which have caused frustration. After he has produced an auxiliary-line and made a complete proof-tree, he would learn several associative strategies. Each frustration-causing condition or subgoal will constitute the if-part of each strategy. He will then recognize several lumps of figure-patterns in the proof-tree, each of which has contributed to resolving each frustration. All pieces of geometrical information of each figure-pattern will constitute the then-part of each strategy. Learning an auxiliary-line problem through frustration-based learning means to understand it as a composition of figure-patterns each of which has those features represented in the THEN-part of the corresponding strategy. The frustration-based learning method is regarded as a method for learning some essential figure-patterns which underlie and structurize a problem solving process of elementary geometry.

PCLEARN: A Computer Model for Learning Perceptual Chunks

Acquiring search control knowledge of high utility is essential to reasoners in speeding up their problem-solving performance. In the domain of geometry problem-solving, the role of “perceptual chunks”, an assembly of diagram elements many problems share in common, in effectively guiding problem-solving search has been extensively studied, but the issue of learning these chunks from experiences has not been addressed so far. Although the explanation-based learning technique is a typical learner for search control knowledge, the goal-orientedness of its chunking criterion leads to produce such search control knowledge that can only be used for directly accomplishing a target-concept, which is totally different from what perceptual-chunks are for. This paper addresses the issues of acquiring domain-specific perceptual-chunks and demonstrating the utility of acquired chunks. The proposed technique is that the learner acquires, for each control decision node in the problem-solving traces, a chunk which is an assembly of diagram elements that can be visually recognizable and grouped together with the control decision node. Recognition rules implement this chunking criterion in the learning system PCLEARN. We show the feasibility of the proposed technique by investigating the cost-effective utility of the learned perceptual chunks in the geometry domain, and also discuss the potential for the technique being applied to other domains.

A Perceptual Criterion for Visually Controlling Learning

Acquiring search control knowledge of high utility is essential to reasoners in speeding up their problem-solving performance. In the domain of geometry problem-solving, the role of “perceptual chunks”, an assembly of diagram elements many problems share in common, in effectively guiding problem-solving search has been extensively studied, but the issue of learning these chunks from experiences has not been addressed so far. Although the explanation-based learning technique is a typical learner for search control knowledge, the goal-orientedness of its chunking criterion leads to produce such search control knowledge that can only be used for directly accomplishing a target-concept, which is totally different from what perceptual-chunks are for. This paper addresses the issues of acquiring domain-specific perceptual-chunks and demonstrating the utility of acquired chunks. The proposed technique is that the learner acquires, for each control decision node in the problemsolving traces, a chunk which is an assembly of diagram elements that can be visually recognizable and grouped together with the control decision node. Recognition rules implement this chunking criterion in the learning system PCLEARN. We show the feasibility of the proposed technique by investigating the applicability and cost-effective utility of the learned perceptual chunks in the geometry domain.