Learning perceptual schemas to avoid the utility problem

This paper describes principles for representing and organising planning knowledge in a machine learning architecture. One of the difficulties with learning about tasks requiring planning is the utility problem: as more knowledge is acquired by the learner, the utilisation of that knowledge takes on a complexity which overwhelms the mechanisms of the original task. This problem does not, however, occur with human learners: on the contrary, it is usually the case that, the more knowledgeable the learner, the greater the efficiency and accuracy in locating a solution. The reason for this lies in the types of knowledge acquired by the human learner and its organisation. We describe the basic representations which underlie the superior abilities of human experts, and describe algorithms for using equivalent representations in a machine learning architecture

What forms the chunks in a subject's performance? Lessons from the CHREST computational model of learning

Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk

CHREST+: A simulation of how humans learn to solve problems using diagrams.

This paper describes the underlying principles of a computer model, CHREST+, which learns to solve problems using diagrammatic representations. Although earlier work has determined that experts store domain-specific information within schemata, no substantive model has been proposed for learning such representations. We describe the different strategies used by subjects in constructing a diagrammatic representation of an electric circuit known as an AVOW diagram, and explain how these strategies fit a theory for the learnt representations. Then we describe CHREST+, an extended version of an established model of human perceptual memory. The extension enables the model to relate information learnt about circuits with that about their associated AVOW diagrams, and use this information as a schema to improve its efficiency at problem solving

Learning-based constraints on schemata

Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models

The CHREST model of active perception and its role in problem solving

We discuss the relation of TEC to a computational model of expert perception, CHREST, based on the chunking theory. TEC’s status as a verbal theory leaves several questions unanswerable, such as the precise nature of internal representations used, or the degree of learning required to obtain a particular level of competence: CHREST may help answer such questions

Learning perceptual chunks for problem decomposition

How students learn to use diagrammatic representations is an important topic in the design of effective representations for problem solving or conceptual learning, but few good models of their learning exist. In this paper, we explore the learning process with an experiment using AVOW diagrams as a representation for solving problems in electric circuits. We find that the participants decompose each circuit into a similar set of groups when solving the problems. The natural question is whether these groups are an artifact of the visual form of the circuit, or indeed the result of prior learning. We argue that the decompositions are a result of perceptual chunking, and that they are (at least partly) a result of learning. In support of this, we describe a computational model of perceptual learning, CHREST+, and show that it predicts the decomposition of problems evident in the participants' data

An investigation into the cognitive, metacognitive, and spatial markers of creativity and efficiency in architectural design

This paper presents a preliminary study into the spatial features that can be used to distinguish creativity andefficiency in design layouts, and the distinct pattern of cognitive and metacognitive activity that is associated with creative design. In a design experiment, a group of 12 architects were handed a design brief. Their drawing activity was recorded and they were required to externalize their thoughts during the design process. Both design solutions and verbal comments were analysed and modelled. A separate group of experienced architects used their expert knowledge to assign creativity and efficiency scores to the 12 design solutions. The design solutions were evaluated spatially. Protocol analysis studies including linkography and macroscopic analysis were used to discern distinctive patterns in the cognitive and metacognition activity of designs marked with the highest and least creativity scores. Entropy models of the linkographs and knowledge graphs were further introduced Finally, we assessed how creativity and efficiency correlates to experiment variables, cognitive activity, metacognitive activity, spatial and functional distribution of spaces in the design solutions, and the number and type of design constraints applied through the course of design. Through this investigation, we suggest that expert knowledge can be used to assess creativity and efficiency in designs. Our findings indicate that efficient layouts have distinct spatial features, and that cognitive and metacognitive activity in design that yields a highly creative outcome corresponds to higher frequencies of design moves and higher linkages between design moves

Considerations in Representation Selection for Problem Solving: A Review

Choosing how to represent knowledge effectively is a long-standing open problem. Cognitive science has shed light on the taxonomisation of representational systems from the perspective of cognitive processes, but a similar analysis is absent from the perspective of problem solving, where the representations are employed. In this paper we review how representation choices are made for solving problems in the context of theorem proving from three perspectives: cognition, heterogeneity, and computational demands. We contrast the different factors that are most important for each perspective in the context of problem solving to produce a list of considerations for developers of problem solving tools regarding representations that are appropriate for particular users and effective for specific problem domains

How to (Re)represent it?

Choosing an effective representation is fundamental to the ability of the representation’s user to exploit it for the intended purpose. The major contribution of this paper is to provide a novel, flexible framework, rep2rep, that can be used by AI systems to recommend effective representations. What makes an effective representation is determined by whether it expresses the necessary information, supports the execution of tasks, and reflects the user’s cognitive abilities. In general, there is no single ‘most effective’ representation for every problem and every user, which makes it difficult to choose one from the plethora of possible representations. To address this, rep2rep includes: a domain-independent language for describing representations, algorithms that compute measures of informational suitability and cognitive cost, and uses these measures to recommend representations. We demonstrate the application of rep2rep in the probability domain. Importantly, our framework provides the foundations for personalised interaction with AI systems in the context of representation choice

Correspondence-based analogies for choosing problem representations

Mathematics and computing students learn new concepts and fortify their expertise by solving problems. The representation of a problem, be it through algebra, diagrams, or code, is key to understanding and solving it. Multiple-representation interactive environments are a promising approach, but the task of choosing an appropriate representation is largely placed on the user. We propose a new method to recommend representations based on correspondences: conceptual links between domains. Correspondences can be used to analyse, identify, and construct analogies even when the analogical target is unknown. This paper explains how correspondences build on probability theory and Gentner's structure-mapping framework; proposes rules for semi-automated correspondence discovery; and describes how correspondences can explain and construct analogies