Building expert systems: cognitive emulation.

Chapter 1 briefly introduces the concept of cognitive emulation, and outlines its current status. Chapter 2 reviews psychological research on human expert thinking. First, the study of expert thinking is placed in the context of modern cognitive psychology. Next, the principal methods and techniques employed by psychologists examining expert cognition are examined. The remainder of the chapter is given over to a review of the published literature on the nature and development of human expertise. Chapter 3 reviews the main arguments for and against cognitive emulation in expert system design. The tentative conclusion reached is that a significant degree of emulation is inevitable, but that a pure, unselective strategy of emulation is neither realistic nor desirable. Chapter 4 examines the prospects for cognitive emulation from a more pragmatic angle. Several factors are identified that represent constraints on the usefulness of a cognitive approach. However, a second set of factors is identified which should facilitate an emulation strategy - especially in the longer term. Some guidance is given on when to seriously consider adopting an emulation strategy. Chapter 5 presents a critical survey of expert system research that has already addressed the emulation issue. Six basic approaches to cognitive emulation are distinguished and evaluated. This helps draw out in more detail the implications of an emulation strategy for knowledge acquisition, knowledge representation and system architecture. The chapter concludes by discussing the issues that arise when different approaches to emulation are combined. Some guidance is offered on how this might be achieved. Chapter 6 summarizes the main themes and issues to have emerged, the design advice contained in the thesis, and the original contributions made by the thesis

Knowledge restructing and the development of expertise in computer programming

This thesis reports a number of empirical studies exploring the development of expertise in computer programming. Experiments 1 and 2 are concerned with the way in which the possession of design experience can influence the perception and use of cues to various program structures. Experiment 3 examines how violations to standard conventions for constructing programs can affect the comprehension of expert, intermediate and novice subjects. Experiment 4 looks at the differences in strategy that are exhibited by subjects of varying skill level when constructing programs in different languages. Experiment 5 takes these ideas further to examine the temporal distribution of different forms of strategy during a program generation task. Experiment 6 provides evidence for salient cognitive structures derived from reaction time and error data in the context of a recognition task. Experiments 7 and 8 are concerned with the role of working memory in program generation and suggest that one aspect of expertise in the programming domain involves the acquisition of strategies for utilising display-based information. The final chapter attempts to bring these experimental findings together in terms of a model of knowledge organisation that stresses the importance of knowledge restructuring processes in the development of expertise. This is contrasted with existing models which have tended to place emphasis upon schemata acquisition and generalisation as the fundamental modes of learning associated with skill development. The work reported here suggests that a fine-grained restructuring of individual schemata takes places during the later stages of skill development. It is argued that those mechanisms currently thought to be associated with the development of expertise may not fully account for the strategic changes and the types of error typically found in the transition between novice, intermediate and expert problem solvers. This work has a number of implications for existing theories of skill acquisition. In particular, it questions the ability of such theories to account for subtle changes in the various manifestations of skilled performance that are associated with increasing expertise. Secondly, the work reported in this thesis attempts to show how specific forms of training might give rise to the knowledge restructuring process that is proposed. Finally, the thesis stresses the important role of display-based problem solving in complex tasks such as programming and highlights the role of programming language notation as a mediating factor in the development and acquisition of problem solving strategies

A Comprehensive Computational Model of PRIMs Theory for Task-Independent Procedural Learning

The human ability to reason about and learn practically any task has been studied for countless years, but to date we still do not truly understand how human learning is task-independent at the computational level. Researchers have theorized that we can account for many human cognitive behaviors if we combine a task-independent set of primitive procedures with a robust, general learning mechanism that compiles them into cognitive skills for various tasks. The PRIMs theory of procedure learning and transfer is a cognitive architecture theory of human learning that shows how a task-independent set of primitive procedures can support learning in any task that is also supported by the underlying architecture. However, its published architecture implementation, Actransfer, focuses on modeling transfer and does not specify all of the computational details of PRIMs theory. This thesis presents a computationally comprehensive cognitive architecture model of PRIMs theory that I call the PROPs system. I comprehensively define each of the processing steps that PRIMs theory requires and implement these in an agent model using the Soar cognitive architecture. I do this through a methodology for incrementally refining a cognitive architecture model. I use this methodology to extend PRIMs theory and unify it with three-phase learning theory from human performance research, task set theory from psychology and neuroscience, and Soar theory from cognitive architecture research. This achieves several improvements in the model’s ability to replicate human learning behavior. Among the contributions of this work, I introduce a novel form of primitive processing that explains the origins of the primitive procedures of PRIMs theory and supports procedural learning in an unbounded, dynamic working memory space. I show that this improves the model’s ability to match human power-law learning. I also extend Soar cognitive architecture theory with gradual procedural learning in a manner consistent with Soar’s existing theory and introduce a novel computational approach by which a cognitive architecture model can learn to guide automatic long-term declarative memory retrievals based on working memory contents. I finally introduce a novel computational approach by which a model can guide deliberate retrievals through choice-based decision making. In my evaluation of the PROPs system, I identify ways in which PRIMs theory for procedural learning might be further unified with neuroscience theory to broaden the model to include declarative learning. I also identify boundaries where PRIMs models can or cannot currently account for types of human cognitive processing when the models are constrained to be fully task-independent and consistent with the surrounding cognitive architecture. This reveals a path for future cognitive architecture research and development.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169770/1/stearns_1.pd