Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain

A framework for empirical discovery

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws

A cognitive architecture for learning in reactive environments

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws

Discovering qualitative empirical laws

In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Language acquisition and machine learning

In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition

Theory formation by abduction : initial results of a case study based on the chemical revolution

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" — dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction

Chemical discovery as belief revision

In this paper we describe STAHLp, a system that constructs componential models of chemical substances. STAHLp is a descendent of Zytkow and Simon's (1986) STAHL system, and both use chemical reactions and known componential models in order to construct new chemical models. However, STAHLp employs a more unified and effective strategy for recovering from erroneous inferences, based partly on de Kleer's (1984) assumption-based method of belief revision. This involves recording the underlying source beliefs or premises which lead to each inferred reaction or model. Where Zytkow and Simon's system required multiple methods for detecting errors and recovering from them, STAHLp uses a more powerful representation and additional rules which allow a unified method for error detection and recovery. When given the same initial data, the new system constructs the same historically correct models as STAHL, but it has other capabilities as well. In particular, STAHLp can modify data it has been given if this is necessary to achieve consistent models, and then proceed to construct new models based on the revised data

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research