Theories of categorization typically assume that categories are represented by some set of features that describe the properties of category members. However this view of category representation is incomplete. This dissertation lays out a framework for category representation, following Markman and Stilwell (2001), that creates a taxonomy of categories based on different components of relational structures. Relational categories are categories of entire relational systems while, role-governed categories, are represented as the roles in these systems. Lastly, thematic-relation categories group entities together that play complementary roles within a system. Four experiments are presented in support of this framework. They contrast thematic-relation categorization with role-governed categorization. Thematic-relation categorization entails categorizing objects together that play different roles within a domain, while role-governed categorization entails categorizing two entities that play the same role across domains. When the two are put in direct conflict, people prefer to form a thematic-relation category because within-domain connections are easier to find than across-domain connections. The purpose of the four experiments is to examine ways to boost the preference for role-governed categorization, thus revealing underlying processes. Here, role-governed categorization is facilitated in two ways. Experiment 1 re-frames the question of category formation as novel word extension. Natural role-governed categories have labels while thematic-relation categories do not. This pattern is reflected in the measured behavior as novel labels are extended across members of role-governed categories more readily than across members of thematic-relation categories. By claiming relational structures are critical to category representation, the framework described in this dissertation predicts that role-governed categorization and analogical reasoning share underlying mechanisms. Experiments 2-4 examine how making an analogy between the members of role-governed categories facilitates forming such categories. When making an analogy, people align the relational representations of a pair of domains, putting entities into correspondence by role, ignoring featural dissimilarities. When analogical comparison is induced, the rate of role-governed categorization is shown to double as compared to a baseline with no such analogical processes. The thesis concludes by outlining several future lines of research generated by unifying the fields of analogy and concept learning. / text

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This article provides an overview of recent developments relating to the application of thesauri in information organisation and retrieval on the World Wide Web. It describes some recent thesaurus projects undertaken to facilitate resource description and discovery and access to wide-ranging information resources on the Internet. Types of thesauri available on the Web, thesauri integrated in databases and information retrieval systems, and multiple-thesaurus systems for cross-database searching are also discussed. Collective efforts and events in addressing the standardisation and novel applications of thesauri are briefly reviewed.

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This thesis studies planning problems whose solution plans are program-like structures that contain branches and loops. Such problems are a generalization of classical and conditional planning, and usually involve infinitely many cases to be handled by a single plan. This form of planning is useful in a number of applications, but meanwhile challenging to analyze and solve. As a result, it is drawing increasing interest in the AI community. In this thesis, I will give a formal definition of planning with loops in the situation calculus framework, and propose a corresponding plan representation in the form of finite-state automata. It turns out that this definition is more general than a previous formalization that uses restricted programming structures for plans. For the verification of plans with loops, we study a property of planning problems called finite verifiability. Such problems have the property that for any candidate plan, only a finite number of cases need to be checked in order to conclude whether the plan is correct for all the infinitely many cases. I will identify several forms of finitely-verifiable classes of planning problems, including the so-called one-dimensional problems where an unknown and unbounded number of objects need independent processing. I will also show that this property is not universal, in that finite verifiability of less restricted problems would mean a solution to the Halting problem or an unresolved mathematical conjecture. For the generation of plans with loops, I will present a novel nondeterministic algorithm which essentially searches in the space of the AND/OR execution trees of an incremental partial plan on a finite set of example instances of the planning problem. Two different implementations of the algorithm are explored for search efficiency, namely, heuristic search and randomized search with restarts. In both cases, I will show that the resulting planner generates compact plans for a dozen benchmark problems, some of which are not solved by other existing approaches, to the best of our knowledge. Finally, I will present generalizations and applications of the framework proposed in this thesis that enables the analysis and solution of related planning problems recently proposed in the literature, namely, controller synthesis, service composition and planning programs. Notably, the latter two require possiblynon-terminating execution in a dynamic environment to provide services to coming requests. I will show a generic definition and planner whose instantiation accommodates and solves all the three example applications. Interestingly, the instantiations are competitive with, and sometimes even outperform, the original tailored approaches proposed in the literature.

This thesis studies planning problems whose solution plans are program-like structures that contain branches and loops. Such problems are a generalization of classical and conditional planning, and usually involve infinitely many cases to be handled by a single plan. This form of planning is useful in a number of applications, but meanwhile challenging to analyze and solve. As a result, it is drawing increasing interest in the AI community. In this thesis, I will give a formal definition of planning with loops in the situation calculus framework, and propose a corresponding plan representation in the form of finite-state automata. It turns out that this definition is more general than a previous formalization that uses restricted programming structures for plans. For the verification of plans with loops, we study a property of planning problems called finite verifiability. Such problems have the property that for any candidate plan, only a finite number of cases need to be checked in order to conclude whether the plan is correct for all the infinitely many cases. I will identify several forms of finitely-verifiable classes of planning problems, including the so-called one-dimensional problems where an unknown and unbounded number of objects need independent processing. I will also show that this property is not universal, in that finite verifiability of less restricted problems would mean a solution to the Halting problem or an unresolved mathematical conjecture. For the generation of plans with loops, I will present a novel nondeterministic algorithm which essentially searches in the space of the AND/OR execution trees of an incremental partial plan on a finite set of example instances of the planning problem. Two different implementations of the algorithm are explored for search efficiency, namely, heuristic search and randomized search with restarts. In both cases, I will show that the resulting planner generates compact plans for a dozen benchmark problems, some of which are not solved by other existing approaches, to the best of our knowledge. Finally, I will present generalizations and applications of the framework proposed in this thesis that enables the analysis and solution of related planning problems recently proposed in the literature, namely, controller synthesis, service composition and planning programs. Notably, the latter two require possiblynon-terminating execution in a dynamic environment to provide services to coming requests. I will show a generic definition and planner whose instantiation accommodates and solves all the three example applications. Interestingly, the instantiations are competitive with, and sometimes even outperform, the original tailored approaches proposed in the literature.

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