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This thesis examines a novel induction-based framework for logic programming. Limiting programs are logic programs distinguished by two features, in general they contain an infinite data stream over which induction will be performed, and in general it is not possible for a system to know when a solution for any program is correct. These facts are characteristic of some problems involving induction in artificial intelligence, and several problems in knowledge representation and logic programming have exactly these properties. This thesis presents a specification language for problems with an inductive nature, limiting programs, and a resolution based system, limiting resolution, for solving these problems. This framework has properties which guarantee that the system will converge upon a particular answer in the limit. Solutions to problems which have such an inductive property by nature can be implemented using the language, and solved with the solver. For instance, many classification problems are inductive by nature. Some generalized planning problems also have the inductive property. For a class of generalized planning problems, we show that identifying a collection of domains where a plan reaches a goal is equivalent to producing a plan. This thesis gives examples of both. Limiting resolution works by a generate-and-test strategy, creating a potential solution and iteratively looking for a contradiction with the growing stream of data provided. Limiting resolution can be implemented by modifying conventional PROLOG technology. The generateand- test strategy has some inherent inefficiencies. Two improvements have arisen from this work; the first is a tabling strategy which records previously failed attempts to produce a solution and thereby avoids redundant test steps. The second is based on the heuristic observation that for some problems the size of the test step is proportional to the closeness of the generated potential-solution to the real solution, in a suitable metric. The observation can be used to improve the performance of limiting resolution. Thus this thesis describes, from theoretical foundations to implementation, a coherent methodology for incorporating induction into existing general A.I. programming techniques, along with examples of how to perform such tasks.

Emotional Intelligence ( EI ) is held to explain how emotions may function to advance life goals, with the basic proposition being that individuals monitor and discriminate emotions within themselves and others to solve problems. A number of different theories of EI have been proposed and consequently there is still controversy about the best way in which to conceptualise and measure EI. It is, nonetheless, agreed that the relevance of EI is largely dependent on it being able to predict significant life outcomes. Academic achievement, life satisfaction, coping, problem - solving ability and anxiety are variables that have relevance in academic, occupational and interpersonal situations. The relationship between these variables and EI was assessed in two diverse populations ( University sample : N = 246 ; mean age = 19.9 ; Older community sample : N = 212 ; mean age = 51.6 ). The magnitude and direction of findings in both studies were found to be remarkably similar. As expected, older adults ( community sample ) were found to score significantly higher on EI than younger adults ( University sample ). Few gender differences in EI, however, were apparent, but those that were significantly favoured females. Previously identified relationships suggesting that self - report EI measures are moderately - to - highly correlated to personality, whereas ability EI is reasonably distinct from other constructs, were also upheld. Inconsistent with previous research, however, differential associations between EI and verbal and abstract reasoning ability were not observed. Fitting theoretical expectations, in both studies EI was low - to - moderately correlated with higher life satisfaction, problem and emotion focused coping and perceived problem solving ability and with lower avoidance coping and anxiety. However, the correlations for academic achievement were not significant. These correlations were found to be higher for self - report EI than they were ability EI, perhaps due to method variance with the life skills. Nevertheless, despite these low - to - moderate correlations, hierarchical regression analyses controlling for personality and cognitive abilities revealed that the incremental predictive validity of EI was 7 % at most. This finding was found for all life skills regardless of the EI measure involved. This raises some implications for the field of EI and highlights that personality and cognitive abilities should be taken into account when making assertions about EI ' s predictive power. / Thesis (Ph.D.) -- University of Adelaide, School of Psychology, 2006.

A resurgence of interest in ontology emerged in the 1990s from the field of information systems engineering. From beginnings such as the Cyc project to codify commonsense knowledge and the Stanford Knowledge Sharing Laboratory efforts to build a shareable ontology of terms, emerged a multitude of ontologies, academic contributions, conferences and commercial companies. / However, does "applied ontology", as a joint field between information systems engineering and philosophy, actually exist? A field that equally informs both engineering and philosophical ontology has obstacles to overcome. For example, according to Grüber's (1993) ubiquitous definition, engineering ontology is a "specification of a conceptualization", whereas in philosophy an ontology is "a systematic account of Existence" - a significant difference. Furthermore, there are philosophical objections to ontology that may undermine its practical application. In this dissertation, we aim to overcome these obstacles by approaching engineering requirements analysis through a particularist metaphysics. More specifically, we argue that engineering 'requirements analysis' can be approached through the ontological question "what exists when the requirements are satisfied?" This approach to requirements analysis forms the core of a joint engineering and philosophical ontology. / The argument obligates us to demonstrate an example of the ontological approach to requirements analysis. We select 'Competitive Intelligence' (CI) as a commercial practice where engineering requirements lend themselves to ontological analysis. A working definition for CI emerges as being "the integration of piecemeal information to support organisational strategy". The major part of the dissertation is a formal analysis (using logic programs) that demonstrates a modified version of this definition can be coherently expressed and used to show the existence of CI as such. The logic also shows CI, as defined, supervenes on other information systems, and depends mainly on a strategic framework. / As such, for the research at hand, the analysis suffices as foundation of an ontology of CI, demonstrates the use of ontology as a requirements analysis approach, and develops a practice of applied ontology that equally informs engineering and philosophy.

<p>Initially we pursue a minimal model of a cognitive system. This in turn form the basis for the development of amethodological and theoretical framework. Two methodological requirements of the model are that explanation be from the perspective of the phenomena, and that we have structural determination. The minimal model is derived from the explanatory side of a biologically based cognitive science. Fransisco Varela is our principal source for this part. The model defines the relationship between a formally defined autonomous system and an environment, in such a way as to generate the world of the system, its actual environment. The minimal model is a modular explanation in that we find it on different levels in bio-cognitive systems, from the cell to small social groups. For the latter and for the role played by artefactual systems we bring in Edwin Hutchins' observational study of a cognitive system in action. This necessitates the introduction of a complementary form of explanation. A key aspect of Hutchins' findings is the social domain as environment for humans. Aspects of human cognitive abilities usually attributed to the person are more properly attributed to the social system, including artefactual systems.</p><p>Developing the methodological and theoretical framework means making a transition from the bio-cognitive to the computational. The two complementary forms of explanation are important for the ability to develop a methodology that supports the construction of actual systems. This has to be able to handle the transition from external determination of a system in design to internal determination (autonomy) in operation.</p><p>Once developed, the combined framework is evaluated in an application area. This is done by comparing the standard conception of the Semantic Web with how this notion looks from the perspective of the framework. This includes the development of the methodological framework as a metalevel external knowledge representation. A key difference between the two approaches is the directness by which the semantic is approached. Our perspective puts the focus on interaction and the structural regularities this engenders in the external representation. Regularities which in turn form the basis for machine processing. In this regard we see the relationship between representation and inference as analogous to the relationship between environment and system. Accordingly we have the social domain as environment for artefactual agents. For human level cognitive abilities the social domain as environment is important. We argue that a reasonable shortcut to systems we can relate to, about that very domain, is for artefactual agents to have an external representation of the social domain as environment.</p>

Since Robotics is the field concerned with the connection of perception to action, Artificial Intelligence must have a central role in Robotics if the connection is to be intelligent. Artificial Intelligence addresses the crucial questions of: what knowledge is required in any aspect of thinking; how that knowledge should be represented; and how that knowledge should be used. Robotics challenges AI by forcing it to deal with real objects in the real world. Techniques and representations developed for purely cognitive problems, often in toy domains, do not necessarily extend to meet the challenge. Robots combine mechanical effectors, sensors, and computers. AI has made significant contributions to each component. We review AI contributions to perception and object oriented reasoning. Object-oriented reasoning includes reasoning about space, path-planning, uncertainty, and compliance. We conclude with three examples that illustrate the kinds of reasoning or problem solving abilities we would like to endow robots with and that we believe are worthy goals of both Robotics and Artificial Intelligence, being within reach of both.