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<p class="Abstract">The work proposed in this thesis continues the research into qualitative model learning (QML), a branch of qualitative reasoning.&nbsp; After the investigation of all existing qualitative model learning systems, especially the state-of-the-art system ILP-QSI, a novel system named QML-Morven is presented. <p class="Abstract">QML-Morven inherits many essential features of the existing QML systems: it can learn models from positive only data, make use of the well-posed model constraints, process hidden variables, learn models from incomplete data, and perform systematic experiments to verify the hypotheses being made by researchers. <p class="Abstract">The development of QML-Morven allows us to further investigate some interesting yet unsolved questions in the QML research.&nbsp; As a result, four significant hypotheses are tested and validated by performing a series of systematic experiments with QML-Morven:&nbsp; 1. The information of state variables and the number of hidden variables are two important actors that can influence the learning, and the different combination of these two factors may give a different learning result in terms of the kernel subset (minimal data for a successful learning) and learning precision; 2. The scalability of QML may be improved by the use of an evolutionary algorithm; 3. For some models, the kernel subsets can be constructed by combining several sets of qualitative states, and the states in a kernel subset tend to scatter over the solution space; 4. The integration of domain-specific knowledge makes QML more applicable for learning the qualitative models of the real-world dynamic systems of high complexity. <p class="Abstract">The results and analysis of these experiments with respect to QML-Morven also raise many questions and indicates several new research directions.&nbsp; In the final part of this thesis, several possible future directions are explored.

<p> We propose to examine a representation which features combined action and perception signals, i.e., instead of having a purely geometric representation of the perceptual data, we include the motor actions, e.g., aiming a camera at an object, which are also actions that generate the particular shape. This generative perception-action representation uses Leyton&rsquo;s cognitive representation based on wreath products. The wreath product is a special kind of group which captures information through symmetries on the sensorimotor data. The key insight is the bundling of actuation and perception data together in order to capture the cognitive structure of interactions with the world. This involves developing algorithms and methods: (1) to perform symmetry detection and parsing, (2) to represent and characterize uncertainties in the data and representations, and (3) to provide an overall cognitive architecture for a robot agent. We demonstrate these functions in 2D text classification, as well as on 3D data, on a real robot operating according to a well-defined experimental protocol for benchmarking indoor navigation, along with capabilities for multirobot communication and knowledge sharing. A cognitive architecture called the <i>Wreath Product Cognitive Architecture</i> is developed to support this approach.</p>

The modern trend in electricity industries around the world is towards privatisation. Increased competition, it is argued, will ultimately benefit the consumer. However, the particular nature of electricity generation and supply means strong regulation of a privatised market will always be necessary. In establishing a privatised industry, decisions need to be made about the mechanisms governing the requirements to meet demand, to maintain the viability of the network and to ensure generators are paid correctly for power generated. Unfortunately, it is unclear what processes to use to achieve these goals while still delivering some benefit to the consumer in the form of reduced electricity costs. This research, sponsored by the National Grid Company, examines whether the application of new ideas in artificial intelligence could offer the potential for gaining insights into the affects of certain market mechanisms on the competitors in the market. Our approach to gaining greater understanding into how the market operates is to adopt an evolutionary economics perspective. We have constructed autonomous adaptive agents to represent the generating companies in a simplified model of the UK market in electricity generation. The main body of the thesis contains a description of the process of developing the model and the agent architecture. Once we were satisfied that the model incorporated some key features of the real world market and that the agents, based on learning classifier systems, were able to perform well in simpler environments, we examined how multiple adaptive agents learn to interact in the simplified model. We conclude that the agents are able to learn how to behave in ways analogous to the observed behaviour of real world generating companies. We then illustrate the potential for this type of economic model by examining how alterations to market structure affect agent behaviour, and investigate to what extent the agents are able to learn how to cooperate for mutual long term benefit.

<p> While robotics has made considerable strides toward more robust and adaptive manipulation, perception, and planning, robots in the near future are unlikely to be as dexterous, competent, and versatile as human workers. Rather than try to create fully autonomous systems that accomplish tasks independently, a more practical approach is to construct robots that work alongside people. This allows human and robot workers to concentrate on the tasks for which they are each best suited, while simultaneously providing the capability to assist each other during tasks that one worker lacks the ability to complete independently in a safe or maximally proficient manner. Human-robot teaming advances have the potential to extend applications of autonomous robots well beyond their current, limited roles in factory automation settings. Much of modern robotics remains inapplicable in many domains where tasks are either too complex, beyond modern hardware limitations, too sensitive for non-human completion, or too flexible for static automation practices. In these situations human-robot teaming can be leveraged to improve the efficiency, quality-of-life, and safety of human partners.</p><p> In this thesis, I describe algorithms that can create collaborative robots that call provide assistance when useful, remove dull or undesirable responsibilities when possible, and assist with dangerous tasks when feasible. In doing so, I present a novel method for autonomously constructing hierarchical task networks that factor complex tasks in was that make theism approachable by modern planning and coordination algorithms. In particular, within these complex cooperative tasks I focus on facilitating collaboration between a lead worker and robotic assistant within a shared space, defining and investigating a class of actions I term supportive behaviors: actions that serve to reduce the cognitive or kinematic complexity of tasks for teammates. The majority of contributions within this work center around discovering, learning, and executing these types of behaviors in multi-agent domains with asymmetric authority. I provide an examination of supportive behavior learning and execution from the perspective of task and motion planning, as well as that of learning directly from interactions with humans. These algorithms provide a collaborative robot with the capability to anticipate the needs of a human teammate and proactively offer help as needed or desired. This work enables to creation of robots that provide tools just-in-time, robots that alter workspaces to make more optimal task orderings more obvious and more feasible, and robots that recognize when a user is delayed in a complex task and offer assistance.</p><p> Combining these algorithms provides a basis for a robot with both a capacity for rich task comprehension and a theory of mind about its collaborators, enabling methods to allow such a robot to leverage knowledge it acquires to transition between the role of learner, able assistant, and informative instructor during interactions with teammates.</p>

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<p> The U.S. electricity industry is being restructured to increase competition. Although existing policies may lead to efficient wholesale institutions, designing policies for the retail level is more complex because of intricate interactions between individuals and quasi-monopolistic institutions. It is argued that Hirshman's ideas of "exit" and "voice" (Hirshman, 1970) provide powerful abstractions for design of retail institutions. While competition is a known mechanism of "exit," a novel design of the "voice" mechanism is demonstrated through an artificial intelligence (AI) based software process model. The process model of "voice" in retail institutions is designed within the economic context of electricity distribution &mdash; a common property resource (CPR), characterized by technological uncertainty and path-dependency. First, it is argued that participant feedback (voice) has to be used effectively to manage the CPR. Further, it is noted that the decision process, of using participant feedback (voice) to incrementally manage uncertainty and path-dependencies, is non-monotonic because it requires the decision makers to often retract previously made assumptions and decisions. An AI based process model of "voice" is developed using an assumption-based truth maintenance system. The model can emulate the non-monotonic decision making process and therefore assist in decision support. Such a systematic framework is flexible, consistent, and easily reorganized as assumptions change. It can provide an effective, formal "voice" mechanism to the retail customers and improve institutional performance.</p>

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