This thesis describes a formal analysis of nonmonotonic inheritance. The need for such an understanding of inheritance has been apparent from the time that multiple inheritance and exceptions were mixed in the same representation with the result that the meaning of an inheritance network was no longer clear. Many attempts to deal with the problems associated with nonmonotonic multiple inheritance appeared in the literature but, probably due to the lack of clear semantics there was no general agreement on how many of the standard examples should be handled. This thesis attempts to resolve these problems by presenting a framework for a family of path based inheritance reasoners which allows the consequences of design decisions to be explored. Many of the major theorems are therefore proved without the need to make any commitment as to how conflicts between nonmonotonic chains of reasoning are to be resolved. In particular it is shown that consistent sets of conclusions, known as expansions, exist for a wide class of networks. When commitment is made to a method of choosing between conflicting arguments, particular inheritance systems are produced. The systems described in this thesis can be divided into three classes. The simplest of these, in which an arbitrary choice is made between conflicting arguments, is shown to be very closely related to default logic. The other classes each of which contain four systems, are the decoupled and coupled inheritance systems which use specificity as a guide to choosing between conflicting arguments. In a decoupled system the results relating to a particular node are not affected in any way by derived results concerning other nodes in the inheritance network, whereas in a coupled system decisions in the face of ambiguity are linked to produce expansions which are more intuitively acceptable as a consistent view of the world. A number of results concerning the relationship between these systems are given. In particular it is shown that the process of coupling will not affect the results which lie in the intersection of the expansions produced for a given network.

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<p> Action selection for an autonomous agent was studied within the confines of truck task scheduling. An experimental setup was established to compare a naive selection approach, a simple market-based optimization approach, and a learning-assisted market-based optimization over a series of scenarios with varying complexity. For sufficiently complex scenarios, the results showed that learning was able to improve the performance of the truck by delaying delivery to a given site until it was the most protable action available. This research adds to the existing autonomous planning research by demonstrating a novel approach for planning under resource constraints. This approach improves upon an existing market-based optimization technique through the use of on-line reinforcement learning for market adjustment.</p>

Sequences of neural activity representing paths in an environment are expressed in the rodent hippocampus at three distinct time scales, with different hypothesized roles in hippocampal function. As an animal moves through an environment and passes through a series of place fields, place cells activate and deactivate in sequence, at the time scale of the animal’s movement (i.e., the behavioral time scale). Moreover, at each moment in time, as the animal’s location in the environment overlaps with the firing fields of many place cells, the active place cells fire in sequence during each cycle of the 4-12 Hz theta oscillation observed in the hippocampal local field potentials (i.e., the theta time scale), such that the neural activity, in general, represents a short path that begins slightly behind the animal and ends slightly ahead of the animal. These sequences have been hypothesized to play a role in the encoding and recall of episodes of behavior. Sequences of neural activity occurring at the third time scale are observed during both sleep and awake but restful states, when animals are paused and generally inattentive, and are associated with sharp wave ripple complexes (SWRs) observed in the hippocampal local field potentials. During the awake state, these sequences have been shown to begin near the animal’s location and extend forward (forward replay) or backward (backward replay), and have been hypothesized to play a role in memory consolidation, path planning, and reinforcement learning. This thesis uses a novel sequence detection method and a novel behavioral spatial decision task to study the functional significance of theta sequences and SWR sequences. The premise of the thesis is that by investigating the behavioral content represented by these sequences, we may further our understanding of how these sequences contribute to hippocampal function. The first part of the thesis presents an analysis of SWR sequences or replays, revealing several novel properties of these sequences. In particular it was found that instead of preferentially representing the more recently experienced parts of the maze, as might be expected for memory consolidation, paths that were not recently experienced were more likely to be replayed. Additionally, paths that were never experienced, including shortcut paths, were observed. These observations suggest that hippocampal replay may play a role in constructing and maintaining a "cognitive map" of the environment. The second part of the thesis investigates the properties of theta sequences. A recent study found that theta sequences extend further forward at choice points on a maze and suggested that these sequences may be partly under cognitive control. In this part of the thesis I present an analysis of theta sequences showing that there is diversity in theta sequences, with some sequences extending more forward and others beginning further backward. Furthermore, certain components of the environment are preferentially represented by theta sequences, suggesting that theta sequences may reflect the cognitive "chunking" of the animal’s environment. The third part of the thesis describes a computational model of the hippocampus which explores how synaptic learning due to neural activity during navigation (i.e., theta sequences) may enable the hippocampal network to produce forward, backward, and shortcut sequences during awake rest states (i.e., SWR sequences).