We give a concise introduction to the coalgebraic theory of Moore machines, and building on [6], develop a method for constructing a final Moore machine based on a simple modal logic. Completeness for the logic follows easily from the finality construction, and we furthermore show how this logical framework can be used for machine learning.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references (leaves 297-310). Available also in an electronic version.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis--Wisconsin. / Vita. Includes bibliographical references (leaves 217-219).

Thesis (M.S.)--University of Wisconsin--Madison, 1968. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S.)--Montana State University--Bozeman, 2007. / Typescript. Chairperson, Graduate Committee: Rockford Ross. Includes bibliographical references (leaf 51).

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains ii, 23 p. : ill. Includes abstract. Includes bibliographical references (p. 22-23).

Turing Machines and Markov Algorithms are, and were designed to be, the most powerful devices possible in the field of abstract automata: by their means any and every computable function can be computed. Because of their equal, indeed maximal, strength, it was naturally assumed that these devices should be equivalent. Nonetheless a formal, exact proof of this universally presumed equivalence was lacking. The present dissertation rectifies that omission by developing the desired complete, rigorous proof of the equivalence between Turing Machines and Markov Algorithms. The demonstration is being conducted in a constructionist way: for any given Markov Algorithm it is shown that a Turing Machine can be constructed capable of performing exactly what the Algorithm can do and nothing more, and vice versa. The proof consists in the theoretical construction, given an arbitrary Markov Algorithm, of a Turing Machine behaving in exactly the same way as the Algorithm for all possible inputs; and conversely. Furthermore, the proof is given concrete shape by designing a computer program which can actually carry out the said theoretical constructions. The equivalence between TM and MA as proven in the first part of our thesis, is being used in the second part for establishing some important consequent results: Thus the equivalence of Deterministic and Nondeterministic MA, of TM and Type 0 Grammars, and of Labelled and Unlabelled MA is concisely shown, and the use of TM as recognizers for type 1 and 3 grammars exclusively is exhibited. It is interesting that, by utilizing the equivalence of TM and MA, it was made possible that the proofs of these latter results be based on primitive principles.

My thesis is an analysis of classical problems in perceptual cognition as they appear in Descartes' mechanical philosophy. My primary focus will be on animals, as well as on the models and metaphors that Descartes used to explain how sense perception, information processing, self-regulation, and self-determination occur in natural automata. His models and metaphors typically include man-made devices of his age and a variety of natural processes taken from the inanimate part of nature, which will also be an integral part of my discussion. Throughout the analysis, I will approach these issues from the vantage point of the notion of physiological autonomy, a concept I develop to show how the inner mechanisms of organic bodies contribute to their autonomous functioning in the physical world in Descartes' conception. This is an important task because it allows us to have a better understanding of the mechanical approach to the living in the early modern period, but also because the approach I adopt here highlights the shortcomings of existing literature on the bête-machine theory which most often fail to appreciate Descartes' efforts to imagine a working cognitive system inside non-human living creatures. Even those commentators who direct their attention to Descartes' views about animals emphasise the limitations of natural automata resulting from what they are not, i.e. they are not mind-body unions as humans, whereas I shall maintain that if we understand correctly what the machinery of the body is capable of, we will understand better what Descartes has to say about human cognition as well, in particular, what he believes the body contributes to the cognitive economy of embodied minds.

The history of the robot as a concept and as a fact is indicated, and the current linguistic approach to robotology discussed. The problem of designing a robot-controller is approached by taking a simplified, computer-simulated, model of a robot in an environment, and writing programs to enable the robot to move around its environment in a reasonably intelligent manner. The problems of concept representation and the creation and execution of plans are dealt with in this simple system, and the problem of exploration is encountered but not satisfactorily dealt with. The robots' environment consists of a rectangular grid in which squares are labelled as belonging to the boundary, to fixed or movable objects, or to holes, while the robot itself occupies a single square, can sense the labels of the eight surrounding squares, can turn, and can pickup, move, and drop movable objects. The boundary of a typical environment is thus a rectanguloid polygon, which can be compared to the floor-plan of a one-level house. After an initial exploration the basic representation of the environment is as a sequence of edge-lengths and turns, called the ring-representation. An algorithm is described which produces the set of maximal subrectangles of the environment (i.e. rooms, passages, doorways) from the ring representation. To make plans for moving within the environment, the robot first views the maximal subrectangles as the vertices of a graph, wherein two vertices are connected by an edge if and only if the corresponding maximal subrectangles overlap, and then uses a path-finding algorithm to find a path between two vertices of the graph. This path constitutes a "plan of action". Whenever an isolated object or hole is found, its ring-representation is generated and its set of maximal subrectangles produced. Thus the shapes of objects and holes within the environment can be compared in various ways. In particular, an algorithm is described which compares the shape of a movable object with that of a hole to ascertain if the movable object could be moved to fit inside the hole without "physically" moving the object. ROSS, an interactive computer program which simulates the robot-environment model, is described. A command language allows the user to specify tasks for the robot at various conceptual levels. Several problems are listed concerning the ways in which a robot might explore, represent, and make plans about, its environment, most of which are amenable to direct attack in this simplified model. Finally, theoretical questions concerning two-dimensional rectanguloid shapes are raised. / Science, Faculty of / Computer Science, Department of / Graduate