Computer Science Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2756

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    Time-Situated Metacognitive Agency and Other Aspects of Commonsense Reasoning
    (2022) Goldberg, Matthew David; Perlis, Donald; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Much research in commonsense reasoning (CSR) involves use of external representations of an agent's reasoning, based on compelling features of classical logic. However, these advantages come with severe costs, including: omniscience, consistency, static semantics, frozen deadlines, lack of self-knowledge, and lack of expressive power to represent the reasoning of others. Active logic was developed to address many of these, but work to date still leaves serious gaps. The present work focuses on major extensions of active logic to deal with self-knowledge, and their implementation into a newly-developed automated reasoner for commonsense active logic. Dealing with self-knowledge has been designed and implemented in the reasoner via a new treatment of quotation as a form of nesting. More sophisticated varieties of nesting, particularly quasi-quotation mechanisms, have also been developed to extend the basic form of quotation. Active logic and the reasoner are applied to classical issues in CSR, including a treatment of one agent having the knowledge and inferential mechanisms to reason about another's time-situated reasoning.
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    A Unified Theory Of Acting And Agency For A Universal Interfacing Agent
    (2005-12-12) Josyula, Darsana Purushothaman; Perlis, Donald R; Anderson, Michael L; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    With consumer electronics becoming numerous, various and complex, the idea of a single, shared, general and flexible interfacing agent to interface human users with the multitude of task-oriented systems or devices seems appealing. Such a universal interfacing agent has to understand user instructions and issue commands to control the task-oriented system to which it is connected, in a manner that the given user desires. Two important issues that such an agent has to deal with are: (i) how to represent and reason about the tasks that a given device can perform and the results that a given device can produce and (ii) how to represent and reason about when different tasks are to be performed and whether the tasks have been successful. The dissertation explores these issues in detail and provides a solution to deal with these issues within a contradiction-tolerant and time-sensitive framework called Active logic. The solution involves explicitly representing the beliefs, desires, intentions, expectations, observations and achievements of the interfacing agent and reasoning based on these attitudes; the dissertation provides a theory (ALFA) that agents can use in order to perform this reasoning. The theory specifies the interactions between beliefs, observations, desires, intentions, expectations and achievements for a universal interfacing agent, while taking into consideration issues associated with concurrent execution of actions as well as perturbation tolerance. The main characteristics of the theory are: representing and reasoning about concurrent actions and results, dealing with interactions of preconditions of actions or results, dynamic reconsideration of intentions and reasoning using expectations and achievements. The dissertation also provides an architecture (DIRECTOR) for implementing agents based on the theory. In this architecture, a meta-cognitive process controls the cognitive activities of the agent. The rudimentary results of implementing the architecture to create a natural language based interfacing agent (ALFRED) are also discussed in the dissertation. This work also discusses how the agent's underlying Active logic knowledge base evolves during reasoning and provides proofs for properties that the knowledge base exhibits, using a meta-theory that specifies how the knowledge base evolves.