Dissertation/Thesis Abstract

Sense-Making Machines
by Sarathy, Vasanth, Ph.D., Tufts University, 2020, 414; 28089396
Abstract (Summary)

Although statistical machine learning techniques have led to significant advances in AI systems, they are still far from demonstrating fundamental intelligence capabilities possessed by human toddlers and even some animals. After decades of research and millennia of scientific and philosophical thought, the central goals of AI -- to explain and replicate human intelligence and creativity -- still remain unmet. In this thesis, I argue for instilling in AI systems, the ability to continually "make sense" of its changing world to guide behavior and understand perceptual information. Different from current mainstream AI approaches, I propose that agents maintain and update mental representations of the world that allow them to reason about symbolic concepts under uncertainty. I show that such representations and inference machinery are needed at all levels of cognitive processing -- from language interpretation, basic visual perception and action selection to high-level deliberation and even creative problem-solving. In the latter, sense-making becomes sense-breaking, in which I demonstrate how an agent can break its own assumptions and biases in order to discover novel ideas and solutions. Symbolic representations allow an agent to reason beyond statistical patterns, verify the veracity of their knowledge, recognize gaps in their understanding, raise questions, and explore the world to seek out answers. In doing so, such representations also allow artificial agents to provide us, humans, explanations of their behaviors, allow us to better interpret and understand their actions, and

ensure that they comply with our social norms.

Indexing (document details)
Advisor: Scheutz, Matthias, Dennett, Daniel
Commitee: Blumer, Anselm, Sinapov, Jivko, Premaratne, Kamal
School: Tufts University
Department: Computer Science
School Location: United States -- Massachusetts
Source: DAI-B 82/4(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer science, Artificial intelligence, Cognitive psychology
Keywords: Affordance, Creative problem-solving, Human-robot interaction, Language understanding, Sense-making, Social norms
Publication Number: 28089396
ISBN: 9798684655234
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