In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures.
This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games.
The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many years. Researchers in artificial intelligence have gone further, attempting to implement actual machines that mimic, simulate, and perhaps even replicate this capacity, called metareasoning. In this volume, leading authorities offer a variety of perspectives--drawn from philosophy, cognitive psychology, and computer science--on reasoning about the reasoning process.
The last decade has seen computational implementations of large hand-crafted natural language grammars in formal frameworks such as Tree-Adjoining Grammar (TAG), Combinatory Categorical Grammar (CCG), Head-driven Phrase Structure Grammar (HPSG), and Lexical Functional Grammar (LFG). Grammars in these frameworks typically associate linguistically motivated rich descriptions (Supertags) with words.
The conceptual history of cognitive science remains for the most part unwritten. In this groundbreaking book, Jean-Pierre Dupuy—one of the principal architects of cognitive science in France—provides an important chapter: the legacy of cybernetics. Contrary to popular belief, Dupuy argues, cybernetics represented not the anthropomorphization of the machine but the mechanization of the human.
Constraint logic programming, the notion of computing with partial information, is becoming recognized as a way of dramatically improving on the current generation of programming languages. This collection presents the best of current work on all aspects of constraint logic programming languages, from theory through language implementation.
Building a person has been an elusive goal in artificial intelligence. This failure, John Pollock argues, is because the problems involved are essentially philosophical; what is needed for the construction of a person is a physical system that mimics human rationality. Pollock describes an exciting theory of rationality and its partial implementation in OSCAR, a computer system whose descendants will literally be persons.
In the late 1950s, with mind-brain identity theories no longer dominant in philosophy of mind scientific materialists turned to functionalism, the view that the identity of any mental state depends on its function in the cognitive system of which it is a part. The philosopher Hilary Putnam was one of the primary architects of functionalism and was the first to propose computational functionalism, which views the human mind as a computer or an information processor.
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications.
Despite their apparently divergent accounts of higher cognition, cognitive theories based on neural computation and those employing symbolic computation can in fact strengthen one another. To substantiate this controversial claim, this landmark work develops in depth a cognitive architecture based in neural computation but supporting formally explicit higher-level symbolic descriptions, including new grammar formalisms.