Understanding intelligence-both human and artificial-requires not only theoretical insight but also practical tools for modeling complex cognitive processes. Modeling Intelligence: Computational Techniques in Cognitive Systems bridges the gap between cognitive theory and computational implementation. This book introduces readers to foundational and advanced techniques used in simulating perception, reasoning, learning, and decision-making, drawing from disciplines such as artificial intelligence, cognitive psychology, and ...
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Understanding intelligence-both human and artificial-requires not only theoretical insight but also practical tools for modeling complex cognitive processes. Modeling Intelligence: Computational Techniques in Cognitive Systems bridges the gap between cognitive theory and computational implementation. This book introduces readers to foundational and advanced techniques used in simulating perception, reasoning, learning, and decision-making, drawing from disciplines such as artificial intelligence, cognitive psychology, and neuroscience. The focus is on computational modeling as a means to both replicate and better understand the mechanisms of intelligent behavior. To make these concepts tangible and applicable, the book integrates hands-on examples using Python programming. Python's readability and the strength of its scientific libraries make it an ideal choice for demonstrating models ranging from neural networks and Bayesian inference to symbolic reasoning and agent-based systems. Each chapter pairs conceptual explanations with practical code to encourage exploration and experimentation. This approach is designed to support students, researchers, and practitioners seeking to deepen their understanding of cognitive systems through computational methods.
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