Between a Rock and a Hard Place: Cognitive Science Principles Meet AI-Hard Problems
Contents
Artificial Intelligence: What It Is, and What It Should Be
PDFRobust Inference with Simple Cognitive Models
PDFContents
PDFCogSci to AI: It’s the Brainware, Stupid!
PDFOrganizing Committee
PDFA Cognitive Science Based Machine Learning Architecture
PDFHow One Can Learn Programming While Teaching Reasoning to Children with Autism
PDFMotivating the 2006 AAAI Spring Symposium: Cognitive Science Principles Meet AI-Hard Problems
PDF4CAPS: An Adaptive Architecture for Human Information Processing
PDFMixing Cognitive Science Concepts with Computer Science Algorithms and Data Structures: An Integrative Approach to Strong AI
PDFFor Problems Sufficiently Hard … AI Needs CogSci
PDFCognitive Approaches to the Traveling Salesperson Problem: Perceptual Complexity that Produces Computational Simplicity
PDFNew Challenges in AI for Military Simulation: Are Multilevel Heterogeneous Models the Solution?
PDFA Change Detection Model for Non-Stationary K-Armed Bandit
PDFMulti-Modal Cognitive States: Augmenting the State in Cognitive Architectures
PDFThe Relevance of Artificial Intelligence for Human Cognition
PDFAI, Cognitive, and Quantum Models of Organizations. A Progress Report
PDFNeither Here nor There: Inference Research Bridges the Gaps between Cognitive Science and AI
PDFCognitive Automation Solves Many AI-Hard Problems
PDFIn Support of Pragmatic Computation
PDFCan NLP Systems be a Cognitive Black Box? (Is Cognitive Science Relevant to AI Problems?)
PDFArtificial Intelligence and Cognitive Science have the Same Problem
PDFSimulating Intelligent Behavior Requires a Complex Approach
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