Artificial intelligence: the very idea
Artificial intelligence: the very idea
Building expert systems
Generality in artificial intelligence
Communications of the ACM
Cyc: toward programs with common sense
Communications of the ACM
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Intelligence without representation
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
The Universal Computer: The Road from Leibniz to Turing
The Universal Computer: The Road from Leibniz to Turing
Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence Programming
Artificial Intelligence Programming
Inside Computer Understanding: Five Programs Plus Miniatures
Inside Computer Understanding: Five Programs Plus Miniatures
Computers and Thought
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Constraint Processing
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
Semantic Information Processing
Semantic Information Processing
On probabilistic inference by weighted model counting
Artificial Intelligence
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A new representation and associated algorithms for generalized planning
Artificial Intelligence
Answer set programming at a glance
Communications of the ACM
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Generalized planning: synthesizing plans that work for multiple environments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Artificial Intelligence is a brain child of Alan Turing and his universal programmable computer. During the 1960s and 1970s, AI researchers used computers for exploring intuitions about intelligence and for writing programs displaying intelligent behavior. A significant change occurred however in the 1980s, as many AI researchers moved from the early AI paradigm of writing programs for ill-defined problems to writing solvers for well-defined mathematical models like Constraint Satisfaction Problems, Strips Planning, SAT, Bayesian Networks, Partially Observable Markov Decision Processes and General Game Playing. Solvers are programs that take a compact description of a particular model instance and automatically compute its solution. Unlike the early AI programs, solvers are general as they must deal with any instance that fits the model. Many ideas have been advanced to address this crisp computational challenge from which a number of lessons can be drawn. In this paper, I revisit the problem of generality in AI, look at the way in which this 'Models and Solvers' agenda addresses the problem, and discuss the relevance of this agenda to the grand AI goal of a computational account of intelligence and human cognition.