The Cambridge Handbook of Artificial IntelligenceKeith Frankish, William M. Ramsey Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. AI applications are transforming the way we interact with each other and with our environment, and work in artificially modeling intelligence is offering new insights into the human mind and revealing new forms mentality can take. This volume of original essays presents the state of the art in AI, surveying the foundations of the discipline, major theories of mental architecture, the principal areas of research, and extensions of AI such as artificial life. With a focus on theory rather than technical and applied issues, the volume will be valuable not only to people working in AI, but also to those in other disciplines wanting an authoritative and up-to-date introduction to the field. |
Contents
Foundations | 4 |
History motivations and core themes | 15 |
Philosophical foundations | 34 |
Philosophical challenges | 64 |
GOFAI | 89 |
Connectionism and neural networks | 108 |
Dynamical systems and embedded cognition | 128 |
Learning | 151 |
Reasoning and decision making | 191 |
Language and communication | 213 |
Actions and agents | 232 |
Artificial emotions and machine consciousness | 247 |
Robotics | 269 |
wooden blocks on its way back to its recharging hutch | 271 |
Artificial life | 296 |
The ethics of artificial intelligence | 316 |
Other editions - View all
The Cambridge Handbook of Artificial Intelligence Keith Frankish,William M. Ramsey Limited preview - 2014 |
The Cambridge Handbook of Artificial Intelligence Keith Frankish,William M. Ramsey No preview available - 2014 |
Common terms and phrases
action applications approach architecture argument Artificial Intelligence assumptions autonomous agents Bayesian Bayesian networks behavior biological brain Brooks Cambridge causal chapter Chinese Room cognitive science complex computer vision concepts connectionism connectionist models consciousness decision distributed domain Dreyfus dynamical systems embodied emotions environment ethical evolution evolutionary robotics evolved example experience expert systems formal function goal GOFAI human hybrid implement inference input interaction internal issues knowledge learning algorithms linguistic logic machine learning mechanisms mental methods mind moral status multi-agent natural language natural language processing neural networks nodes object ofAI ofthe output particular patterns philosophical physical predict probabilistic problem psychological question reasoning reinforcement learning relevant represent representation role Searle Searle’s semantic sensors simulation situated soft artificial specific strong AI structure supervised learning symbolic task techniques theoretical theory tion Turing Turing Test typically understanding University Press variables