Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian model of plan recognition
Artificial Intelligence
Negotiation and cooperation in multi-agent environments
Artificial Intelligence - Special issue on economic principles of multi-agent systems
A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
Towards a "theory of mind" in simulated robots
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Corpus-based, statistical goal recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Goal recognition with variable-order Markov models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Intention Recognition via Causal Bayes Networks Plus Plan Generation
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Learning to coordinate in complex networks
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Intention-based decision making with evolution prospection
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
| Hi-index | 0.00 |
Intention recognition is ubiquitous in most social interactions among humans and other primates. Despite this, the role of intention recognition in the emergence of cooperative actions remains elusive. Resorting to the tools of evolutionary game theory, herein we describe a computational model showing how intention recognition coevolves with cooperation in populations of self-regarding individuals. By equipping some individuals with the capacity of assessing the intentions of others in the course of a prototypical dilemma of cooperation-the repeated prisoner's dilemma-we show how intention recognition is favored by natural selection, opening a window of opportunity for cooperation to thrive. We introduce a new strategy IR that is able to assign an intention to the actions of opponents, on the basis of an acquired corpus consisting of possible plans achieving that intention, as well as to then make decisions on the basis of such recognized intentions. The success of IR is grounded on the free exploitation of unconditional cooperators while remaining robust against unconditional defectors. In addition, we show how intention recognizers do indeed prevail against the best-known successful strategies of iterated dilemmas of cooperation, even in the presence of errors and reduction of fitness associated with a small cognitive cost for performing intention recognition.