Given its ubiquity, scale and complexity, few problems have created the combined interest of so many unrelated areas as the evolution of cooperation. Using the tools of evolutionary game theory, here we address, for the first time, the role played by intention recognition in the final outcome of cooperation in large populations of self-regarding individuals. By equipping individuals with the capacity of assessing intentions of others in the course of repeated Prisoner’s Dilemma interactions, we show how intention recognition opens a window of opportunity for cooperation to thrive, as it precludes the invasion of pure cooperators by random drift while remaining robust against defective strategies. Intention recognizers are able to assign an intention to the action of their opponents based on an acquired corpus of possible intentions. We show how intention recognizers can prevail against most famous strategies of repeated dilemmas of cooperation, even in the presence of errors. Our approach invites the adoption of other classification and pattern recognition mechanisms common among Humans, to unveil the evolution of complex cognitive processes in the context of social dilemmas. 1

Few problems have created the combined interest of so many unrelated areas as the evolution of cooperation. As a result, several mechanisms have been identified to work as catalyzers of cooperative behavior. Yet, these studies, mostly grounded on evolutionary dynamics and game theory, have neglected the important role played by intention recognition in behavioral evolution. Here we address explicitly this issue, characterizing the dynamics emerging from a population of intention recognizers. We derive a Bayesian Network model for intention recognition in the context of repeated social dilemmas and evolutionary game theory, by assessing the internal dynamics of trust between intention recognizers and their opponents. Intention recognizers are then able to predict the next move of their opponents based on past direct interactions, which, in turn, enables them to prevail over the most famous strategies of repeated dilemmas of cooperation, even in presence of noise. Overall, our framework offers new insights on the complexity and beauty of behavioral evolution driven by elementary forms of cognition.

This paper is twofold. First, we present a new method for collective intention recognition based on mainstream philosophical accounts. Second, we extend our previous Elder Care system with collective intention recognition ability for assisting a couple of elderly people. The previous system was just capable of individual intention recognition, and so it has now been enabled to deal with situations where the elders intend to do things together. 1.

We present a method for context-dependent and incremental intention recognition by means of incrementally constructing a Bayesian Network (BN) model as more actions are observed. It is achieved with the support of a knowledge base of readily maintained and constructed fragments of BNs. The simple structure of the fragments enables to easily and efficiently acquire the knowledge base, either from domain experts or automatically from a plan corpus. We exhibit experimental results improvement for the Linux Plan corpus. For additional experimentation, new plan corpora for the iterated Prisoner’s Dilemma are created. We show that taking into account contextual information considerably increases intention recognition performance. 1

Given its ubiquity, scale and complexity, few problems have created the combined interest of so many unrelated areas as the evolution of cooperation. Using the tools of evolutionary game theory, here we address, for the first time, the role played by intention recognition in the final outcome of cooperation in large populations of self-regarding individuals. By equipping individuals with the capacity of assessing intentions of others in the course of repeated Prisoner’s Dilemma interactions, we show how intention recognition opens a window of opportunity for cooperation to thrive, as it precludes the invasion of pure cooperators by random drift while remaining robust against defective strategies. Intention recognizers are able to assign an intention to the action of their opponents based on an acquired corpus of possible intentions. We show how intention recognizers can prevail against most famous strategies of repeated dilemmas of cooperation, even in the presence of errors. Our approach invites the adoption of other classification and pattern recognition mechanisms common among Humans, to unveil the evolution of complex cognitive processes in the context of social dilemmas. 1

Abstract. We explore a coherent combination, for decision making, of

We present a method for incremental intention recognition by means of incrementally constructing a Bayesian Network (BN) model as more actions are observed. It is achieved based on a knowledge base of easily maintained and constructed fragments of BNs, connecting intentions to actions. The simple structure of the fragments enables to easily and efficiently acquire the knowledge base, either from domain experts or automatically from a plan corpus. We show experimental results improvement for the Linux Plan Corpus. In addition, we create a new, so-called IPD Plan Corpus, for strategies in the iterated Prisoner’s Dilemma and show the experimental results for it. 1.

In many applications, it is required to detect the occurrence of an event in a system, which entails observing the system. Observation can be costly, so it makes sense to try and reduce the number of observations, without losing certainty about the event’s occurrence. In this paper, we propose a formalization of the problem. We show (formally) that when the event to be detected follows a discrete spatial or temporal pattern, it is possible to reduce the number of observations. We provide an experimental evaluation of algorithms for this purpose. We apply the result to verification of linear temporal logics formulæ. Finally, we discuss possible generalizations, and how event detection and related applications can benefit from logic programming techniques. 1