The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions. – Marvin Minsky, (Minsky, 1986) p. 163 In every art form it is the emotional content that makes the difference between mere technical skill and true art.

Emotions have a powerful impact on behavior and beliefs. The goal of our research is to create general computational models of this interplay of emotion, cognition and behavior to inform the design of virtual humans. Here, we address an aspect of emotional behavior that has been studied extensively in the psychological literature but largely ignored by computational approaches, emotion-focused coping. Rather than motivating external action, emotion-focused coping strategies alter beliefs in response to strong emotions. For example an individual may alter beliefs about the importance of a goal that is being threatened, thereby reducing their distress. We present a preliminary model of emotion-focused coping and discuss how coping processes, in general, can be coupled to emotions and behavior. The approach is illustrated within a virtual reality training environment where the models are used to create virtual human characters in high-stress social situations.

We analyze the computational complexity of causal relationships in Pearl's structural models, where we focus on causality between variables, event causality, and probabilistic causality. In particular, we analyze the complexity of the sophisticated notions of weak and actual causality by Halpern and Pearl. In the course of this, we also prove an open conjecture by Halpern and Pearl, and establish other semantic results. To our knowledge, no complexity aspects of causal relationships have been considered so far, and our results shed light on this issue. 1

In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl and (also briefly with) representations used in probabilistic planning. The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables -- whose values are determined by factors beyond our model -- in incorporating probabilities. We also incorporate probabilities into reasoning with narratives. 1

This paper is directed towards combining Pearl’s structural-model approach to causal reasoning with high-level formalisms for reasoning about actions. More precisely, we present a combination of Pearl’s structural-model approach with Poole’s independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structural-model approach. We illustrate this along Halpern and Pearl’s sophisticated notions of actual cause, explanation, and partial explanation. Furthermore, this mapping also adds first-order modeling capabilities and explicit actions to the structural-model approach.

In this paper, we continue our research on the algorithmic aspects of Halpern and Pearl's causes and explanations in the structural-model approach. To this end, we present new characterizations of weak causes for certain classes of causal models, which show that under suitable restrictions deciding causes and explanations is tractable. To our knowledge, these are the first explicit tractability results for the structuralmodel approach.

Abduction is an inference mechanism where given a knowledge base and some observations, the reasoner tries to find hypotheses which together with the knowledge base explain the observations. A reasoning based on such an inference mechanism is referred to as abductive reasoning. Given a theory and some observations, by filtering the theory with the observations, we mean selecting only those models of the theory that entail the observations. Entailment with respect to these selected models is referred to as filter entailment. In this paper we give necessary and sufficient conditions when abductive reasoning with respect to a theory and some observations is equivalent to the corresponding filter entailment. We then give sufficiency conditions for particular knowledge representation formalisms that guarantee that abductive reasoning can indeed be done through filtering and present examples from the knowledge representation literature where abductive reasoning is done through filtering. We...

We analyze the computational complexity of Halpern and Pearl's (causal) explanations in the structural-model approach, which are based on their notions of weak and actual causality. In particular, we give a precise picture of the complexity of deciding explanations, -partial explanations, and partial explanations, and of computing the explanatory power of partial explanations.

In this paper, we address the problem of answering formal requests about actions for active components that can be found in online services on the Internet. The components are described using a specific language and have access at runtime to a formal specification of their actions and current states. We present a formal model for requests about actions, based on the combination of speech acts and procedural types, that allows us to represent the different kinds of questions that a human user can ask about the actions, behaviour and activity of an active component. We propose answering algorithms and we illustrate them on some examples.

Intelligent agents are typically situated in a social environment and must reason about social cause and effect. Such reasoning is qualitatively different from physical causal reasoning that underlies most intelligent systems. Modeling social causal reasoning can enrich the capabilities of multi-agent systems and intelligent user interfaces. In this paper, we empirically evaluate a computational model of social causality and responsibility against human social judgments. Results from our experimental studies show that in general, the model’s predictions of internal variables and inference process are consistent with human responses, though they also suggest some possible refinement to the computational model.