this paper the internal dynamics of mental states, in particular states based on beliefs, desires and intentions, is formalised using a temporal language. A software environment is presented that can be used to specify, simulate and analyse temporal dependencies between mental states in relation to traces of them. If also relevant data on internal physical states over time are available, these can be analysed with respect to their relation to mental states as well

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Previous work on early language acquisition has shown that word meanings can be acquired by an associative procedure that maps perceptual experience onto linguistic labels based on cross-situational observation. A new trend termed social-pragmatic theory [27] focuses on the effect of the child’s social-cognitive capacities, such as joint attention and intention reading. In this paper, we argue that statistical and social cues can be seamlessly integrated to facilitate early word learning. To support this idea, we first introduce a statistical learning mechanism that provides a formal account of cross-situational observation. The main part of this paper then presents a unified model that is able to make use of different kinds of social cues, such as joint attention and prosody in maternal speech, in the statistical learning framework. In a computational analysis of infant data, we report the quantitative results of our unified model in computing word-meaning associations, which outperforms the purely statistical learning method. 1

Abstract: It is widely believed that what distinguishes the social cognition of humans from that of other animals is the belief-desire psychology of four-year-old children and adults (so-called theory of mind). We argue here that this is actually the second ontogenetic step in uniquely human social cognition. The first step is one year old children’s understanding of persons as intentional agents, which enables skills of cultural learning and shared intentionality. This initial step is ‘the real thing ’ in the sense that it enables young children to participate in cultural activities using shared, perspectival symbols with a conventional/normative/reflective dimension—for example, linguistic communication and pretend play—thus inaugurating children’s understanding of things mental. Understanding beliefs and participating in collective intentionality at four years of age—enabling the comprehension of such things as money and marriage—results from several years of engagement with other persons in perspective-shifting and reflective discourse containing propositional attitude constructions. By all appearances, the cognitive skills of human beings are very different from those of other animal species, including our nearest primate relatives. Human

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Developing robots with social skills and understanding is a critical step towards enabling them to cooperate with people as capable partners, to communicate with people intuitively, and to learn quickly and effectively from natural human instruction. These abilities would enable many new and exciting applications for robots that require them to play a long-term, supportive, and helpful role in people's daily lives. This paper describes our work towards building sociable autonomous robots that can work in collaboration with people. Our approach puts an emphasis on task dialog and social communication under the theoretical framework of joint intention theory.

A mounting body of evidence in psychology and neuroscience points towards an embodied model of cognition, in which the mechanisms governing perception and action are strongly interconnected, and also play a central role in higher cognitive functions, traditionally modeled as amodal symbol systems. We argue that robots designed to interact fluidly with humans must adopt a similar approach, and shed traditional distinctions between cognition, perception, and action. In particular, embodiment is crucial to fluid joint action, in which the robot’s performance must tightly integrate with that of a human counterpart, taking advantage of rapid sub-cognitive processes. We thus propose a model for embodied robotic cognition that is built upon three propositions: (a) modal, perceptual models of knowledge; (b) integration of perception and action; (c) top-down bias in perceptual processing. We then discuss implications and derivatives of our approach. “[T]he human being is a unity, an indivisible whole. [...] ideas, emotions and sensations are all indissolubly interwoven. A bodily movement ‘is ’ a thought and a thought expresses itself in corporeal form.”

In this paper a temporal trace language is defined in which statements can be expressed that provide an external temporal grounding of intentional notions. Justifying conditions are presented that formalise criteria that a (candidate) statement must satisfy in order to qualify as an external representation of a belief, desire or intention. Using these conditions, external representation statements for intentional notions can be identified.

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among agents or detection of situations that can pose a threat. In this paper, we propose an approach that allows a robot to detect intentions of others based on experience acquired through its own sensory-motor capabilities, then using this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of Hidden Markov Models designed to model a robot’s experience and interaction with the world. The robot’s capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics – autonomous vehicles, operator interfaces, sensors.