Collaborating Authors

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self Artificial Intelligence

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the-art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

A Biosemiotic Framework for Artificial Autonomous Sign Users

AAAI Conferences

A conceptual framework inspired by biosemiotics is developed for the study of signs in autonomous artificial sign users. Our theory of reference uses an ethological analysis of animal-environment interaction. We first discuss semiotics with respect to the meaning of signals taken up from the environment of an autonomous agent. We then show how semantic issues arise in a similar way in the study of adaptive artificial sign users. Anticipation and adaptation play the important role of defining purpose which is a necessary concept in the semiotics of learning robots. The proposed focus on sign acts leads to a semantics in which meaning and reference are based on the anticipated outcome of sign-based interaction. It is argued that such a novel account of semantics based on indicative acts of reference is compatible with merely indicative approaches in more conventional semiotic frameworks such as symbol anchoring approaches in robotics.

Leveling Up: Strategies to Achieve Integrated Cognitive Architectures

AAAI Conferences

Human-level cognition (most uniquely characterized by our abilities to use language) should be seen as a superset of functional and behavioral capabilities shared by lower life-forms including animals and insects, and this perspective ought to principally guide our strategies for developing integrated cognitive architectures. Just as the study of biological model organisms has led to tremendous advances in our scientific knowledge of genetics and cellular function, the study of embodied cognition in simple agent-environment simulations can yield similar advances in Cognitive Science, Artificial Intelligence, and Robotics. By working first on the foundations of intelligent interaction with one’s environment, and by focusing on core functions such as predictive and inductive learning, probabilistic goal-directed behavior compilation, and empathetic reasoning, we can better establish the grounding that the physical symbol system hypothesis assumes (Newell and Simon 1976), yet often without explicit demonstration of a mechanism to derive symbolic relations and semantics from raw sensory data. Logic and language are seen to emerge from our willingness to make discrete simplifying assumptions in a continuous and probabilistic world of experience, and developing a Standard Model of the Mind can help build much-needed bridges between historically non-aligned research communities.

Grounding the Acquisition of Grammar in Sensorimotor Representations

AAAI Conferences

Drawing on data from linguistics, developmental psychology and the neurosciences, we present a computational theory of the acquisition of early grammar by infants. Based on the view that language is a mapping between form and meaning, we propose that a theory of language acquisition must be tightly integrated with a theory of the infant's prelinguistic representations. Namely, the infant's task is to learn how to map the linguistic form in the input to her representations of the corresponding scenes. We have developed a theory of prelinguistic cognition based on i) what is currently known about the architecture of the brain, and ii) the representational requirements for successful (sensorimotor) behavior in the world. We show how such prelinguistic sensorimotor representations can provide the basis for the acquisition of early grammatical forms, and thereby ground language in the world. Importantly, this is true not only at the lexical level, but also at the grammatical level.