I see what you mean

The ability to understand and predict others' behavior is essential for successful interactions. When making predictions about what other humans will do, we treat them as intentional systems and adopt the intentional stance, i.e., refer to their mental states such as desires and intentions. In the present experiments, we investigated whether the mere belief that the observed agent is an intentional system influences basic social attention mechanisms. We presented pictures of a human and a robot face in a gaze cuing paradigm and manipulated the likelihood of adopting the intentional stance by instruction: in some conditions, participants were told that they were observing a human or a robot, in others, that they were observing a human-like mannequin or a robot whose eyes were controlled by a human. In conditions in which participants were made to believe they were observing human behavior (intentional stance likely) gaze cuing effects were significantly larger as compared to conditions when adopting the intentional stance was less likely. This effect was independent of whether a human or a robot face was presented. Therefore, we conclude that adopting the intentional stance when observing others' behavior fundamentally influences basic mechanisms of social attention. The present results provide striking evidence that high-level cognitive processes, such as beliefs, modulate bottom-up mechanisms of attentional selection in a top-down manner

Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot using the mechanisms of classical and operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, number or configuration of the robot's sensors. In this article we provide a detailed presentation of the model, and show our results with the Khepera and Pioneer 1 mobile robots.Office of Naval Research (N00014-96-1-0772, N00014-95-1-0409

A design strategy for autonomous systems

Some solutions to crucial issues regarding the competent performance of an autonomously operating robot are identified; namely, that of handling multiple and variable data sources containing overlapping information and maintaining coherent operation while responding adequately to changes in the environment. Support for the ideas developed for the construction of such behavior are extracted from speculations in the study of cognitive psychology, an understanding of the behavior of controlled mechanisms, and the development of behavior-based robots in a few robot research laboratories. The validity of these ideas is supported by some simple simulation experiments in the field of mobile robot navigation and guidance