We present GOSMR ("goal oriented scenario modeling robots"), a cognitive architecture designed to show coordinated, goal-directed behavior over the Internet, focusing on the web browser as a case study. The architecture combines a variety of artificial intelligence techniques, including planning, temporal difference learning, elementary reasoning over uncertainty, and natural language parsing, but is designed to be computationally lightweight. Its intended use is to be deployed on virtual machines in large-scale network experiments in which simulated users' adaptation in the face of resource denial should be intelligent but varied. The planning system performs temporal difference learning of action times, discounts goals according to hyperbolic discounting of time-to-completion and chance of success, takes into account the assertions of other agents, and separates abstract action from site-specific affordances. Our experiment, in which agents learn to prefer a social networking style site for sending and receiving messages, shows that utility-proportional goal selection is a reasonable alternative to Boltzmann goal selection for producing a rational mix of behavior.

This paper investigates the potential usefulness of viewing the system of human, robot, and environment as an "information pipeline" from environment to user and back again. Information theory provides tools for analyzing and maximizing the information rate of each stage of this pipeline, and could thus encompass several common HRI goals: "situational awareness," which can be seen as maximizing the information content of the human's model of the situation; efficient robotic control, which can be seen as finding a good codebook and high throughput for the Human-Robot channel; and artificial intelligence, which can be assessed by how much it reduces the traffic on all four channels. Analysis of the information content of the four channels suggests that human to robot communication tends to be the bottleneck, suggesting the need for greater onboard intelligence and a command interface that can adapt to the situation.

This paper presents the results of a panel discussion titled "The Future of HRI," held during an NSF workshop for graduate students on human-robot interaction in August 2006. The panel divided the workshop into groups tasked with inventing models of the field, and then asked these groups their opinions on the future of the field. In general, the workshop participants shared the belief that HRI can and should be seen as a single scientific discipline, despite the fact that it encompasses a variety of beliefs, methods, and philosophies drawn from several "core" disciplines in traditional areas of study. HRI researchers share many interrelated goals, participants felt, and enhancing the lines of communication between different areas would help speed up progress in the field. Common concerns included the unavailability of common robust platforms, the emphasis on human perception over robot perception, and the paucity of longitudinal real-world studies. The authors point to the current lack of consensus on research paradigms and platforms to argue that the field is not yet in the phase that philosopher Thomas Kuhn would call "normal science," but believe the field shows signs of approaching that phase.

We present a method of grounded word learning that can learn the meanings of first and second person pronouns. The model selectively associates new words with agents in the environment by using already understood words to establish context. The method uses chi-square tests to find significant associations between the new words and attributes of the relevant agents. We show that this model can learn from a transcript of a parent-child interaction that "I" refers to the person who is speaking. With the additional information that questions about wants refer to the person being asked about them, the system learns that "you" refers to the person being addressed. We show that an incorrect assumption about the subject of "want" questions can lead to pronoun reversal, a linguistic error most commonly found in autistic and congenitally blind children. Finally, we present results from a physical implementation on a robot that runs in real time.

By learning a range of possible times over which the effect of an action can take place, a robot can reason more effectively about causal and contingent relationships in the world. An algorithm is presented for learning the interval of possible times during which a response to an action can take place. The algorithm was implemented on a physical robot for the domains of visual self-recognition and auditory social-partner recognition. The environment model assumes that natural environments generate Poisson distributions of random events at all scales. A linear-time algorithm called Poisson threshold learning can generate a threshold T that provides an arbitrarily small rate of background events λ (T), if such a threshold exists for the specified error rate.

We present a method for allowing a humanoid robot to recognize its own motion in its visual field, thus enabling it to distinguish itself from other agents in the vicinity. Our approach consists of learning a characteristic time window between the initiation of motor movement and the perception of arm motions. The method has been implemented and evaluated on an infant humanoid platform. Our results demonstrate the effectiveness of using the delayed temporal contingency in the action-perception loop as a basis for simple self-other discrimination. We conclude by suggesting potential applications in social robotics and in generating forward models of motion.