Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Multi-Agent Simulation of Emergence of Schwa Deletion Pattern in Hindi

Recently, there has been a revival of interest in multi-agent simulation techniques for exploring the nature of language change. However, a lack of appropriate validation of simulation experiments against real language data often calls into question the general applicability of these methods in modeling realistic language change. We try to address this issue here by making an attempt to model the phenomenon of schwa deletion in Hindi through a multi-agent simulation framework. The pattern of Hindi schwa deletion and its diachronic nature are well studied, not only out of general linguistic inquiry, but also to facilitate Hindi grapheme-to-phoneme conversion, which is a preprocessing step to text-to-speech synthesis. We show that under certain conditions, the schwa deletion pattern observed in modern Hindi emerges in the system from an initial state of no deletion. The simulation framework described in this work can be extended to model other phonological changes as well.Language Change, Linguistic Agent, Language Game, Multi-Agent Simulation, Schwa Deletion

Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots

The present dissertation addresses problems related to robot learning from demonstra¬ tion. It presents the building of a connectionist architecture, which provides the robot with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬ guage taught by an external teacher agent. This thesis considers three main issues: 1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol grounding of a robot's actions and perceptions, 3) development of a common symbolic representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow the agent to co-ordinate its actionn to that of the teacher agent, are required aside to general cognitive abilities of associativity, in order to constrain the agent's attention to making relevant perceptions, onto which it grounds the teacher agent's symbolic expression. In addition, the agent should be provided with the cognitive capacity for extracting spatial and temporal invariance in the continuous flow of its perceptions. Based on this requirement, we develop a connectionist architecture for learning time series. The model is a Dynamical Recurrent Associative Memory Architecture, called DRAMA. It is a fully connected recurrent neural network using Hebbian update rules. Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated robotic experiments. Training of the network is computationally fast and inexpensive, which allows its implementation for real time computation and on-line learning in a inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner robot is taught by a teacher agent, a human instructor and another robot, a vocabulary to describe its perceptions and actions. The experiments are based on an imitative strategy, whereby the learner robot reproduces the teacher's actions. While imitating the teacher's movements, the learner robot makes similar proprio and exteroceptions to those of the teacher. The learner robot grounds the teacher's words onto the set of common perceptions they share. We carry out experiments in simulated and physical environments, using different robotic set-ups, increasing gradually the complexity of the task. In a first set of experiments, we study transmission of a vocabulary to designate actions and perception of a robot. Further, we carry out simulation studies, in which we investigate transmission and use of the vocabulary among a group of robotic agents. In a third set of experiments, we investigate learning sequences of the robot's perceptions, while wandering in a physically constrained environment. Finally, we present the implementation of DRAMA in Robota, a doll-like robot, which can imitate the arms and head movements of a human instructor. Through this imitative game, Robota is taught to perform and label dance patterns. Further, Robota is taught a basic language, including a lexicon and syntactical rules for the combination of words of the lexicon, to describe its actions and perception of touch onto its body

Flexibly Instructable Agents

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible instructability that distinguish it from previous systems: (1) it can take known or unknown commands at any instruction point; (2) it can handle instructions that apply to either its current situation or to a hypothetical situation specified in language (as in, for instance, conditional instructions); and (3) it can learn, from instructions, each class of knowledge it uses to perform tasks.Comment: See http://www.jair.org/ for any accompanying file

Thomas S. Schelling: game theory and indirect communication

In their recent work Thomas S. Schelling (2007, 2010), reiterating original arguments about game theory and its applications to social sciences. In particular, game theory helps to explore situations in which agents make decisions interdependent (strategic communication). Schelling's originality is to extend economic theory to social sciences. When a player can anticipate the options and influence the decisions of others. The strategy, indirect communication plays a crucial role. To illustrate, we investigate how to perform the payoff matrix in cases of bribery and threatSocial Science, Schelling, game theory, strategic communications, bribes, threats

Report on the Second Workshop on Distributed AI

On June 24, 1981 twenty-five participants from organizations around the country gathered in MIT's Endicott House for the Second Annual Workshop on Distributed AI. The three-day workshop was designed as an informal meeting, centered mainly around brief research reports presented by each group, along with an invited talk. In keeping with the spirit of the meeting, this report was prepared as a distributed document, with each speaker contributing a summary of his remarks.MIT Artificial Intelligence Laborator