Information driven self-organization of complex robotic behaviors

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.Comment: 29 pages, 12 figure

The Current State of Normative Agent-Based Systems

Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling

The effects of team-skills training on transactive memory and performance

The existence of effective Transactive Memory Systems (TMS) in teams has been found to enhance task performance. Methods of developing Transactive Memory (TM) are therefore an important focus of research. This study aimed to explore one such method, the use of a generic team-skills training programme to develop TM and subsequent task performance. Sixteen three-member teams were all trained to complete a complex collaborative task, prior to which half the teams (n=8), completed a team-skills training programme. Results confirmed that those teams who had been trained to develop a range of team skills such as problem-solving, interpersonal relationships, goal setting and role allocation, evidenced significantly higher team skill, TM and performance than those who were not trained in such skills. Results are discussed with reference to the wider TM literature and the mechanisms through which team-skills training could facilitate the more rapid development of TM

The dynamics of Environmentalism and the Environment

We study the relationship between environmental preferences and the environment. Preferences are transmitted intergenerationally and through social interactions, where we assume that agents are more likely to adopt environmental preferences the larger the amount of pollution. In the basic setting we find that both converge non-monotonically towards an interior steady state. When including technical change we notice that there will be no change in the steady state level of the environment unless technical change is sufficiently strong, which stands in stark contrast to the literature. Upon introducing environmental laws we find that these may lead to a virtually pollution-free environment. This happens if environmental laws are implemented when public support is strong enough. 1 Department of Economics, Ecole Polytechnique, 91128 Palaiseau Cedex, France. email: [email protected]. tel: 0033 169333038. The author kindly acknowledges the helpful comments by two anonymous referees.

Robot computer problem solving system

The conceptual, experimental, and practical phases of developing a robot computer problem solving system are outlined. Robot intelligence, conversion of the programming language SAIL to run under the THNEX monitor, and the use of the network to run several cooperating jobs at different sites are discussed

Challenges to the Integration of Renewable Resources at High System Penetration

Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches