Q Learning Behavior on Autonomous Navigation of Physical Robot

Behavior based architecture gives robot fast and reliable action. If there are many behaviors in robot, behavior coordination is needed. Subsumption architecture is behavior coordination method that give quick and robust response. Learning mechanism improve robot’s performance in handling uncertainty. Q learning is popular reinforcement learning method that has been used in robot learning because it is simple, convergent and off policy. In this paper, Q learning will be used as learning mechanism for obstacle avoidance behavior in autonomous robot navigation. Learning rate of Q learning affect robot’s performance in learning phase. As the result, Q learning algorithm is successfully implemented in a physical robot with its imperfect environment

Behavior Coordination Methods on Autonomous Navigation of Physical Robot

Behavior based architecture gives robot fast and reliable action. If there are many behaviors in robot, behavior coordination is needed. Subsumption architecture and motor schema is example of behavior coordination methods. In order to study those methods characteristics, computer simulation is not enough, experiments in physical robot are needed to be done. It can be concluded from experiment result that the first method gives quick, robust but non smooth response. Meanwhile the latter gives smooth but slower response, and it is tend to reach target faster than the first one. Some limitation of physical robot experiment also presented here

Measuring Strategic Uncertainty in Coordination Games

Lecture on the first SFB/TR 15 meeting, Gummersbach, July, 18 - 20, 2004This paper explores predictability of behavior in coordination games with multiple equilibria. In a laboratory experiment we measure subjects' certainty equivalents for three coordination games and one lottery. Attitudes towards strategic uncertainty in coordination games are related to risk aversion, experience seeking, gender and age. From the distribution of certainty equivalents among participating students we estimate probabilities for successful coordination in a wide range of coordination games. For many games success of coordination is predictable with a reasonable error rate. The best response of a risk neutral player is close to the global-game solution. Comparing choices in coordination games with revealed risk aversion, we estimate subjective probabilities for successful coordination. In games with a low coordination requirement, most subjects underestimate the probability of success. In games with a high coordination requirement, most subjects overestimate this probability. Data indicate that subjects have probabilistic beliefs about success or failure of coordination rather than beliefs about individual behavior of other players.

Strong Coordination over a Line Network

We study the problem of strong coordination in a three-terminal line network, in which agents use common randomness and communicate over a line network to ensure that their actions follow a prescribed behavior, modeled by a target joint distribution of actions. We provide inner and outer bounds to the coordination capacity region, and show that these bounds are partially optimal. We leverage this characterization to develop insight into the interplay between communication and coordination. Specifically, we show that common randomness helps to achieve optimal communication rates between agents, and that matching the network topology to the behavior structure may reduce inter-agent communication rates.Comment: To be presented at ISIT 2013, Istanbul, Turke

Coordination Behavior and Optimal Committee Size

How many members should committees consist of? This paper addresses this question in view of imperfect information and coordination behavior among the members, which is a new approach alternative to introducing information acquisition cost. First, using a simple model, I show that the existence of the coordination motive dismisses Condorcetfs (1785) suggestion and the finite optimal size of the committee is determined. Second, I provide an application of the mechanism to monetary policy committees in a basic New Keynesian model. This example will inspire other applications to policy issues in the dynamic stochastic general equilibrium framework.committee, Condorcet jury theorem, coordination, higher order beliefs monetary policy

The Open Method of Coordination (OMC) as an Evolutionary Learning Process

We interpret the Open Method of Coordination (OMC), recently adopted by the EU as a mode of governance in the area of social policy and other fields, as an imitative learning dynamics of the type considered in evolutionary game theory. The best-practise feature and the iterative design of the OMC correspond to the behavioral rule "imitate the best." In a redistribution game with utilitarian governments and mobile welfare beneficiaries, we compare the outcomes of imitative behavior (long-run evolutionary equilibrium), decentralized best-response behavior (Nash equilibrium), and coordinated policies. The main result is that the OMC allows policy coordination on a strict subset of the set of Nash equilibria, favoring in particular coordination on intermediate values of the policy instrument

Communication, coordination and networks

We study experimentally how the network structure and length of pre-play communication affect behavior and outcome in a multi-player coordination game with conflicting preferences. Network structure matters but the interaction between network and time effects is more subtle. Under each time treatment, substantial variations are observed in both the rate of coordination and distribution of coordinated outcomes across networks. But, increasing the communication length improves both efficiency and equity of coordination. In all treatments, coordination is mostly explained by convergence in communication. We also identify behaviors that explain variations in the distribution of coordinated outcomes both within and across networks

Communicating Processes with Data for Supervisory Coordination

We employ supervisory controllers to safely coordinate high-level discrete(-event) behavior of distributed components of complex systems. Supervisory controllers observe discrete-event system behavior, make a decision on allowed activities, and communicate the control signals to the involved parties. Models of the supervisory controllers can be automatically synthesized based on formal models of the system components and a formalization of the safe coordination (control) requirements. Based on the obtained models, code generation can be used to implement the supervisory controllers in software, on a PLC, or an embedded (micro)processor. In this article, we develop a process theory with data that supports a model-based systems engineering framework for supervisory coordination. We employ communication to distinguish between the different flows of information, i.e., observation and supervision, whereas we employ data to specify the coordination requirements more compactly, and to increase the expressivity of the framework. To illustrate the framework, we remodel an industrial case study involving coordination of maintenance procedures of a printing process of a high-tech Oce printer.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

The Open Method of Coordination (OMC)

We interpret the Open Method of Coordination (OMC), recently adopted by the EU as a mode of governance in the area of social policy and other ¯elds, as an imitative learning dynamics of the type considered in evolutionary game theory. The best-practise feature and the iterative design of the OMC correspond to the behavioral rule \imitate the best." In a redistribution game with utilitarian gov- ernments and mobile welfare bene¯ciaries, we compare the outcomes of imitative behavior (long-run evolutionary equilibrium), decentralized best-response behavior (Nash equilibrium), and coordinated policies. The main result is that the OMC allows policy coordination on a strict subset of the set of Nash equilibria, favoring in particular coordination on intermediate values of the policy instrument.

Coordination, Local Interactions and Endogenous Neighborhood Formation

The paper presents a local interaction model of coordination with spatially-located agents who simultaneously choose stage-game strategies and adaptively learn how to choose their neighbors. We analyze the behavior of the system when network externalities become eventually negative as neighborhood sizes increase. We show that the society can robustly reach in the long-run steady states characterized by high levels of coordination and small average neighborhood sizes. Moreover, we find that: (i) neighborhood adjustment allows for higher coordination than if interaction structures were static; and: (ii) large populations attain higher coordination provided that average initial neighborhood sizes are not too small.Learning, Coordination, Nearest-Neighbor Interactions, Endogenous Neighborhood Formation.