Learning Optimal Discriminant Functions through a Cooperative Game of Automata

The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.

Indicators of wellbeing in recreational video game players

Video game play is a popular entertainment choice, yet we have a limited understanding of the potential wellbeing benefits associated with recreational play. An online survey (final sample, n = 297) addresses this by investigating how the player experience related to wellbeing. The impact of amount of play, game genre, mode of play (social or solitary play) and the psychological experience of play (flow and need satisfaction) on a multi-dimensional measure of wellbeing (emotional, psychological and social) was examined via hierarchical regression. Age, gender, the play of casual games compared to shooters, and in-game experiences of flow, autonomy and relatedness were associated with increases in dimensions of wellbeing.

A cooperative game of a pair of learning automata

A cooperative game played in a sequential manner by a pair of learning automata is investigated in this paper. The automata operate in an unknown random environment which gives a common pay-off to the automata. Necessary and sufficient conditions on the functions in the reinforcement scheme are given for absolute monotonicity which enables the expected pay-off to be monotonically increasing in any arbitrary environment. As each participating automaton operates with no information regarding the other partner, the results of the paper are relevant to decentralized control.

Hakoah men's soccer team at a game against Graz Hasair; Vienna Portraits Men

Score 2:1 Hakoah; Names of players with numbers handwritten on verso

Location of Pathogenic Bacteria during Persistent Infections: Insights from an Analysis Using Game Theory

Bacterial persistent infections are responsible for a significant amount of the human morbidity and mortality. Unlike acute bacterial infections, it is very difficult to treat persistent bacterial infections (e.g. tuberculosis). Knowledge about the location of pathogenic bacteria during persistent infection will help to treat such conditions by designing novel drugs which can reach such locations. In this study, events of bacterial persistent infections were analyzed using game theory. A game was defined where the pathogen and the host are the two players with a conflict of interest. Criteria for the establishment of Nash equilibrium were calculated for this game. This theoretical model, which is very simple and heuristic, predicts that during persistent infections pathogenic bacteria stay in both intracellular and extracellular compartments of the host. The result of this study implies that a bacterium should be able to survive in both intracellular and extracellular compartments of the host in order to cause persistent infections. This explains why persistent infections are more often caused by intracellular pathogens like Mycobacterium and Salmonella. Moreover, this prediction is in consistence with the results of previous experimental studies.

Playing the policy game: A review of the barriers and enablers of nutrition policy change

- Objective To progress nutrition policy change and develop more effective advocates, it is useful to consider real-world factors and practical experiences of past advocacy efforts to determine the key barriers and enablers to nutrition policy change. This review aimed to identify and synthesize the enablers and barriers to public policy change within the field of nutrition. - Design Electronic databases were searched systematically for studies examining policymaking in public health nutrition. An interpretive synthesis was undertaken. Setting: International, national, state and local government jurisdictions within high-income, democratic countries. - Results Sixty-three studies were selected for inclusion. Numerous themes were identified explaining the barriers and enablers to policy change, all of which fell under the overarching category, ‘political will’, underpinned by a second major category, ‘public will’. Sub-themes, including pressure from industry; neoliberal ideology; use of emotions and values, and being visible were prevalent in describing links between public will, political will and policy change. - Conclusions The frustration around lack of public policy change in nutrition frequently stems from a belief that policymaking is a rational process in which evidence is used to assess the relative costs and benefits of options. The findings from this review confirm that evidence is only one component of influencing policy change. For policy change to occur there needs to be the political will, and often the public will, for the proposed policy problem and solution. This review presents a suite of enablers which can assist health professionals to influence political and public will in future advocacy efforts.

Cooperative game play with avatars and agents: Differences in brain activity and the experience of play

The current study sought to identify the impact of whether teammates in a cooperative videogame were controlled by other humans (avatars) or by the game (agents). The impact on player experience was explored through both subjective questionnaire measures and brain wave activity measurement (electroencephalography). Play with human teammates was associated with a greater sense of relatedness, but less competence and flow than play with other computer-controlled teammates. In terms of brain activity, play with human teammates was associated with greater activity in the alpha, theta and beta power bands than play with computer-controlled teammates. Overall, the results suggest that play with human teammates involves greater cognitive activity in terms of 'mentalising' than play with computer-controlled teammates. Additionally, the associations between subjective measures of player experience and brain activity are described. Limitations of the current study are identified and key directions for future research are discussed.

Grand Push Auto: A car based exertion game

Grand Push Auto is an exertion game in which players aim to push a full sized car to ever increasing speeds. The re-appropriation of a car as essentially a large weight allows us to create a highly portable and distributable exertion game in which the main game element has a weight of over 1000 kilograms. In this paper we discuss initial experiences with GPA, and present 3 questions for ongoing study which have been identified from our early testing: How might we appropriate existing objects in exertion game design, and does appropriation change how we think about these objects in different contexts, for example environmental awareness? How does this relate to more traditional sled based weight training? How can we create exertion games that allow truly brutal levels of force?

A cooperative game theoretic approach to prototype selection

In this paper we consider the task of prototype selection whose primary goal is to reduce the storage and computational requirements of the Nearest Neighbor classifier while achieving better classification accuracies. We propose a solution to the prototype selection problem using techniques from cooperative game theory and show its efficacy experimentally.

Experimental implementation of a three qubit quantum game with corrupt source using nuclear magnetic resonance quantum information processor

In a three player quantum `Dilemma' game each player takes independent decisions to maximize his/her individual gain. The optimal strategy in the quantum version of this game has a higher payoff compared to its classical counterpart. However, this advantage is lost if the initial qubits provided to the players are from a noisy source. We have experimentally implemented the three player quantum version of the `Dilemma' game as described by Johnson, [N.F. Johnson, Phys. Rev. A 63 (2001) 020302(R)] using nuclear magnetic resonance quantum information processor and have experimentally verified that the payoff of the quantum game for various levels of corruption matches the theoretical payoff. (c) 2007 Elsevier Inc. All rights reserved.

Two-agent cooperative search using game models with endurance-time constraints

In this article, the problem of two Unmanned Aerial Vehicles (UAVs) cooperatively searching an unknown region is addressed. The search region is discretized into hexagonal cells and each cell is assumed to possess an uncertainty value. The UAVs have to cooperatively search these cells taking limited endurance, sensor and communication range constraints into account. Due to limited endurance, the UAVs need to return to the base station for refuelling and also need to select a base station when multiple base stations are present. This article proposes a route planning algorithm that takes endurance time constraints into account and uses game theoretical strategies to reduce the uncertainty. The route planning algorithm selects only those cells that ensure the agent will return to any one of the available bases. A set of paths are formed using these cells which the game theoretical strategies use to select a path that yields maximum uncertainty reduction. We explore non-cooperative Nash, cooperative and security strategies from game theory to enhance the search effectiveness. Monte-Carlo simulations are carried out which show the superiority of the game theoretical strategies over greedy strategy for different look ahead step length paths. Within the game theoretical strategies, non-cooperative Nash and cooperative strategy perform similarly in an ideal case, but Nash strategy performs better than the cooperative strategy when the perceived information is different. We also propose a heuristic based on partitioning of the search space into sectors to reduce computational overhead without performance degradation.

Design of incentive compatible protocols for wireless networks: A game theoretic approach

In this thesis work, we design rigorous and efficient protocols/mechanisms for different types of wireless networks using a mechanism design [1] and game theoretic approach [2]. Our work can broadly be viewed in two parts. In the first part, we concentrate on ad hoc wireless networks [3] and [4]. In particular, we consider broadcast in these networks where each node is owned by independent and selfish users. Being selfish, these nodes do not forward the broadcast packets. All existing protocols for broadcast assume that nodes forward the transit packets. So, there is need for developing new broadcast protocols to overcome node selfishness. In our paper [5], we develop a strategy proof pricing mechanism which we call immediate predecessor node pricing mechanism (IPNPM) and an efficient new broadcast protocol based on IPNPM. We show the efficacy of our proposed broadcast protocol using simulation results.