No abstract

No abstract

No abstract

Abstract⎯Classical game theory is concerned with how rational players make decisions when they are faced with known payoffs. In the past decade, Fuzzy Logic has been widely used to manage uncertainties in games. In this paper, we employ fuzzy logic to determine the priority of a payoff to other payoffs. A new term is introduced to measure the preference of one payoff to others. By this means a fuzzy preference relation is constructed and using a least deviation method, the priority of every payoff for each player is calculated and the relation of this value with the degree of being Nash is discussed. In the second part of the paper, games with fuzzy payoffs and fuzzy satisfaction functions (SF), satisfaction degree from each payoff, are considered and a new method for analyzing these games is proposed. In this regard we calculate the similarity between SF and payoffs and make a crisp game from the fuzzy game and apply our mentioned method to analyze that game. Compared to the previous generalization, our method has more sensitivity to the slight alternation of payoffs and yields more realistic results. We also studied the effect of players' greediness, modeled by the SF, on the game's equilibriums.

Crowd sensing is an approach to collect many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals may provide high-quality and high-coverage measurements of a phenomena. In this work, we propose an incentive assignment mechanism for crowd sensing variable phenomena (e.g., temperature) that balances the goal of maximizing coverage of the area of interest, while at the same time staying within a budget constraint. This algorithm not only takes into account the area covered by the participants' sensors, but also the spread of these sensors through a target area. This characteristic enables more representative sampling than existing methods, assuming the same budget. Compared to existing methods, this algorithm improves the spread of the set of acquired samples by more than 56 % percent, without sacrificing the number of samples purchased from human sensors.