Search results for "potential game"
showing 7 items of 7 documents
A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids
2017
In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. In our specific instantiation, we consider the scenario when the power system has a local-area Smart Grid subnet comprising of a single power source and multiple customers. The objective of the exercise is to tacitly control the total power consumption of the customers’ shiftable loads, so to approach the rigid power budget determined by the power source, but to simultaneously not exceed this threshold. As opposed to the…
Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme
2017
In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. From a modeling perspective, the distributed scheduling problem is formulated as a game, and in particular, a so-called “Potential” game. This game has at least one pure strategy Nash Equilibrium (NE), and we demonstrate that the NE point is a global optimal point. The solution that we propose, which is the pioneering solution that incorporates the theory of Learning Automata (LA), permits the total supplied loads to approach the p…
Toward a Collective Agenda on AI for Earth Science Data Analysis
2021
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to advance the modeling and understanding of the Earth system. Despite such great opportunities, we also observed a worrying tendency to remain in disciplinary comfort zones applying recent advances from artificial intelligence on well resolved remote sensing problems. Here we take a position on research directions where we think the interface between these fields will have the most impact and be…
Consensus in Noncooperative Dynamic Games: a Multi-Retailer Inventory Application
2008
We focus on Nash equilibria and Pareto optimal Nash equilibria for a finite horizon noncooperative dynamic game with a special structure of the stage cost. We study the existence of these solutions by proving that the game is a potential game. For the single-stage version of the game, we characterize the aforementioned solutions and derive a consensus protocol that makes the players converge to the unique Pareto optimal Nash equilibrium. Such an equilibrium guarantees the interests of the players and is also social optimal in the set of Nash equilibria. For the multistage version of the game, we present an algorithm that converges to Nash equilibria, unfortunately, not necessarily Pareto op…
A Bayesian Learning Automata-Based Distributed Channel Selection Scheme for Cognitive Radio Networks
2014
We consider a scenario where multiple Secondary Users SUs operate within a Cognitive Radio Network CRN which involves a set of channels, where each channel is associated with a Primary User PU. We investigate two channel access strategies for SU transmissions. In the first strategy, the SUs will send a packet directly without operating Carrier Sensing Medium Access/Collision Avoidance CSMA/CA whenever a PU is absent in the selected channel. In the second strategy, the SUs implement CSMA/CA to further reduce the probability of collisions among co-channel SUs. For each strategy, the channel selection problem is formulated and demonstrated to be a so-called "Potential" game, and a Bayesian Lea…
Theoretical Game Approach for Mobile Users Resource Management in a Vehicular Fog Computing Environment
2018
Vehicular Cloud Computing (VCC) is envisioned as a promising approach to increase computation capabilities of vehicle devices for emerging resource-hungry mobile applications. In this paper, we introduce the new concept of Vehicular Fog Computing (VFC). The Fog Computing (FC) paradigm evolved and is employed to enhance the quality of cloud computing services by extending it to the edge of the network using one or more collaborative end-user clients or near-user edge devices. The VFC is similar to the VCC concept but uses vehicles resources located at the edge of the network in order to serve only local on-demand mobile applications. The aim of this paper is to resolve the problem of admissi…
Identifying the Impact of Game Music both Within and Beyond Gameplay
2021
This paper presents an overview of and a brief critical reflection on game music’s impact on players both within and beyond the context of gameplay. The analysis is based both on the current literature as well as on preliminary (work-in-progress) observations of our research project Game Music Everyday Memories. We consider how the functions and uses of game music potentially extend to people’s everyday life, thus constituting a personally and culturally meaningful relationship with music that is not immediately connected to gameplay. On the other hand, we consider the ways game music and a person’s attachment to the music are involved in gameplay motivation and potential game retention. As…