scholarly journals Decentralized Anti-coordination Through Multi-agent Learning

2013 ◽  
Vol 47 ◽  
pp. 441-473 ◽  
Author(s):  
L. Cigler ◽  
B. Faltings
Keyword(s):  
Resource Allocation ◽  
Nash Equilibria ◽  
Learning Algorithm ◽  
Channel Allocation ◽  
Pure Strategy ◽  
Correlated Equilibrium ◽  
Agent Learning ◽  
Multi Agent ◽  
Resource Allocation Game ◽  
The One

To achieve an optimal outcome in many situations, agents need to choose distinct actions from one another. This is the case notably in many resource allocation problems, where a single resource can only be used by one agent at a time. How shall a designer of a multi-agent system program its identical agents to behave each in a different way? From a game theoretic perspective, such situations lead to undesirable Nash equilibria. For example consider a resource allocation game in that two players compete for an exclusive access to a single resource. It has three Nash equilibria. The two pure-strategy NE are efficient, but not fair. The one mixed-strategy NE is fair, but not efficient. Aumann's notion of correlated equilibrium fixes this problem: It assumes a correlation device that suggests each agent an action to take. However, such a "smart" coordination device might not be available. We propose using a randomly chosen, "stupid" integer coordination signal. "Smart" agents learn which action they should use for each value of the coordination signal. We present a multi-agent learning algorithm that converges in polynomial number of steps to a correlated equilibrium of a channel allocation game, a variant of the resource allocation game. We show that the agents learn to play for each coordination signal value a randomly chosen pure-strategy Nash equilibrium of the game. Therefore, the outcome is an efficient correlated equilibrium. This CE becomes more fair as the number of the available coordination signal values increases.

scholarly journals Improving Multi-agent Coordination by Learning to Estimate Contention

2021 ◽  
Author(s):  
Panayiotis Danassis ◽  
Florian Wiedemair ◽  
Boi Faltings
Keyword(s):  
Large Scale ◽  
Learning Algorithm ◽  
Coordination Mechanism ◽  
Large Scale Systems ◽  
Fast Learning ◽  
Agent Learning ◽  
Meeting Scheduling ◽  
Multi Agent ◽  
Device Deployment ◽  
Agent Coordination

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need for mutually consistent actions) by relying on the ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning is decentralized, observes only own action/reward pairs, requires no inter-agent communication, and achieves near-optimal (<5% loss) and fair coordination in a variety of synthetic scenarios and a real-world meeting scheduling problem. The lightweight nature and fast learning constitute ALMA-Learning ideal for on-device deployment.

scholarly journals Distributed Multichannel Access in High-Frequency Diversity Networks: A Multi-Agent Learning Approach With Correlated Equilibrium

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 85581-85593
Author(s):  
Wen Li ◽  
Yuhua Xu ◽  
Yunpeng Cheng ◽  
Yang Yang ◽  
Xueqiang Chen ◽  
...  
Keyword(s):  
High Frequency ◽  
Correlated Equilibrium ◽  
Learning Approach ◽  
Frequency Diversity ◽  
Agent Learning ◽  
Multi Agent ◽  
Multichannel Access

scholarly journals Multiagent Learning in Large Anonymous Games

2011 ◽  
Vol 40 ◽  
pp. 571-598 ◽  
Author(s):  
I. A. Kash ◽  
E. J. Friedman ◽  
J. Y. Halpern
Keyword(s):  
Nash Equilibria ◽  
Learning Algorithms ◽  
Efficient Algorithms ◽  
Statistical Information ◽  
Multiagent Learning ◽  
Alternative Approach ◽  
Agent Learning ◽  
Multi Agent ◽  
Large Systems ◽  
Anonymous Games

In large systems, it is important for agents to learn to act effectively, but sophisticated multi-agent learning algorithms generally do not scale. An alternative approach is to find restricted classes of games where simple, efficient algorithms converge. It is shown that stage learning efficiently converges to Nash equilibria in large anonymous games if best-reply dynamics converge. Two features are identified that improve convergence. First, rather than making learning more difficult, more agents are actually beneficial in many settings. Second, providing agents with statistical information about the behavior of others can significantly reduce the number of observations needed.

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

2005 ◽  
Author(s):  
Jonathan Bredin ◽  
Rajiv T. Maheswaran ◽  
Cagri Imer ◽  
Tamer Basar ◽  
David Kotz ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Multi Agent ◽  
Game Theoretic

scholarly journals Expressiveness and Nash Equilibrium in Iterated Boolean Games

2021 ◽  
Vol 22 (2) ◽  
pp. 1-38
Author(s):  
Julian Gutierrez ◽  
Paul Harrenstein ◽  
Giuseppe Perelli ◽  
Michael Wooldridge
Keyword(s):  
Nash Equilibrium ◽  
Nash Equilibria ◽  
Infinite Sequence ◽  
Multi Agent Systems ◽  
Temporal Logics ◽  
Agent Systems ◽  
Temporal Properties ◽  
Multi Agent ◽  
Game Theoretic ◽  
Boolean Games

We define and investigate a novel notion of expressiveness for temporal logics that is based on game theoretic equilibria of multi-agent systems. We use iterated Boolean games as our abstract model of multi-agent systems [Gutierrez et al. 2013, 2015a]. In such a game, each agent  has a goal  , represented using (a fragment of) Linear Temporal Logic ( ) . The goal  captures agent  ’s preferences, in the sense that the models of  represent system behaviours that would satisfy  . Each player controls a subset of Boolean variables , and at each round in the game, player is at liberty to choose values for variables in any way that she sees fit. Play continues for an infinite sequence of rounds, and so as players act they collectively trace out a model for , which for every player will either satisfy or fail to satisfy their goal. Players are assumed to act strategically, taking into account the goals of other players, in an attempt to bring about computations satisfying their goal. In this setting, we apply the standard game-theoretic concept of (pure) Nash equilibria. The (possibly empty) set of Nash equilibria of an iterated Boolean game can be understood as inducing a set of computations, each computation representing one way the system could evolve if players chose strategies that together constitute a Nash equilibrium. Such a set of equilibrium computations expresses a temporal property—which may or may not be expressible within a particular fragment. The new notion of expressiveness that we formally define and investigate is then as follows: What temporal properties are characterised by the Nash equilibria of games in which agent goals are expressed in specific fragments of  ? We formally define and investigate this notion of expressiveness for a range of fragments. For example, a very natural question is the following: Suppose we have an iterated Boolean game in which every goal is represented using a particular fragment of : is it then always the case that the equilibria of the game can be characterised within ? We show that this is not true in general.

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

scholarly journals User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Giuseppe Caso ◽  
Ozgu Alay ◽  
Guido Carlo Ferrante ◽  
Luca De Nardis ◽  
Maria-Gabriella Di Benedetto ◽  
...  
Keyword(s):  
Game Theory ◽  
Learning Algorithms ◽  
Technology Selection ◽  
Access Technology ◽  
Radio Access Technology ◽  
Radio Access ◽  
Agent Learning ◽  
Multi Agent ◽  
User Centric ◽  
Radio Access Technology Selection
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