Introduction to the special issue on learning and computational game theory

  title={Introduction to the special issue on learning and computational game theory},
  author={Amy Greenwald and Michael L. Littman},
  journal={Machine Learning},
Game theory is concerned with the decision making of utility-maximizing individuals in their interactions with one another and their environment. From its earliest days of study, researchers have recognized the important relationship between game theory and learning— using experience from past play to guide future decisions. Recently, there has been a surge in research that applies a computational perspective to learning in general-sum games. The editors have been involved with such projects… Expand
On Similarities between Inference in Game Theory and Machine Learning
The equivalence between inference in game theory and machine learning is elucidated, and an equivalent vocabulary between the two domains is established so as to facilitate developments at the intersection of both fields. Expand
Existence, convergence and efficiency analysis of nash equilibrium and its applications
Game theory deals with strategic interactions among multiple players, where each player tries to maximize/minimize its utility/cost. It has been applied in a broad array of areas such as economics,Expand
Reinforcement Learning Algorithms for Uncertain, Dynamic, Zero-Sum Games
A novel algorithm, based on heterogeneous games of learning automata (HEGLA), as well as algorithms based on model-based and model-free reinforcement learning, are presented as possible approaches to learning the solution Markov equilibrium policies when they are assumed to satisfy the sufficient conditions for existence. Expand
Statistical Prediction of the Outcome of a Noncooperative Game
Conventionally, game theory predicts that the mixed strategy profile of players in a noncooperative game will satisfy some equilibrium concept. Relative probabil- ities of the strategy profilesExpand
Statistical prediction of the outcome of a game
Many machine learning problems involve predicting the joint strategy choice of some goaldirected “players” engaged in a noncooperative game. Conventional game theory predicts that that joint strategyExpand
Multi-Agent Learning II: Algorithms
Neither the problem definition for mutli-agent learning, nor the algorithms offered, follow in a straightforward way from the single-agent case. Expand
Multi-Agent Learning I: Problem Definition
Neither the problem definition for mutli-agent learning, nor the algorithms offered, follow in a straightforward way from the single-agent case. Expand
Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help futureExpand
Learning Through Hypothesis Refinement Using Answer Set Programming
A new meta-level learning approach is proposed that overcomes the scalability problem of ASPAL by breaking the learning process up into small manageable steps and using theory revision over the meta- level representation of the hypothesis space to improve the hypothesis computed at each step. Expand
A Game-Based Price Bidding Algorithm for Multi-Attribute Cloud Resource Provision
  • Junyan Hu, Kenli Li, Chubo Liu, Keqin Li
  • Computer Science
  • IEEE Transactions on Services Computing
  • 2021
This work proposes a novel and incentive resource provision model referring to the Quality-of-Service (QoS) and the bidding price, and demonstrates the existence of Nash equilibrium solution set for the formulated game model by assuming that the quantity function of provided resources from every provider is continuous. Expand


Calibrated Learning and Correlated Equilibrium
For each correlated equilibrium there is some calibrated learning rule that the players can use which results in their playing this correlated equilibrium in the limit, and the statistical concept of a calibration is strongly related to the game theoretic concept of correlated equilibrium. Expand
The Complexity of Computing a Nash Equilibrium
It is shown that finding a Nash equilibrium in three-player games is indeed PPAD-complete; and this result is resolved by a reduction from Brouwer's problem, thus establishing that the two problems are computationally equivalent. Expand
Multiagent learning using a variable learning rate
This article introduces the WoLF principle, “Win or Learn Fast”, for varying the learning rate, and examines this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games. Expand
Bounds for Regret-Matching Algorithms
A general class of learning algorithms, regret-matching algorithms, and a regret-based framework for analyzing their performance in online decision problems are introduced, based on a set Φ of transformations over the set of actions. Expand
If multi-agent learning is the answer, what is the question?
The goal of this article is to start a discussion in the research community that will result in firmer foundations for the area of learning in multi-agent systems. Expand
Graphical Models for Game Theory
The main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in one-stage games represented by trees or sparse graphs. Expand
Risk analysis of the space shuttle: Pre-Challenger prediction of failure
Abstract The Rogers Commission report on the space shuttle Challenger accident concluded that the accident was caused by a combustion gas leak through a joint in one of the booster rockets, which wasExpand