# Introduction to the special issue on learning and computational game theory

@article{Greenwald2007IntroductionTT, title={Introduction to the special issue on learning and computational game theory}, author={Amy Greenwald and Michael L. Littman}, journal={Machine Learning}, year={2007}, volume={67}, pages={3-6} }

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…

## 12 Citations

### On Similarities between Inference in Game Theory and Machine Learning

- Computer ScienceJ. Artif. Intell. Res.
- 2008

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.

### Existence, convergence and efficiency analysis of nash equilibrium and its applications

- Economics
- 2016

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,…

### Reinforcement Learning Algorithms for Uncertain, Dynamic, Zero-Sum Games

- Computer Science2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
- 2018

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.

### Statistical Prediction of the Outcome of a Noncooperative Game

- Economics
- 2008

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 profiles…

### Statistical prediction of the outcome of a game

- Economics
- 2008

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 strategy…

### Multi-Agent Learning II: Algorithms

- Computer ScienceEncyclopedia of Machine Learning
- 2010

Neither the problem definition for mutli-agent learning, nor the algorithms offered, follow in a straightforward way from the single-agent case.

### Multi-Agent Learning I: Problem Definition

- Computer ScienceEncyclopedia of Machine Learning
- 2010

Neither the problem definition for mutli-agent learning, nor the algorithms offered, follow in a straightforward way from the single-agent case.

### ESSENTIALS OF GAME THEORY

- Computer Science, Education
- 2007

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 future…

### Learning Through Hypothesis Refinement Using Answer Set Programming

- Computer ScienceILP
- 2013

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.

### A Game-Based Price Bidding Algorithm for Multi-Attribute Cloud Resource Provision

- Computer ScienceIEEE 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.

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