scholarly journals Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning

2019 ◽  
Vol 33 ◽  
pp. 4723-4730
Author(s):  
Mirko Polato ◽  
Fabio Aiolli
Keyword(s):  
Game Theory ◽  
Large Body ◽  
Rule Learning ◽  
Preference Learning ◽  
Learning Problem ◽  
Learning Framework ◽  
On Line ◽  
Game Theoretic ◽  
Classification Tasks ◽  
Zero Sum

A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incrementally include new useful features into the hypothesis. This can be particularly important when dealing with a very large number of potential features like, for instance, in relational learning and rule extraction. A game theoretical analysis is used to demonstrate the convergence of the algorithm. Furthermore, leveraging on the natural analogy between features and rules, the resulting models can be easily interpreted by humans. An extensive set of experiments on classification tasks shows the effectiveness of the proposed method in terms of interpretability and feature selection quality, with accuracy at the state-of-the-art.

scholarly journals A Novel Zero-Sum Polymatrix Game Theory Bidding Strategy for Power Supply Market

2019 ◽  
Vol 8 (2) ◽  
pp. 5669-5675
Keyword(s):  
Game Theory ◽  
Financial Risk ◽  
Optimal Solution ◽  
Theoretic Approach ◽  
Bidding Strategies ◽  
Load Demand ◽  
Novel Approach ◽  
Game Theoretic ◽  
Player Game ◽  
Zero Sum

The competitive power system market involves very high financial risk due to the essential requirements of real-time bidding decision making. Decisions once taken cannot be altered easily because multiple generators participate in bidding process while simultaneously dispatching to meet the load demand most economically. In order to avoid such risks it becomes pertinent to re-structure the bidding strategies from time to time to meet upcoming techno-economical challenges. In this paper, three generating units are studied using Matrix Laboratory software with a novel approach for deciding the best strategy from the most economical strategy viewpoint. A scenario of different formulations is created for muti-player game, which then is solved with the help of zero-sum polymatrix game theory. A systematic tabular layout of revenues pertaining to each formulation in terms of mixed strategies is developed. The minimax and maximin revenues, identified using Game theoretic approach, gave the most economical strategy. Thus exact and self-enforcing generalized method for best bidding strategies of all three generators are logically derived for the most optimal solution.

Legal Disputes Resolved via Game Theoretic Methods

2015 ◽  
Vol 17 (02) ◽  
pp. 1540015 ◽  
Author(s):  
T. E. S. Raghavan
Keyword(s):  
Game Theory ◽  
Solution Concept ◽  
Hourly Wage ◽  
Old Babylonian ◽  
Solution Methods ◽  
Game Theoretic ◽  
Person Game ◽  
Zero Sum ◽  
Mathematical Foundations ◽  
Non Cooperative Games

Mathematical foundations of conflict resolutions are deeply rooted in the theory of cooperative and non-cooperative games. While many elementary models of conflicts are formalized, one often raises the question whether game theory and its mathematically developed tools are applicable to actual legal disputes in practice. We choose an example from union management conflict on hourly wage dispute and how zero sum two person game theory can be used by a judge to bring about the need for realistic compromises between the two parties. We choose another example from the 2000-year old Babylonian Talmud to describe how a certain debt problem was resolved. While they may be unaware of cooperative game theory, their solution methods are fully consistent with the solution concept called the nucleolus of a TU game.

scholarly journals Adversarial Machine Learning with Double Oracle

2019 ◽  
Author(s):  
Kai Wang
Keyword(s):  
Machine Learning ◽  
Game Theory ◽  
Minimax Problem ◽  
Learning Problem ◽  
Zero Sum

We aim to improve the general adversarial machine learning solution by introducing the double oracle idea from game theory, which is commonly used to solve a sequential zero-sum game, where the adversarial machine learning problem can be formulated as a zero-sum minimax problem between learner and attacker.

Noncooperative Game Theory

2017 ◽  
Author(s):  
João P. Hespanha
Keyword(s):  
Game Theory ◽  
Computer Science ◽  
Dynamic Games ◽  
Design Methodology ◽  
Noncooperative Game ◽  
Noncooperative Game Theory ◽  
Original Design ◽  
Theoretical Perspectives ◽  
Engineering Designs ◽  
Zero Sum

This book is aimed at students interested in using game theory as a design methodology for solving problems in engineering and computer science. The book shows that such design challenges can be analyzed through game theoretical perspectives that help to pinpoint each problem's essence: Who are the players? What are their goals? Will the solution to “the game” solve the original design problem? Using the fundamentals of game theory, the book explores these issues and more. The use of game theory in technology design is a recent development arising from the intrinsic limitations of classical optimization-based designs. In optimization, one attempts to find values for parameters that minimize suitably defined criteria—such as monetary cost, energy consumption, or heat generated. However, in most engineering applications, there is always some uncertainty as to how the selected parameters will affect the final objective. Through a sequential and easy-to-understand discussion, the book examines how to make sure that the selection leads to acceptable performance, even in the presence of uncertainty—the unforgiving variable that can wreck engineering designs. The book looks at such standard topics as zero-sum, non-zero-sum, and dynamic games and includes a MATLAB guide to coding. This book offers students a fresh way of approaching engineering and computer science applications.

scholarly journals Interactive effects of incentive value and valence on the performance of discrete action sequences

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tyler J. Adkins ◽  
Bradley S. Gary ◽  
Taraz G. Lee
Keyword(s):  
Motor Learning ◽  
Large Body ◽  
Interactive Effects ◽  
Motor Tasks ◽  
Skill Retention ◽  
Incentive Value ◽  
Discrete Action ◽  
On Line ◽  
And Performance ◽  
Gains And Losses

AbstractIncentives can be used to increase motivation, leading to better learning and performance on skilled motor tasks. Prior work has shown that monetary punishments enhance on-line performance while equivalent monetary rewards enhance off-line skill retention. However, a large body of literature on loss aversion has shown that losses are treated as larger than equivalent gains. The divergence between the effects of punishments and reward on motor learning could be due to perceived differences in incentive value rather than valence per se. We test this hypothesis by manipulating incentive value and valence while participants trained to perform motor sequences. Consistent with our hypothesis, we found that large reward enhanced on-line performance but impaired the ability to retain the level of performance achieved during training. However, we also found that on-line performance was better with reward than punishment and that the effect of increasing incentive value was more linear with reward (small, medium, large) while the effect of value was more binary with punishment (large vs not large). These results suggest that there are differential effects of punishment and reward on motor learning and that these effects of valence are unlikely to be driven by differences in the subjective magnitude of gains and losses.

Development of Game Theoretic Protocols for Multilevel Design

2010 ◽  
Author(s):  
Ayan Sinha ◽  
Farrokh Mistree ◽  
Janet K. Allen
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Geometric Modeling ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Level ◽  
Engineering Teams ◽  
Game Theoretic ◽  
Multiple Levels ◽  
The Relationship

The effectiveness of the use of game theory in addressing multi-objective design problems has been illustrated. For the most part, researchers have focused on design problems at single level. In this paper, we illustrate the efficacy of using game theoretic protocols to model the relationship between multidisciplinary engineering teams and facilitate decision making at multiple levels. We will illustrate the protocols in the context of an underwater vehicle with three levels that span material and geometric modeling associated with microstructure mediated design of the material and vehicle.

MONTE CARLO METHODS IN FUZZY GAME THEORY

2007 ◽  
Vol 03 (02) ◽  
pp. 259-269 ◽  
Author(s):  
AREEG ABDALLA ◽  
JAMES BUCKLEY
Keyword(s):  
Game Theory ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Monte Carlo Methods ◽  
Mixed Strategy ◽  
Minimax Theorem ◽  
Approximate Solutions ◽  
Mixed Strategies ◽  
Fuzzy Game ◽  
Zero Sum

In this paper, we consider a two-person zero-sum game with fuzzy payoffs and fuzzy mixed strategies for both players. We define the fuzzy value of the game for both players [Formula: see text] and also define an optimal fuzzy mixed strategy for both players. We then employ our fuzzy Monte Carlo method to produce approximate solutions, to an example fuzzy game, for the fuzzy values [Formula: see text] for Player I and [Formula: see text] for Player II; and also approximate solutions for the optimal fuzzy mixed strategies for both players. We then look at [Formula: see text] and [Formula: see text] to see if there is a Minimax theorem [Formula: see text] for this fuzzy game.

scholarly journals Transformer-CNN: Fast and Reliable Tool for QSAR

2019 ◽  
Author(s):  
Pavel Karpov ◽  
Guillaume Godin ◽  
Igor Tetko
Keyword(s):  
Transfer Learning ◽  
Source Code ◽  
Future Directions ◽  
Reliable Tool ◽  
Benchmark Datasets ◽  
On Line ◽  
Classification Tasks ◽  
Transformer Model

We present SMILES-embeddings derived from internal encoder state of a Transformer model trained to canonize SMILES as a Seq2Seq problem. Using CharNN architecture upon the embeddings results in a higher quality QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis grounds on an internal consensus. Both the augmentation and transfer learning based on embedding allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings are available on https://github.com/bigchem/transformer-cnn, whereas the OCHEM environment (https://ochem.eu) hosts its on-line implementation.

The philosophical interpretation of language game theory

2021 ◽  
Author(s):  
Nick Zangwill
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Language Game ◽  
Linguistic Meaning ◽  
Theoretic Approach ◽  
Philosophical Interpretation ◽  
Game Theoretic ◽  
Game Theoretic Approach

Abstract I give an informal presentation of the evolutionary game theoretic approach to the conventions that constitute linguistic meaning. The aim is to give a philosophical interpretation of the project, which accounts for the role of game theoretic mathematics in explaining linguistic phenomena. I articulate the main virtue of this sort of account, which is its psychological economy, and I point to the casual mechanisms that are the ground of the application of evolutionary game theory to linguistic phenomena. Lastly, I consider the objection that the account cannot explain predication, logic, and compositionality.

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