Inductive Inference of Chess Player Strategy

Abstract Der Beitrag untersucht die Bedeutung des Schachspiels in Elias Canettis Roman Die Blendung zum einen auf der Ebene der historischen und sozialen Kontexte, in denen der Schachspieler Siegfried Fischer im Wien des frühen 20. Jahrhunderts steht. Er fokussiert zum anderen die Bedeutung des Schachspiels auf Handlungsebene. Denn Siegfried Fischer – genannt Fischerle – überträgt seine strategischen Fähigkeiten im Schach auf die ihn umgebende Welt und bringt so Peter Kien ,Zug um Zug‘ um dessen Reichtum.The article examines the significance of chess in Elias Canetti’s novel Die Blendung in the historical and social context of early 20th century Vienna. It further focuses on the function of chess within the novel: The actor and chess player Siegfried Fischer – called Fischerle – transfers his strategic skills from chess to his surroundings, thus depriving Peter Kien of his wealth ‘move by move’.

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Introduction

10.1093/oso/9780198747048.003.0001 ◽

2018 ◽

Author(s):

Jacob Stegenga

Keyword(s):

Medical Practice ◽

Evidence Based Medicine ◽

Inductive Inference ◽

Formal Theory ◽

Practice Research ◽

Evidence Based ◽

Master Argument ◽

Medical Interventions ◽

Concepts Of Disease ◽

Based Medicine

This chapter introduces the book, describes the key arguments of each chapter, and summarizes the master argument for medical nihilism. It offers a brief survey of prominent articulations of medical nihilism throughout history, and describes the contemporary evidence-based medicine movement, to set the stage for the skeptical arguments. The main arguments are based on an analysis of the concepts of disease and effectiveness, the malleability of methods in medical research, and widespread empirical findings which suggest that many medical interventions are barely effective. The chapter-level arguments are unified by our best formal theory of inductive inference in what is called the master argument for medical nihilism. The book closes by considering what medical nihilism entails for medical practice, research, and regulation.

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Deep learning investigation for chess player attention prediction using eye-tracking and game data

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications - ETRA '19 ◽

10.1145/3314111.3319827 ◽

2019 ◽

Author(s):

Justin Le Louedec ◽

Thomas Guntz ◽

James L. Crowley ◽

Dominique Vaufreydaz

Keyword(s):

Deep Learning ◽

Eye Tracking ◽

Chess Player

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Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems

Games ◽

10.3390/g12030062 ◽

2021 ◽

Vol 12 (3) ◽

pp. 62

Author(s):

Ralph S. Redden ◽

Greg A. Gagliardi ◽

Chad C. Williams ◽

Cameron D. Hassall ◽

Olave E. Krigolson

Keyword(s):

Reinforcement Learning ◽

Chess Player ◽

Reward Systems ◽

The Third ◽

Reward Positivity ◽

Eeg Data ◽

Attentional Engagement ◽

Level Of Difficulty ◽

Competitive Games ◽

Reinforcement Model

When we play competitive games, the opponents that we face act as predictors of the outcome of the game. For instance, if you are an average chess player and you face a Grandmaster, you anticipate a loss. Framed in a reinforcement learning perspective, our opponents can be thought of as predictors of rewards and punishments. The present study investigates whether facing an opponent would be processed as a reward or punishment depending on the level of difficulty the opponent poses. Participants played Rock, Paper, Scissors against three computer opponents while electroencephalographic (EEG) data was recorded. In a key manipulation, one opponent (HARD) was programmed to win most often, another (EASY) was made to lose most often, and the third (AVERAGE) had equiprobable outcomes of wins, losses, and ties. Through practice, participants learned to anticipate the relative challenge of a game based on the opponent they were facing that round. An analysis of our EEG data revealed that winning outcomes elicited a reward positivity relative to losing outcomes. Interestingly, our analysis of the predictive cues (i.e., the opponents’ faces) demonstrated that attentional engagement (P3a) was contextually sensitive to anticipated game difficulty. As such, our results for the predictive cue are contrary to what one might expect for a reinforcement model associated with predicted reward, but rather demonstrate that the neural response to the predictive cue was encoding the level of engagement with the opponent as opposed to value relative to the anticipated outcome.

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Naturalizing Logic: How Knowledge of Mechanisms Enhances Inductive Inference

Philosophies ◽

10.3390/philosophies6020052 ◽

2021 ◽

Vol 6 (2) ◽

pp. 52

Author(s):

Paul Thagard

Keyword(s):

Causal Inference ◽

Scientific Knowledge ◽

Inductive Inference ◽

Counterfactual Reasoning

This paper naturalizes inductive inference by showing how scientific knowledge of real mechanisms provides large benefits to it. I show how knowledge about mechanisms contributes to generalization, inference to the best explanation, causal inference, and reasoning with probabilities. Generalization from some A are B to all A are B is more plausible when a mechanism connects A to B. Inference to the best explanation is strengthened when the explanations are mechanistic and when explanatory hypotheses are themselves mechanistically explained. Causal inference in medical explanation, counterfactual reasoning, and analogy also benefit from mechanistic connections. Mechanisms also help with problems concerning the interpretation, availability, and computation of probabilities.

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