'Optimal strategy' diaporamas de présentation

Online Algorithms Lecture notes for lectures given by Dr. Ely Porat, Bar-Ilan University

Online Algorithms Lecture notes for lectures given by Dr. Ely Porat, Bar-Ilan University. Notes taken by: Navot Akiva Yair Kaufman Raz Lin Ohad Lipsky . July 2001. Examples. The Investor Problem:

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Adversarial Search and Game Playing

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Increased Robustness in Spoken Dialog Systems

Increased Robustness in Spoken Dialog Systems. (roadmap to a thesis proposal) Dan Bohus, SPHINX Lunch, May 2003. The problem. S: Are you a registered user ? U: No I'm not. No [NO I'M NOT NO] S: What is your full name ? U: Adam Schumacher [ATHENS IN AKRON]

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Adversarial Search: Games

Adversarial Search: Games. Chapter 6 Feb 21 2007. What are, why study games?. Games are a form of multi-agent environment What do other agents do and how do they affect our success? Competitive multi-agent environments give rise to adversarial search ( games ) Why study games?

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Chapter 19 Probabilistic Dynamic Programming

Chapter 19 Probabilistic Dynamic Programming. to accompany Operations Research: Applications and Algorithms 4th edition by Wayne L. Winston. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Description.

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Module I: Investment Banking: Mergers and Acquisitions

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Evolution: Games, dynamics and algorithms

Evolution: Games, dynamics and algorithms. Karen Page Bioinformatics Unit Dept. of Computer Science, UCL. Evolution. Darwinian evolution is based on three fundamental principles: reproduction, mutation and selection

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Chapter 5 Finance Application

Chapter 5 Finance Application. (i) Simple Cash Balance Problem (ii) Optimal Equity Financing of a corporation (iii) Stochastic Application Cash Balance Model To determine optimal cash levels to meet the demand for cash at minimum total discounted cost.

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Oligopolies and monopolistic competition

Oligopolies and monopolistic competition. In between the two competition benchmarks . Oligopolies and monopolistic competition. Today we finish examining the competition continuum we introduced last week

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Hat Guessing Games

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Hat Guessing Games

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Game Theory

Game Theory. Developed to explain the optimal strategy in two-person interactions. Initially, von Neumann and Morganstern Zero-sum games John Nash Nonzero-sum games Harsanyi, Selten Incomplete information. An example: Big Monkey and Little Monkey. Monkeys usually eat ground-level fruit

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Biological Substrates of Personality

Biological Substrates of Personality. Central nervous system structure and function. Frontotemporal lobar degeneration ? Personality change (more apathetic, less social, more emotionally blunted). Eysenck Returns: Extraversion. Central nervous system structure and function.

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EMGT 501 Fall 2008 Midterm Exam Due Day: October 21, 2008

EMGT 501 Fall 2008 Midterm Exam Due Day: October 21, 2008. Note : (a) Do not send me after copying your computer results. Answer what are your decision variables, formulation and solution, only. See my HW answer on my HP.

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Contracting with Imperfect Commitment and the Revelation Principle: The Single Agent Case

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Games & Adversarial Search

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Learning in games

Learning in games. Vincent Conitzer conitzer@cs.duke.edu. Learning in (normal-form) games. Approach we have taken so far when playing a game: just compute an optimal/equilibrium strategy Another approach: learn how to play a game by playing it many times, and

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Outline: Independence. Odds ratios. Random variables. Distribution function, pmf, density. Expected value .

Outline: Independence. Odds ratios. Random variables. Distribution function, pmf, density. Expected value . Independence: P(B | A) = P(B) (and vice versa) [so, when independent, P(A&B) = P(A)P(B|A) = P(A)P(B).] Reasonable to assume the following are independent:

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How do you model the future?

How do you model the future?. Stochastic approach: The future can be modeled as a distribution over possible events. Very successful in many contexts. Alternative: Think of the future as an adversary, do well against all possible future outcomes. Toy Example: Ski Optimization.

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Monopoly Agent Strategy Simulation

Nicholas Loffredo. Monopoly Agent Strategy Simulation. My project is to make a simulation of Monopoly with agents that can negotiate with each other, buy titles and houses/hotels, mortgage properties, etc. and find the optimal strategy for Monopoly. Overview. Current features:

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