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This paper presents a novel approach for predicting game outcomes using Cumulative Distribution Networks (CDNs) and the Derivative-Sum-Product (DSP) algorithm. By modeling complex dependencies and stochastic orderings inherent in multiplayer gaming scenarios, we demonstrate how CDNs can effectively characterize the cumulative distribution functions of player performances. The study highlights necessary and sufficient conditions for CDN functions, discusses conditioning and marginalization, and illustrates how these methodologies can enhance skill updates and rankings in games like Halo 2.
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Cumulative Distribution Networks and the Derivative-Sum-Product Algorithm Jim C. Huang and Brendan J. Frey Probabilistic and Statistical Inference Group, Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada UAI 2008
Motivation e.g.: Predicting game outcomes in Halo 2 • Problems where density models may be intractable • e.g.: Modelling arbitrary dependencies • e.g.: Modelling stochastic orderings • Cumulative distribution network (CDN) UAI 2008
Cumulative distribution networks (CDNs) • Graphical model of the cumulative distribution function (CDF) • Example: UAI 2008
Cumulative distribution functions Negative convergence • Marginalization maximization • Conditioning differentiation Positive convergence Monotonicity UAI 2008
Necessary/sufficient conditions on CDN functions • Negative convergence (necessity and sufficiency): • Positive convergence (sufficiency): For each Xk, at least one neighboring function 0 All functions 1 UAI 2008
Necessary/sufficient conditions on CDN functions • Monotonicity lemma (sufficiency): All functions monotonically non-decreasing… Sufficient condition for a valid joint CDF: Each CDN function can be a CDF of its arguments UAI 2008
Marginal independence • Marginalization maximization • e.g.: X is marginally independent of Y UAI 2008
Conditional independence • Conditioning differentiation • e.g.: X and Y are conditionally dependent given Z • e.g.: X and Y are conditionally independent given Z • Conditional independence No paths contain observed variables UAI 2008
A toy example Required “Bayes net” Markov random fields Check: UAI 2008
Inference by message passing • Conditioning differentiation • Replace sum in sum-product with differentiation • Recursively apply product rule via message-passing with messages , • Derivative-Sum-Product (DSP) … UAI 2008
Derivative-sum-product • In a CDN: • In a factor graph: UAI 2008
Ranking in multiplayer gaming Player skill functions Player performance Team performance • e.g.: Halo 2 game with 7 players, 3 teams Given game outcomes, update player skills as a function of all player/team performances UAI 2008
Ranking in multiplayer gaming = Local cumulative model linking team rank rn with player performances xn e.g.: Team 2 has rank 2 UAI 2008
Ranking in multiplayer gaming = Pairwise model of team ranks rn,rn+1 Enforce stochastic orderings between teams via h UAI 2008
Ranking in multiplayer gaming • CDN functions = Gaussian CDFs • Skill updates: • Prediction: UAI 2008
Results • Previous methods for ranking players: • ELO (Elo, 1978) • TrueSkill (Graepel, Minka and Herbrich, 2006) • After message-passing… UAI 2008
Summary • The CDN as a graphical model for CDFs • Unique conditional independence structure • Marginalization maximization • Global normalization can be enforced locally • Conditioning differentiation • Efficient inference with Derivative-Sum-Product • Application to Halo 2 Beta Dataset UAI 2008
Discussion • Need to be careful when applying to ordinal discrete variables… • Principled method for learning CDNs • Variational principle? (loopy DSP seems to work well) • Future applications to • Hypothesis testing • Document retrieval • Collaborative filtering • Biological sequence search • … UAI 2008
Thanks • Questions? UAI 2008
Interpretation of skill updates • For any given player let denote the outcomes of games he/she has played previously • Then the skill function corresponds to UAI 2008