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IJCAI
2001
15 years 8 months ago
Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning
Most formulations of Reinforcement Learning depend on a single reinforcement reward value to guide the search for the optimal policy solution. If observation of this reward is rar...
Gregory Z. Grudic, Lyle H. Ungar
AI
2008
Springer
15 years 6 months ago
Sequential Monte Carlo in reachability heuristics for probabilistic planning
The current best conformant probabilistic planners encode the problem as a bounded length CSP or SAT problem. While these approaches can find optimal solutions for given plan leng...
Daniel Bryce, Subbarao Kambhampati, David E. Smith
CJ
2008
97views more  CJ 2008»
15 years 6 months ago
Three Kinds of Probabilistic Induction: Universal Distributions and Convergence Theorems
We will describe three kinds of probabilistic induction problems, and give general solutions for each , with associated convergence theorems that show they tend to give good proba...
Ray J. Solomonoff
MP
2006
107views more  MP 2006»
15 years 6 months ago
Convergence theory for nonconvex stochastic programming with an application to mixed logit
Monte Carlo methods have been used extensively in the area of stochastic programming. As with other methods that involve a level of uncertainty, theoretical properties are required...
Fabian Bastin, Cinzia Cirillo, Philippe L. Toint
PAMI
2006
164views more  PAMI 2006»
15 years 6 months ago
A Binary Linear Programming Formulation of the Graph Edit Distance
A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. ...
Derek Justice, Alfred O. Hero