97 citations · 266 across the 25 of their papers we have counts for
8 papers · 1 filter
Approximate Inference for Stochastic Planning in Factored Spaces
Zhennan Wu, Roni Khardon
Stochastic planning can be reduced to probabilistic inference in large discrete graphical models, but hardness of inference requires approximation schemes to be used. In this paper…
Stochastic Planning and Lifted Inference
Roni Khardon, Scott Sanner
Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorit…
The Complexity of Reasoning with FODD and GFODD
Benjamin J. Hescott, Roni Khardon
Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a knowledge representation that is useful in mechanizing decision theoretic planning in relational domai…
Probabilistic Relational Planning with First Order Decision Diagrams
Saket Joshi, Roni Khardon
Dynamic programming algorithms have been successfully applied to propositional stochastic planning problems by using compact representations, in particular algebraic decision diagr…
Solving Relational MDPs with Exogenous Events and Additive Rewards
S. Joshi, R. Khardon, P. Tadepalli +2
We formalize a simple but natural subclass of service domains for relational planning problems with object-centered, independent exogenous events and additive rewards capturing, fo…
Policy Iteration for Relational MDPs
Chenggang Wang, Roni Khardon
Relational Markov Decision Processes are a useful abstraction for complex reinforcement learning problems and stochastic planning problems. Recent work developed representation sch…
First Order Decision Diagrams for Relational MDPs
Chenggang Wang, Saket Joshi, Roni Khardon
Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient r…
Translating between Horn Representations and their Characteristic Models
R. Khardon
Characteristic models are an alternative, model based, representation for Horn expressions. It has been shown that these two representations are incomparable and each has its advan…