scholarly journals Heuristics based on projection occupation measures for probabilistic planning with dead-ends and risk

2020 ◽  
Author(s):  
Milton Raúl Condori Fernández
Keyword(s):  
Probabilistic Planning ◽  
Occupation Measures

scholarly journals Discrete-Time Constrained Average Stochastic Games with Independent State Processes

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1089
Author(s):  
Wenzhao Zhang
Keyword(s):  
Discrete Time ◽  
Stochastic Games ◽  
Transition Probability ◽  
Existence Condition ◽  
Linear Program ◽  
Global Minimizer ◽  
Independent State ◽  
Occupation Measures ◽  
Best Response ◽  
Mathematic Program

In this paper, we consider the discrete-time constrained average stochastic games with independent state processes. The state space of each player is denumerable and one-stage cost functions can be unbounded. In these game models, each player chooses an action each time which influences the transition probability of a Markov chain controlled only by this player. Moreover, each player needs to pay some costs which depend on the actions of all the players. First, we give an existence condition of stationary constrained Nash equilibria based on the technique of average occupation measures and the best response linear program. Then, combining the best response linear program and duality program, we present a non-convex mathematic program and prove that each stationary Nash equilibrium is a global minimizer of this mathematic program. Finally, a controlled wireless network is presented to illustrate our main results.

scholarly journals Convex optimization of nonlinear feedback controllers via occupation measures

2014 ◽  
Vol 33 (9) ◽  
pp. 1209-1230 ◽  
Author(s):  
Anirudha Majumdar ◽  
Ram Vasudevan ◽  
Mark M. Tobenkin ◽  
Russ Tedrake
Keyword(s):  
Convex Optimization ◽  
Nonlinear Feedback ◽  
Feedback Controllers ◽  
Occupation Measures

Finding Feasible Policies for Extreme Risk-Averse Agents in Probabilistic Planning

2020 ◽  
pp. 93-107
Author(s):  
Milton Condori Fernandez ◽  
Leliane N. de Barros ◽  
Denis Mauá ◽  
Karina V. Delgado ◽  
Valdinei Freire
Keyword(s):  
Risk Averse ◽  
Probabilistic Planning ◽  
Extreme Risk

scholarly journals The Computational Complexity of Probabilistic Planning

1998 ◽  
Vol 9 ◽  
pp. 1-36 ◽  
Author(s):  
M. L. Littman ◽  
J. Goldsmith ◽  
M. Mundhenk
Keyword(s):  
Computational Complexity ◽  
Efficient Algorithms ◽  
Boolean Satisfiability ◽  
Complexity Classes ◽  
Probabilistic Planning ◽  
Plan Evaluation ◽  
Partially Ordered ◽  
Boolean Satisfiability Problem ◽  
Complete Problem ◽  
Planning Problems

We examine the computational complexity of testing and finding small plans in probabilistic planning domains with both flat and propositional representations. The complexity of plan evaluation and existence varies with the plan type sought; we examine totally ordered plans, acyclic plans, and looping plans, and partially ordered plans under three natural definitions of plan value. We show that problems of interest are complete for a variety of complexity classes: PL, P, NP, co-NP, PP, NP^PP, co-NP^PP, and PSPACE. In the process of proving that certain planning problems are complete for NP^PP, we introduce a new basic NP^PP-complete problem, E-MAJSAT, which generalizes the standard Boolean satisfiability problem to computations involving probabilistic quantities; our results suggest that the development of good heuristics for E-MAJSAT could be important for the creation of efficient algorithms for a wide variety of problems.

Chapter 34. Stochastic Boolean Satisfiability

2021 ◽  
Author(s):  
Stephen M. Majercik
Keyword(s):  
Artificial Intelligence ◽  
Computational Complexity ◽  
Probabilistic Reasoning ◽  
Broad Class ◽  
Theoretical Perspective ◽  
Boolean Satisfiability ◽  
Probabilistic Planning ◽  
Empirical Results ◽  
Future Work ◽  
Planning Problems

Stochastic satisfiability (SSAT) is an extension of satisfiability (SAT) that merges two important areas of artificial intelligence: logic and probabilistic reasoning. Initially suggested by Papadimitriou, who called it a “game against nature”, SSAT is interesting both from a theoretical perspective–it is complete for PSPACE, an important complexity class–and from a practical perspective–a broad class of probabilistic planning problems can be encoded and solved as SSAT instances. This chapter describes SSAT and its variants, their computational complexity, applications of SSAT, analytical results, algorithms and empirical results, related work, and directions for future work.

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