[BOOK][B] Planning with Markov decision processes: An AI perspective
A Kolobov - 2012 - books.google.com
Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling
sequential decision-making scenarios with probabilistic dynamics. They are the framework …
sequential decision-making scenarios with probabilistic dynamics. They are the framework …
Improved non-deterministic planning by exploiting state relevance
We address the problem of computing a policy for fully observable non-deterministic (FOND)
planning problems. By focusing on the relevant aspects of the state of the world, we …
planning problems. By focusing on the relevant aspects of the state of the world, we …
Stochastic safest and shortest path problems
F Teichteil-Königsbuch - Proceedings of the AAAI Conference on …, 2012 - ojs.aaai.org
Abstract Optimal solutions to Stochastic Shortest Path Problems (SSPs) usually require that
there exists at least one policy that reaches the goal with probability 1 from the initial state …
there exists at least one policy that reaches the goal with probability 1 from the initial state …
Replanning in domains with partial information and sensing actions
Replanning via determinization is a recent, popular approach for online planning in MDPs.
In this paper we adapt this idea to classical, non-stochastic domains with partial information …
In this paper we adapt this idea to classical, non-stochastic domains with partial information …
Anticipatory on-line planning
We consider the problem of on-line continual planning, in whichadditional goals may arrive
while plans for previous goals are stillexecuting and plan quality depends on how quickly …
while plans for previous goals are stillexecuting and plan quality depends on how quickly …
Planning under uncertainty using reduced models: Revisiting determinization
We introduce a family of MDP reduced models characterized by two parameters: the
maximum number of primary outcomes per action that are fully accounted for and the …
maximum number of primary outcomes per action that are fully accounted for and the …
Domain-independent intelligent planning technology and its application to automated penetration testing oriented attack path discovery
Y ZHANG, T ZHOU, J ZHU, Q WANG - 电子与信息学报, 2020 - jeit.ac.cn
Attack path discovery is an important research direction in automated penetration testing
area. This paper introduces the research progress of domain independent intelligent …
area. This paper introduces the research progress of domain independent intelligent …
Continual planning for search and rescue robots
The deployment of robots for emergency response tasks such as search and rescue is a
promising application of robotics with growing importance. Given the perilous nature of …
promising application of robotics with growing importance. Given the perilous nature of …
Extending classical planning heuristics to probabilistic planning with dead-ends
Recent domain-determinization techniques have been very successful in many probabilistic
planning problems. We claim that traditional heuristic MDP algorithms have been …
planning problems. We claim that traditional heuristic MDP algorithms have been …
Answerable and unanswerable questions in risk analysis with open‐world novelty
LA Cox Jr - Risk Analysis, 2020 - Wiley Online Library
Decision analysis and risk analysis have grown up around a set of organizing questions:
what might go wrong, how likely is it to do so, how bad might the consequences be, what …
what might go wrong, how likely is it to do so, how bad might the consequences be, what …