A tutorial on planning graph based reachability heuristics
The primary revolution in automated planning in the last decade has been the very
impressive scale-up in planner performance. A large part of the credit for this can be …
impressive scale-up in planner performance. A large part of the credit for this can be …
Probabilistic planning via heuristic forward search and weighted model counting
We present a new algorithm for probabilistic planning with no observability. Our algorithm,
called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to …
called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to …
[PDF][PDF] Monte-Carlo Exploration for Deterministic Planning.
H Nakhost, M Müller - IJCAI, 2009 - webdocs.cs.ualberta.ca
Search methods based on Monte-Carlo simulation have recently led to breakthrough
performance improvements in difficult game-playing domains such as Go and General …
performance improvements in difficult game-playing domains such as Go and General …
Efficient distribution of virtual machines for cloud computing
The commercial success of Cloud computing and recent developments in Grid computing
have brought platform virtualization technology into the field of high performance computing …
have brought platform virtualization technology into the field of high performance computing …
Occupation measure heuristics for probabilistic planning
For the past 25 years, heuristic search has been used to solve domain-independent
probabilistic planning problems, but with heuristics that determinise the problem and ignore …
probabilistic planning problems, but with heuristics that determinise the problem and ignore …
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 …
Pattern databases for goal-probability maximization in probabilistic planning
Heuristic search algorithms for goal-probability maximization (MaxProb) have been known
since a decade. Yet prior work on heuristic functions for MaxProb relies on determinization …
since a decade. Yet prior work on heuristic functions for MaxProb relies on determinization …
Planning and acting in incomplete domains
C Weber, D Bryce - Proceedings of the International Conference on …, 2011 - ojs.aaai.org
Engineering complete planning domain descriptions is often very costly because of human
error or lack of domain knowl-edge. Learning complete domain descriptions is also very …
error or lack of domain knowl-edge. Learning complete domain descriptions is also very …
Synthesizing robust plans under incomplete domain models
Most current planners assume complete domain models and focus on generating correct
plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world …
plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world …
[PDF][PDF] Probabilistic planning is multi-objective
Probabilistic planning is an inherently multi-objective problem where plans must trade-off
probability of goal satisfaction with expected plan cost. To date, probabilistic plan synthesis …
probability of goal satisfaction with expected plan cost. To date, probabilistic plan synthesis …