Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
145,574
Large state space visualization
- In Proc. of Tools and Algorithms for Construction and Analysis of Systems (TACAS 2003), volume 2619 of LNCS
, 2003
"... Abstract. Insight in the global structure of a state space is of great help in the analysis of the underlying process. We advocate the use of visualization for this purpose and present a new method to visualize the structure of very large state spaces. The method uses a clustering method to obtain a ..."
Abstract
-
Cited by 6 (1 self)
- Add to MetaCart
Abstract. Insight in the global structure of a state space is of great help in the analysis of the underlying process. We advocate the use of visualization for this purpose and present a new method to visualize the structure of very large state spaces. The method uses a clustering method to obtain
Exploring Very Large State Spaces Using
, 2002
"... We present a novel framework for exploring very large state spaces of concurrent reactive systems. Our framework exploits applicationindependent heuristics using genetic algorithms to guide a state-space search towards error states. We have implemented this framework in conjunction with VeriSoft ..."
Abstract
- Add to MetaCart
We present a novel framework for exploring very large state spaces of concurrent reactive systems. Our framework exploits applicationindependent heuristics using genetic algorithms to guide a state-space search towards error states. We have implemented this framework in conjunction with Veri
Tackling Large State Spaces in Performance Modelling ⋆
"... Abstract. Stochastic performance models provide a powerful way of capturing and analysing the behaviour of complex concurrent systems. Traditionally, performance measures for these models are derived by generating and then analysing a (semi-)Markov chain corresponding to the model’s behaviour at the ..."
Abstract
- Add to MetaCart
at the state-transition level. However, and especially when analysing industrial-scale systems, workstation memory and compute power is often overwhelmed by the sheer number of states. This chapter explores an array of techniques for analysing stochastic performance models with large state spaces. We
Conservation decision-making in large state spaces
"... Abstract: For metapopulation management problems with small state spaces, it is typically possible to model the problem as a Markov decision process (MDP), and find an optimal control policy using stochastic dynamic programming (SDP). SDP is an iterative procedure that seeks to optimise a value func ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. A large state space makes optimal SDP solutions computationally expensive to compute because optimal SDP techniques
Exploring Very Large State Spaces Using Genetic Algorithms
- SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER
"... We present a novel framework for exploring very large state spaces of concurrent reactive systems. Our framework exploits application-independent heuristics using genetic algorithms to guide a state-space search towards error states. We have implemented this framework in conjunction with VeriSoft, ..."
Abstract
-
Cited by 71 (2 self)
- Add to MetaCart
We present a novel framework for exploring very large state spaces of concurrent reactive systems. Our framework exploits application-independent heuristics using genetic algorithms to guide a state-space search towards error states. We have implemented this framework in conjunction with Veri
Fast Inference and Learning in Large-State-Space HMMs
- Proc. ICML
, 2005
"... For Hidden Markov Models (HMMs) with fully connected transition models, the three fundamental problems of evaluating the likelihood of an observation sequence, estimating an optimal state sequence for the observations, and learning the model parameters, all have quadratic time complexity in the numb ..."
Abstract
-
Cited by 16 (3 self)
- Add to MetaCart
For Hidden Markov Models (HMMs) with fully connected transition models, the three fundamental problems of evaluating the likelihood of an observation sequence, estimating an optimal state sequence for the observations, and learning the model parameters, all have quadratic time complexity
Symbolic Representations and Analysis of Large State Spaces
- In this Proceedings
, 2003
"... This paper describes symbolic techniques for the construction, representation and analysis of large, probabilistic systems. Symbolic approaches derive their eciency by exploiting high-level structure and regularity in the models to which they are applied, increasing the size of the state spaces ..."
Abstract
-
Cited by 6 (2 self)
- Add to MetaCart
This paper describes symbolic techniques for the construction, representation and analysis of large, probabilistic systems. Symbolic approaches derive their eciency by exploiting high-level structure and regularity in the models to which they are applied, increasing the size of the state spaces
Markov Decision Processes in Large State Spaces
- In Proceedings of the 8th Annual Workshop on Computational Learning Theory
, 1995
"... In this paper we propose a new framework for studying Markov decision processes (MDPs), based on ideas from statistical mechanics. The goal of learning in MDPs is to find a policy that yields the maximum expected return over time. In choosing policies, agents must therefore weigh the prospects of sh ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
of short-term versus long-term gains. We study a simple MDP in which the agent must constantly decide between exploratory jumps and local reward mining in state space. The number of policies to choose from grows exponentially with the size of the state space, N . We view the expected returns as defining
Reinforcement Learning in Large State Spaces: Simulated Robotic Soccer as a testbed
- ROBOCUP 2002: ROBOT SOCCER WORLD CUP VI
, 2003
"... Bayesian networks (BNs) are a compact representation of a joint probability distribution. In this paper we show how they can be used for modeling other agents in the environment. More precisely we will have special attention to the problem of large state spaces and incomplete information. For ou ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
Bayesian networks (BNs) are a compact representation of a joint probability distribution. In this paper we show how they can be used for modeling other agents in the environment. More precisely we will have special attention to the problem of large state spaces and incomplete information
Results 1 - 10
of
145,574
