Learning Stationary Temporal Probabilistic Networks
نویسنده
چکیده
The paper describes a method for learning representations of partially observable Markov decision processes in the form of temporal probabilistic networks, which can subsequently be used by robotic agents for action planning and policy determination. A solution is provided to the problem of enforcing stationarity of the learned Markov model. Several preliminary experiments are described that con rm the validity of the solution.
منابع مشابه
A Two-Teams Approach for Robust Probabilistic Temporal Planning
Large real-world Probabilistic Temporal Planning (PTP) is a very challenging research field. A common approach is to model such problems as Markov Decision Problems (MDP) and use dynamic programming techniques. Yet, two major difficulties arise: 1dynamic programming does not scale with the number of tasks, and 2the probabilistic model may be uncertain, leading to the choice of unsafe policies. ...
متن کاملA Link Prediction Method Based on Learning Automata in Social Networks
Nowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probability of creation of a new relationship in future. A wide range of applications can be found for link prediction such as electro...
متن کاملTemporal Hidden Hopfield Models
Many popular probabilistic models for temporal sequences assume simple hidden dynamics or low dimensionality of discrete variables. For higher dimensional discrete hidden variables, recourse is often made to approximate mean field theories, which to date have been applied to models with only simple hidden unit dynamics. We consider a class of models in which the discrete hidden space is defined...
متن کاملApplication of the probabilistic RBF neural network in the reinforcement learning of a mobile robot
The paper presents the application of the reinforcement learning for the autonomous mobile robot moving learning in an unknown, stationary environment. The robot movement policy was represented by a probabilistic RBF neural network. The network proved to be very attractive tool, which enabled efficient, simple and fast approximation of the state value function.
متن کاملRelationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks
A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interaction...
متن کامل