GRAMMARSAMPLING (Process)PROBABILITY theoryGIBBS samplingPROBABILISTIC number theoryInferring formal grammars with nonparametric Bayesian approach is one of the most powerful approach for achieving high accuracy from unsupervised data. In this paper, mildly-context-sensitive probabilities, called (k, l)-...
In this work we propose an approach of incorporating learning context- sensitive grammar in strongly typed genetic programming (GP) employed for evolu- tion and adaptation of locomotion gaits of simulated snake-like robot (Snakebot). In our approach the probabilistic context-sensitive grammar is deriv...
In a probabilistic context-free grammar, each production rule is assigned a probability, so that the probabilities of all production rules with the same nonterminal symbol on the left-hand side (LHS) sum up to 1. Examples of grammars that were used in our experiments are shown in Table 1 ...
A C# library for (Probabilistic) Context Free Grammars (PCFGs). Supports probability-preserving conversion to Chomsky Normal Form (CNF), as well as the CYK and Earley parsing algorithms. These algorithms allow you to tell whether or not a grammar could have generated a given string, and if ...
This success may to a large extent be put down to the augmentation of the context-free grammar to a PCFG 14. As mentioned above, the accuracy of a PCFG depends heavily on the accuracy of the empirical estimate of the probability function. We were lucky to have at our disposal a training...
A Context-Sensitive Model for Probabilistic LR Parsing of Spoken Language with Transformation-Based Postprocessing This paper describes a hybrid approach to spontaneous speech parsing. The ilnplcmeutcd parser uses an extended probabilistic LR parsing model with rich coutext and its output is postproces...
simply context-free rules or context-sensitive properties such as subtrees of proof trees or non-local head-head relations. The algorithm we will present is an extension of the estimation method for log-linear models of [8] to incomplete-data settings. Furthermore, we will present a method ...
In the context of multi-agent systems, probabilistic models can be used to represent the behavior and decision-making processes of individual agents, and to analyze the collective behavior of the system as a whole.Multi-agent systems are systems composed of multiple independent agents that interact...
ILCS introduce a biased mutation in GP via probabilistic context sensitive grammar, in which the probabilities of applying the production rules with multiple right-hand side alternatives depend on the grammatical context. The distribution of these probabilities is learned interactively from the syntax of...
Incorporating learning probabilistic context-sensitive grammar in genetic programming for efficient evolution and adaptation of snakebot - Tanev - 2005 () Citation Context ...ing in a given environment. The major attributes of GP - function set, terminal set, fitness evaluati...