This paper details the development and evaluation of AstonTAC, an energy broker that successfully participated in the 2012 Power Trading Agent Competition (Power TAC). AstonTAC buys electrical energy from the wholesale market and sells it in the retail market. The main focus of the paper is on the broker’s bidding strategy in the wholesale market. In particular, it employs Markov Decision Proce...

The paper presents the application of Hidden Markov Models to text generation in Polish language. A program generating text, taking advantage of Hidden Markov Models was developed. The program uses a reference text to learn the possible sequences of letters. The results of text processing have been also discussed. The presented approach can be also helpful in speech recognition process. Key-Wor...

Hidden Markov Model Regression (HMMR) is an extension of the Hidden Markov Model (HMM) to regression analysis. We assume that the parameters of the regression model are determined by the outcome of a nite-state Markov chain and that the error terms are conditionally independent normally distributed with mean zero and state dependent variance. The theory of HMM regression is quite new, but some ...

Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation a...

Although much effort has been put into speech understanding systems there still exists a rather wide gap between acoustic recognition and linguistic interpretation. We propose a formalism for an extremely close interaction of acoustic recognition and higher level analysis. Instead of a strict horizontal interface at the level of hypothesized word sequences or lattices, a vertical interface to t...

MOTIVATION Statistical sequence comparison techniques, such as hidden Markov models and generalized profiles, calculate the probability that a sequence was generated by a given model. Log-odds scoring is a means of evaluating this probability by comparing it to a null hypothesis, usually a simpler statistical model intended to represent the universe of sequences as a whole, rather than the grou...

By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are connected in that space. The transition matrix can then be constrained to allow transitions only between neighbours; this means that all valid state sequences correspond to connected paths in the topol...

How can we apply machine learning to data that is represented as a sequence of observations over time? For instance, we might be interested in discovering the sequence of words that someone spoke based on an audio recording of their speech. Or we might be interested in annotating a sequence of words with their part-of-speech tags. These notes provides a thorough mathematical introduction to the...

stemming is the process of finding the main morpheme of a word andit is used in natural language processing, text mining and informationretrieval systems. a stemmer extracts the stem of the words. we can classifypersian stemmers in to three main classes: structural stemmers, dictionarybased stemmers and  statistical stemmers.the precision of structural stemmers is low and the expenses of dictio...