A discounted-sum automaton (NDA) is a nondeterministic finite automaton with edge weights, valuing a run by the discounted sum of visited edge weights. More precisely, the weight in the i-th position of the run is divided by λ, where the discount factor λ is a fixed rational number greater than 1. The value of a word is the minimal value of the automaton runs on it. Discounted summation is a co...

We study automata on infinite words and their applications in system specification and verification. We first introduce Büchi automata and survey their closure properties, expressive power, and determinization. We then introduce additional acceptance conditions and the model of alternating automata. We compare the different classes of automata in terms of expressive power and succinctness, and ...

Recently, finite-state transducers (FSTs) have been shown to be useful for a number of applications in speech and language processing. FST operations such as composition, determinization, and minimization make manipulating FSTs very simple. In this paper, we present a method to learn weights for arbitrary FSTs using the EM algorithm. We show that this FST EM algorithm is able to learn pronuncia...

This paper investigates hindsight optimization as an approach for leveraging the significant advances in deterministic planning for action selection in probabilistic domains. Hindsight optimization is an online technique that evaluates the onestep-reachable states by sampling future outcomes to generate multiple non-stationary deterministic planning problems which can then be solved using searc...

In this paper we present durational actions timed automata, DATA*, as a sub class of timed automata. In the contrast of T.A, the underling semantic of DATA* is the maximality semantics which claim that actions have durations and true concurrency is captured differently from choice. DATA* model is in one hand useful for modeling and validating reel aspects of systems. In the other hand, it is de...

We generalize the models of visibly pushdown automata (VPDAs) and visibly pushdown transducers (VPDTs) by equipping them with reversal-bounded counters. We show that some of the results for VPDAs and VPDTs (e.g., closure under intersection and decidability of emptiness for VPDA languages) carry over to the generalized models, but other results (e.g., determinization and closure under complement...

Finite-state automata are a very effective tool in natural language processing. However, in a variety of applications and especially in speech precessing, it is necessary to consider more general machines in which arcs are assigned weights or costs. We briefly describe some of the main theoretical and algorithmic aspects of these machines. In particular, we describe an efficient composition alg...

Weighted finite-state transducers are used in many applications such as text, speech and image processing. This chapter gives an overview of several recent weighted transducer algorithms, including composition of weighted transducers, determinization of weighted automata, a weight pushing algorithm, and minimization of weighted automata. It briefly describes these algorithms, discusses their ru...