Ontology View on Automata Theory

The summary of automata theory ontology is presented in the paper. It is based on the following dependences: a type of an automaton – the language accepted by the automaton – applications. The given ontology does not claim to be exhaustive as automata theory is very extensive and it is a complicated problem to survey all its aspects within one article. Only the main properties of automata and their applications are considered.

Automata–based Method for Solving Systems of Linear Constraints in {0,1}

We consider a finite state automata based method of solving a system of linear Diophantine equations with coefficients from the set {-1,0,1} and solutions in {0,1}.

General Aspects of Constructing an Autonomous Adaptive Agent

There are a great deal of approaches in artificial intelligence, some of them also coming from biology and neirophysiology. In this paper we are making a review, discussing many of them, and arranging our discussion around the autonomous agent research. We highlight three aspect in our classification: type of abstraction applied for representing agent knowledge, the implementation of hypothesis processing mechanism, allowed degree of freedom in behaviour and self-organizing. Using this classification many approaches in artificial intelligence are evaluated. Then we summarize all discussed ideas and propose a series of general principles for building an autonomous adaptive agent.

Timed Transition Automata as Numerical Planning Domain

A general technique for transforming a timed finite state automaton into an equivalent automated planning domain based on a numerical parameter model is introduced. Timed transition automata have many applications in control systems and agents models; they are used to describe sequential processes, where actions are labelling by automaton transitions subject to temporal constraints. The language of timed words accepted by a timed automaton, the possible sequences of system or agent behaviour, can be described in term of an appropriate planning domain encapsulating the timed actions patterns and constraints. The time words recognition problem is then posed as a planning problem where the goal is to reach a final state by a sequence of actions, which corresponds to the timed symbols labeling the automaton transitions. The transformation is proved to be correct and complete and it is space/time linear on the automaton size. Experimental results shows that the performance of the planning domain obtained by transformation is scalable for real world applications. A major advantage of the planning based approach, beside of the solving the parsing problem, is to represent in a single automated reasoning framework problems of plan recognitions, plan synthesis and plan optimisation.