Cognitive Processes by using Finite State Machines

Cognitive Processes by using Finite State Machines

Ismael Rodríguez (Universidad Complutense de Madrid, Spain), Manuel Núñez (Universidad Complutense de Madrid, Spain) and Fernando Rubio (Universidad Complutense de Madrid, Spain)
Copyright: © 2009 |Pages: 13
DOI: 10.4018/978-1-60566-170-4.ch003
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Finite State Machines (FSM) are formalisms that have been used for decades to describe the behavior of systems. They can also provide an intelligent agent with a suitable formalism for describing its own beliefs about the behavior of the world surrounding it. In fact, FSMs are the suitable acceptors for right linear languages, which are the simplest languages considered in Chomsky’s classification of languages. Since Chomsky proposes that the generation of language (and, indirectly, any mental process) can be expressed through a kind of formal language, it can be assumed that cognitive processes can be formulated by means of the formalisms that can express those languages. Hence, we will use FSMs as a suitable formalism for representing (simple) cognitive models. We present an algorithm that, given an observation of the environment, produces an FSM describing an environment behavior that is capable to produce that observation. Since an infinite number of different FSMs could have produced that observation, we have to choose the most feasible one. When a phenomenon can be explained with several theories, Occam’s razor principle, which is basic in science, encourages choosing the simplest explanation. Applying this criterion to our problem, we choose the simplest (smallest) FSM that could have produced that observation. An algorithm is presented to solve this problem. In conclusion, our framework provides a cognitive model that is the most preferable theory for the observer, according to the Occam’s razor criterion.
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Cognitive Informatics [Kinsner 2005, Wang 2002, 2003] provides Computer Science with a remarkable inspiration source for solving computational problems where the objectives are similar to those performed by the human mind. In spite of the fact that computational environments have some specific requirements and constraints that must not be ignored, understanding our mind is usually the key to provide successful (particularized) intelligent systems. This cross-fertilization has yielded the development of some successful intelligence mechanisms such as neural networks [Lau 1991] and case-based reasoning algorithms [Schank and Abelson 1977].

It is particularly relevant to note that the relationship between Computer Science and other mind-related sciences is two-faced. In particular, the development of formal language theories (oriented to computational languages) has led to a better understanding of our mind. Due to the close relationship between language generation and mental processes, some mathematical formalisms proposed for dealing with formal computational languages turned out to be good approximations to model human reasonings. Following this line, it is specially relevant the language theory developed by Noam Chomsky. He proposed that natural languages can be represented as formal languages [Chomsky 1957, 1965]. Chomsky considered four categories of languages (from simpler to more complex, right linear, context-free, context-sensitive, and grammars - or recursive enumerable) and he argued that natural languages are context-sensitive. All of these categories can be produced by a kind of suitable formal machine or acceptor (finite state automata, push-down automata, linear bounded automata, and Turing machines, respectively). Thus, the generation of natural languages can be represented in terms of some kind of formal automata, specifically linear bounded automata. This statement is specially relevant: Since the language is a projection of our cognitive processes, we can say that our own reasonings can be represented by using context-sensitive languages. Similarly, other less expressive languages (like right linear or context-free) may provide approximated and simpler models to represent human mental processes.

The difficulty to use a formal language to represent reasonings in a computational environment has discouraged most researchers to explore this trend. Paradoxically, the great expressivity of formal languages is its main handicap. For example, the beliefs/knowledge of an intelligent system cannot be internally represented by a recursive enumerable language (or its acceptor, a Turing machine), because there is no method to automatically construct the Turing machine that produces some given behavior. So, such an internal representation would be unable to create and maintain. Nevertheless, in some domains, using the simplest languages according to Chomsky’s classification could provide us with formalisms endowed with a suitable structure and expressivity while being efficient to handle. In particular, let us note that right linear languages are a suitable formalism for representing the behavior of a wide range of entities and systems. Their acceptors, that is Finite State Machines, have been used for decades to model the behavior of sequential digital circuits and communication protocols. Similarly, an intelligent entity can use an FSM to represent its belief about the behavior of the world that surrounds it. As any other knowledge representation, this model should be updated and maintained so that it provides, at any time, a feasible explanation of the events the agent has observed so far. If the model is accurate then the agent could use it to predict future situations. Hence, FSMs may be the basic formalism for knowledge representation in a learning system.

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Table of Contents
Yingxu Wang
Chapter 1
Yingxu Wang
Cognitive Informatics (CI) is a transdisciplinary enquiry of the internal information processing mechanisms and processes of the brain and natural... Sample PDF
The Theoretical Framework of Cognitive Informatics
Chapter 2
Withold Kinsner
This chapter provides a review of Shannon and other entropy measures in evaluating the quality of materials used in perception, cognition, and... Sample PDF
Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?
Chapter 3
Ismael Rodríguez, Manuel Núñez, Fernando Rubio
Finite State Machines (FSM) are formalisms that have been used for decades to describe the behavior of systems. They can also provide an intelligent... Sample PDF
Cognitive Processes by using Finite State Machines
Chapter 4
Yingxu Wang
An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain (LRMB) and the Object-Attribute-Relation... Sample PDF
On the Cognitive Processes of Human Perception with Emotions, Motivations, and Attitudes
Chapter 5
Qingyong Li, Zhiping Shi, Zhongzhi Shi
Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of... Sample PDF
A Selective Sparse Coding Model with Embedded Attention Mechanism
Chapter 6
Yingxu Wang
Theoretical research is predominately an inductive process, while applied research is mainly a deductive process. Both inference processes are based... Sample PDF
The Cognitive Processes of Formal Inferences
Chapter 7
Douglas Griffith, Frank L. Greitzer
The purpose of this article is to re-address the vision of human-computer symbiosis as originally expressed by J.C.R. Licklider nearly a... Sample PDF
Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction
Chapter 8
Ray E. Jennings
Although linguistics may treat languages as a syntactic and/or semantic entity that regulates both language production and comprehension, this... Sample PDF
Language, Logic, and the Brain
Chapter 9
Yingxu Wang, Guenther Ruhe
Decision making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of actions is chosen from among a... Sample PDF
The Cognitive Process of Decision Making
Chapter 10
Tiansi Dong
This chapter proposes a commonsense understanding of distance and orientation knowledge between extended objects, and presents a formal... Sample PDF
A Commonsense Approach to Representing Spatial Knowledge Between Extended Objects
Chapter 11
Natalia López, Manuel Núñez, Fernando L. Pelayo
In this chapter we present the formal language, stochastic process algebra (STOPA), to specify cognitive systems. In addition to the usual... Sample PDF
A Formal Specification of the Memorization Process
Chapter 12
Yingxu Wang
Autonomic computing (AC) is an intelligent computing approach that autonomously carries out robotic and interactive applications based on goal- and... Sample PDF
Theoretical Foundations of Autonomic Computing
Chapter 13
Witold Kinsner
Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive... Sample PDF
Towards Cognitive Machines: Multiscale Measures and Analysis
Chapter 14
Amar Ramdane-Cherif
Cognitive approach through the neural network (NN) paradigm is a critical discipline that will help bring about autonomic computing (AC). NN-related... Sample PDF
Towards Autonomic Computing: Adaptive Neural Network for Trajectory Planning
Chapter 15
Lee Flax
We give an approach to cognitive modelling, which allows for richer expression than the one based simply on the firing of sets of neurons. The... Sample PDF
Cognitive Modelling Applied to Aspects of Schizophrenia and Autonomic Computing
Chapter 16
Yan Zhao, Yiyu Yao
Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation... Sample PDF
Interactive Classification Using a Granule Network
Chapter 17
Mehdi Najjar, André Mayers
Encouraging results of last years in the field of knowledge representation within virtual learning environments confirms that artificial... Sample PDF
A Cognitive Computational Knowledge Representation Theory
Chapter 18
Du Zhang
A crucial component of an intelligent system is its knowledge base that contains knowledge about a problem domain. Knowledge base development... Sample PDF
A Fixpoint Semantics for Rule-Base Anomalies
Chapter 19
Christine W. Chan
This chapter presents a method for ontology construction and its application in developing ontology in the domain of natural gas pipeline... Sample PDF
Development of an Ontology for an Industrial Domain
Chapter 20
Václav Rajlich, Shaochun Xu
This article explores the non-monotonic nature of the programmer learning that takes place during incremental program development. It uses a... Sample PDF
Constructivist Learning During Software Development
Chapter 21
Witold Kinsner
Many scientific chapters treat the diversity of fractal dimensions as mere variations on either the same theme or a single definition. There is a... Sample PDF
A Unified Approach to Fractal Dimensions
Chapter 22
Du Zhang, Witold Kinsner, Jeffrey Tsai, Yingxu Wang, Philip Sheu, Taehyung Wang
The 2005 IEEE International Conference on Cognitive Informatics (ICCI’05) was held during August 8th to 10th 2005 on the campus of University of... Sample PDF
Cognitive Informatics: Four Years in Practice
Chapter 23
Yiyu Yao, Zhongzhi Shi, Yingxu Wang, Witold Kinsner, Yixin Zhong, Guoyin Wang
Cognitive informatics (CI) is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics... Sample PDF
Toward Cognitive Informatics and Cognitive Computers: A Report on IEEE ICCI'06
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