An Approach to Representation Changes While Executing Problem Solver Intelligent Systems

Hernando, A.; Ledesma, Luis de; Laita, Luis M.

doi:10.1109/coginf.2007.4341870

Cited by 2 publications

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“…Using this technique for measuring and modifying relevance, we have so far implemented a prototype problem solver able to perform representation changes while problem solving. This technique has been successfully tested in connection to some interesting problem examples [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

A technique for dynamically measuring and modifying relevance while problem solving

Hernando

Ledesma

2009

Rev. R. Acad. Cien. Serie A. Mat.

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Although there is general agreement that efficiency of problem resolution is strongly related to the problem representation adopted, computer problem solvers have been traditionally designed to keep the same representation throughout the whole of the problem solving process. A system able to change representation whilst the actual problem solving process occurs has advantages over traditional ones, not only because a representation change can improve the efficiency of problem resolution (as already proven through much research), but also because the choice of the most suitable representation may be decisively enhanced after learning about the problem during its resolution process. A natural and interesting way of performing representation changes is related to detecting irrelevant elements which can be removed out of the problem representation. In this paper, we deal formally with a new technique for assigning and changing the relevance of the elements involved in the representation during the problem resolution, on behalf of their respective importance so as to actually solve the problem. Una técnica para medir y modificar dinámicamente la relevancia en la resolución de problemasResumen. Aunque existe un consenso general sobre la fuerte dependencia entre la eficiencia en la resolución de problemas y la representación de los mismos que se adopta, los programas resolvedores de problemas tradicionalmente se han diseñado teniendo en cuentaúnicamente una sola representación a lo largo de todo el proceso de resolución. Un sistema capaz de cambiar la representación mientras el mismo proceso de resolución tiene lugar ofrece ventajas respecto de los tradicionales, no sólo porque ciertos cambios de representación pueden mejorar la eficiencia del proceso de resolución (como muchas investigaciones han demostrado), sino también debido a que la tarea de seleccionar la representación más adecuada para un problema puede facilitarse decisivamente mediante el conocimiento adquirido durante el proceso de su resolución. Una manera interesante y natural de ejecutar cambios de representación se apoya en detectar la información irrelevante que puede eliminarse de la representación del problema. En el presente trabajo, presentamos formalmente una nueva técnica para asignar y modificar valores de relevancia a los elementos que integran la representación de los problemas durante el mismo proceso de resolución, de acuerdo con la importancia que tienen para resolverlos.p n+1 = max{R + floor(k 1 · R/R) − k 1 , th} = max{R + k 1 − k 1 , th} = max{R, th}.Since R > th, we have that p n+1 = R iii) We have that:Since k 1 ≤ th ≤ p n and floor(−k 1 /p n ) = −1, we have that p n+1 ≤ max{p n − 1, th}.Since th < R and R < p n , we have that th ≤ p n − 1 and therefore p n+1 ≤ max{p n − 1, th} = p n − 1 < p n .Besides, we have that p n+1 ≥ R p n+1 = max{f (p n , R), th} ≥ max{f (R, R), th} = R.Theorem 2 If N = 0, then we have that:∃n ∈ N ∀m ≥ n p m = R PROOF. We will prove that ∃n ≥ 0 such that p n = R. Once we have proved it, we will prove this lemma ...

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Section: Introductionmentioning

confidence: 99%

A technique for dynamically measuring and modifying relevance while problem solving

Hernando

Ledesma

2009

Rev. R. Acad. Cien. Serie A. Mat.

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“…Cognitive science approach to solve a problem include following steps-First and foremost thing is to ensure an appropriate representation of problem including the critical features and avoiding irrelevant ones [2]. Second step is to discover the goal state of the problem that.…”

Section: Fig 1: Multidisciplinary Approach Of Cognitive Sciencementioning

confidence: 99%

Cognitive Science: Bridging the Gap between Machine and Human Intelligence

Komal¹

2015

IJCA

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Cognitive Science is the interdisciplinary study of concepts and theories related to the fields of psychology, neuroscience, philosophy, computer science, artificial intelligence, humanities and linguistics. A lot of research has been done in the area of machine learning and artificial intelligence which aims to inculcate human thinking, sensing and decisive capabilities into machines. However, such systems lack in various aspects of human behavior and intelligence such as emotions, intuitions, understanding of linguistics and learning acquired from different happenings and incidents occurred in the past. Cognitive science is an emerging field which intends to fill the existing gap in this area to build smarter systems. This paper discusses the advancements in the field of cognitive science, various models and their applicability to different problem domains. The paper also suggests framework of HAICS, a computer system having human alike intelligence.

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An Approach to Representation Changes While Executing Problem Solver Intelligent Systems

Cited by 2 publications

References 9 publications

A technique for dynamically measuring and modifying relevance while problem solving

A technique for dynamically measuring and modifying relevance while problem solving

Cognitive Science: Bridging the Gap between Machine and Human Intelligence

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