Hierarchical Censored Production Rules (HCPRs) system

Bharadwaj, Kamal Kant; Jain, N. K.

doi:10.1016/0169-023x(92)90003-t

Cited by 42 publications

(23 citation statements)

References 4 publications

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“…There may be different interpretations (e.g., Buchanan and Shortliffe 1984;Cohen and Grinberg 1983;Doyle and London 1980;Michalski and Winston 1986;Zadeh 1979) and ways to combine certainty factors depending on the type of knowledge-base system (e.g., Quinlan 1983;Shafer 1976;Winston 1984). Bharadwaj and Jain (1992) suggested the following heuristic formula for the computation of the certainty factor of decision at the ith level of specificity under forward chaining of reasoning:…”

Section: 'δ'=γ + Summation Of All δ'Smentioning

confidence: 99%

“…A detailed discussion of 'γ ' and 'δ' is given in (Bharadwaj and Jain 1992). Their value is constrained to be greater than 0.5 and less than 1 for a meaningful and hence useful implication.…”

Section: 'δ'=γ + Summation Of All δ'Smentioning

confidence: 99%

“…The partitioning of knowledge at different hierarchical levels during representation, allows us to manage the complexity of knowledge easily, to view the knowledge at different levels of detail, and to facilitate inference engine attention on different interesting aspects. An efficient and easily understandable representation of this type is by employing hierarchical censored production rules (HCPRs) (Bharadwaj and Jain 1992). A HCPR is an augmented CPR with specificity and generality information that can be made to exhibit variable precision in reasoning so that both certainty of belief in a conclusion and its specificity can be shown to vary in the reasoning process.…”

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confidence: 99%

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A generalized knowledge representation system for context sensitive reasoning: Generalized HCPRs System

Jain

2008

Artif Intell Rev

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Context sensitive reasoning (i.e., changing the response of the system with change in state of any kind with respect to time, space, matter, or emotions of the system, user, or environment) is significant in real life decision-making. This is a comparatively unexplored facet of intelligent systems and needs to be looked into depth. The Extended Hierarchical Censored Production Rules (EHCPRs) System is an attempt to assemble, maintain, and employ a massive knowledge base exhibiting the property of inheritance and recognition in its reasoning along with the representation of constraints and defaults, and will facilitate all sort of possible learning schemes. Context sensitivity is addressed in the EHCPRs System that result in Generalized HCPRs (GHCPRs) System and its characteristics are exhibited through sample run of implemented system. With different context of user priorities, the GHCPRs system is shown to behave differently in its decision making.

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Section: 'δ'=γ + Summation Of All δ'Smentioning

confidence: 99%

Section: 'δ'=γ + Summation Of All δ'Smentioning

confidence: 99%

mentioning

confidence: 99%

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A generalized knowledge representation system for context sensitive reasoning: Generalized HCPRs System

Jain

2008

Artif Intell Rev

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“…Another form of VPL rule structure called Hierarchical Censored Production Rules (HCPRs). A HCPR is a CPR augmented with specificity and generality information, which can be made to exhibit variable precision in the reasoning such that both certainty of belief in conclusion and its specificity may be controlled by the reasoning process [1]. Hewahi [3] improved HCPR by proposing a rule called General Rule Structure (GRS) to give more flexibility in directing the system where to go if a certain rule fails to fire.…”

Section: Rule Structurementioning

confidence: 99%

Rulebased Knowledge Representation Inspired from Finite Automata

Hewahi¹

2011

IJCA

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In this paper we present a method for rule based knowledge representation. The proposed approach is inspired from Finite Automata (FA). The inspired FA diagrams are easily used to represent rule-based system and make it more reliable structure in large complicated systems. One of the major additions that gives the proposed structure its significance is that it can represent the rules such that else case, the user text input or user menu input, and the relation and sequence of inputs are considered. The proposed diagram is called (RKRFA).

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“…In this context genetic algorithms have been suggested for discovery of Censored Production Rules (CPRs), Hierarchical production rules (HPRs), Hierarchical censored production rules (HCPRs), Ripple Down Rules (RDRs) etc. [27][28][29][30][31][32][33][34][35][36]. These special kinds of knowledge structures augment exceptions to their corresponding commonsense rules of high generality and support.…”

Section: Related Workmentioning

confidence: 99%

Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach

Vashishtha¹,

Kumar²,

Ratnoo³

et al. 2011

IJCA

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A majority of contribution in the domain of rule mining overemphasize on maximizing the predictive accuracy of the discovered patterns. The user-oriented criteria such as comprehensibility and interestingness are have been given secondary importance. Recently, it has been widely acknowledged that even highly accurate discovered knowledge might be worthless if it scores low on the qualitative parameters of comprehensibility and interestingness. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible and interesting in CNF form in which along with conjunction in between various attributes there is disjunction among the values of an attribute. A flexible encoding scheme, genetic operators with appropriate syntactic constraints and a suitable fitness function to measure the goodness of rules are proposed for effective evolution of rule sets. The proposed genetic algorithm is validated on several datasets of UCI data set repository and experimental results are presented which clearly indicate lower error rates and more comprehensibility across a range of datasets. Some of the rules show the interesting and valuable nuggets of knowledge discovered from small disjuncts of high accuracy and low support which are very difficult to capture otherwise.

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Hierarchical Censored Production Rules (HCPRs) system

Cited by 42 publications

References 4 publications

A generalized knowledge representation system for context sensitive reasoning: Generalized HCPRs System

A generalized knowledge representation system for context sensitive reasoning: Generalized HCPRs System

Rulebased Knowledge Representation Inspired from Finite Automata

Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach

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