Add to ORKGThe problem of modeling uncertainty and inexact reasoning in rule-based expert systems is challenging on nonnative as well on cognitive grounds. First, the modular structure of the rule-based architecture does not lend itself to standard Bayesian inference techniques. Second, there is no consensus on how to model human (expert) judgement under uncertainty. These factors have led to a proliferation of quasi-probabilistic belief calculi which are widely-used in practice. This paper investigates the descriptive and external validity of three well-known "belief languages:" the Bayesian, ad-hoc Bayesian, and the certainty factors languages. These models are implemented in many commercial expert system shells, and their validity is clearly an im...
Much of the research done in Artificial Intelligence involves investigating and developing methods o...
AbstractThis paper compares four measures that have been advocated as models for uncertainty in expe...
Most research on rule-based inference under uncertainty has focused on the normative validity and ef...
The problem of modeling uncertainty and inexact reasoning in rule-based expert systems is challengin...
Rule based expert systems deal with inexact reasoning through a variety of quasi-probabilistic metho...
Rule-based expert systems must deal with uncertain data, subjective expert opinions, and inaccurate ...
One of the key challenges in designing expert systems is a credible representation of uncertainty an...
Belief updating schemes in artificial intelligence may be viewed as three dimensional languages, con...
AbstractThis paper addresses the problem of modeling of expert knowledge as a starting point for inf...
One of the key challenges in designing expert systems is a credible represen-tation of uncertainty a...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
In developing methods for dealing with uncertainty in reasoning systems, it is important to consider...
AbstractMost expert knowledge is ill-defined and heuristic. Therefore, many present-day rule-based e...
The solution of non-deterministic expert systems consists of two components –the solution reached an...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Much of the research done in Artificial Intelligence involves investigating and developing methods o...
AbstractThis paper compares four measures that have been advocated as models for uncertainty in expe...
Most research on rule-based inference under uncertainty has focused on the normative validity and ef...
The problem of modeling uncertainty and inexact reasoning in rule-based expert systems is challengin...
Rule based expert systems deal with inexact reasoning through a variety of quasi-probabilistic metho...
Rule-based expert systems must deal with uncertain data, subjective expert opinions, and inaccurate ...
One of the key challenges in designing expert systems is a credible representation of uncertainty an...
Belief updating schemes in artificial intelligence may be viewed as three dimensional languages, con...
AbstractThis paper addresses the problem of modeling of expert knowledge as a starting point for inf...
One of the key challenges in designing expert systems is a credible represen-tation of uncertainty a...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
In developing methods for dealing with uncertainty in reasoning systems, it is important to consider...
AbstractMost expert knowledge is ill-defined and heuristic. Therefore, many present-day rule-based e...
The solution of non-deterministic expert systems consists of two components –the solution reached an...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Much of the research done in Artificial Intelligence involves investigating and developing methods o...
AbstractThis paper compares four measures that have been advocated as models for uncertainty in expe...
Most research on rule-based inference under uncertainty has focused on the normative validity and ef...