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Distinguished lecture (Hong Kong Baptist University. Dept. of Computer Science)

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Fusing Multiple Sources of Conflicting Information

DEPARTMENT:

Department of Computer Science

CONYTRIBUTOR / SPEAKER:

SANTOS, Eugene

RELEASE DATE:

2011

LENGTH:

83 Minutes

VIDEO TYPE:

Scholarly Talks

In this talk, I address the challenges of information/knowledge fusion from multiple (possibly conflicting) sources. For example, consider that there are multiple experts (sources) providing knowledge-based models of the same scenario/situation and we wish to aggregate this information in order to assist in decision-making. There are several problems we may run into by naively merging the information from each source - the experts may disagree on the probability (uncertainty) of a certain event or they may disagree on the direction of causality between two events (e.g., one thinks A causes B while another thinks B causes A); the experts may even disagree on the entire structure of dependencies among a set of variables in a (probabilistic) network. The challenge here is to develop a semantically sound and computationally effective methodology that explicitly accounts for the uncertainty and conflicts. In our solution to this problem, we represent the knowledge-based models as Bayesian Knowledge Bases (BKBs) and provide an algorithm called Bayesian knowledge fusion that allows the fusion of multiple BKBs into a single BKB that retains the information from all input sources. This allows for easy aggregation and de-aggregation of information from multiple expert sources and facilitates multi-expert/source decision making by providing a framework in which all opinions can be preserved and reasoned over.

Fusing Multiple Sources of Conflicting Information

部門:

計算機科學系

講者:

SANTOS, Eugene

發佈日期:

2011

影片長度:

83 分鐘

影片類型:

學術論壇

In this talk, I address the challenges of information/knowledge fusion from multiple (possibly conflicting) sources. For example, consider that there are multiple experts (sources) providing knowledge-based models of the same scenario/situation and we wish to aggregate this information in order to assist in decision-making. There are several problems we may run into by naively merging the information from each source - the experts may disagree on the probability (uncertainty) of a certain event or they may disagree on the direction of causality between two events (e.g., one thinks A causes B while another thinks B causes A); the experts may even disagree on the entire structure of dependencies among a set of variables in a (probabilistic) network. The challenge here is to develop a semantically sound and computationally effective methodology that explicitly accounts for the uncertainty and conflicts. In our solution to this problem, we represent the knowledge-based models as Bayesian Knowledge Bases (BKBs) and provide an algorithm called Bayesian knowledge fusion that allows the fusion of multiple BKBs into a single BKB that retains the information from all input sources. This allows for easy aggregation and de-aggregation of information from multiple expert sources and facilitates multi-expert/source decision making by providing a framework in which all opinions can be preserved and reasoned over.

Fusing Multiple Sources of Conflicting Information

部门:

计算机科学系

讲者:

SANTOS, Eugene

发布日期:

2011

影片长度:

83 分钟

影片类型:

学术论坛

In this talk, I address the challenges of information/knowledge fusion from multiple (possibly conflicting) sources. For example, consider that there are multiple experts (sources) providing knowledge-based models of the same scenario/situation and we wish to aggregate this information in order to assist in decision-making. There are several problems we may run into by naively merging the information from each source - the experts may disagree on the probability (uncertainty) of a certain event or they may disagree on the direction of causality between two events (e.g., one thinks A causes B while another thinks B causes A); the experts may even disagree on the entire structure of dependencies among a set of variables in a (probabilistic) network. The challenge here is to develop a semantically sound and computationally effective methodology that explicitly accounts for the uncertainty and conflicts. In our solution to this problem, we represent the knowledge-based models as Bayesian Knowledge Bases (BKBs) and provide an algorithm called Bayesian knowledge fusion that allows the fusion of multiple BKBs into a single BKB that retains the information from all input sources. This allows for easy aggregation and de-aggregation of information from multiple expert sources and facilitates multi-expert/source decision making by providing a framework in which all opinions can be preserved and reasoned over.

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