Cognitive Maps for Knowledge Represenation and Reasoning

Sedki, Karima; Beaufort, Louis Bonneau de

doi:10.1109/ictai.2012.175

Cited by 16 publications

(11 citation statements)

References 7 publications

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“…For example, having an observation about an increase in the number of fishers in a fishing activity problem, only some arguments and attacks can be active. Works dealing with transformations from cognitive maps into other formalisms exist such as those proposed in [10], [11], [14]. In particular, in these works, the cognitive map is either integrated or transformed into Bayesian network.…”

Section: Discussionmentioning

confidence: 99%

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Constructing Weighted Argumentation Framework with Cognitive Maps

Sedki

Beaufort

2015

2015 IEEE 27th International Conference on Tools With Artificial Intelligence (ICTAI)

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International audienceCognitive map is a qualitative decision model which is frequently used in social science and decision making applications. This model allows to easily organize individuals’ judgments, thinking or beliefs about a given problem in a graphical representation containing different concepts and influences between them. However, reasoning on this model presents some limits and remains a difficult task. For example, cognitive maps donot model uncertainty within the variables, and only deductive reasoning (predicting an effect given a cause) is possible. In this paper, we show how to translate the knowledge represented in cognitive maps in the form of arguments and attack relations among them. In particular, given a decision problem, a cognitive map was first built by eliciting knowledge from experts and then transforming it in a weighted argumentation framework (WAFfor short) for ensuring efficient reasoning. Another contribution of this paper concerns enriching the WAF obtained from a given cognitive map for dealing with dynamics through the consideration of a varying set of observations

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Section: Discussionmentioning

confidence: 99%

“…It expresses individuals' judgments, thinking or beliefs about a given problem [8], [4]. In this paper, we use the formalism presented in [14] where influence relations are not necessary symmetric, contrary to the original formalism.…”

Section: Cognitive Mapsmentioning

confidence: 99%

Constructing Weighted Argumentation Framework with Cognitive Maps

Sedki

Beaufort

2015

2015 IEEE 27th International Conference on Tools With Artificial Intelligence (ICTAI)

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“…Instead, a decrease in ASP will have no effect on shellfish catches. Therefore, as originally proposed in [Sedki and Bonneau 2012], we enrich the formalism by assigning two values to each causal relation [V 1 , V 2 ].…”

Section: Enriched Causal Map Formalismmentioning

confidence: 99%

“…In [Sucheta et al 2004], the authors ask experts about the elicitation of the CPTs. We propose, as in [Sedki and Bonneau 2012], to take advantage of the causal values given in the CM to compute the conditional probabilities. But because we need to enable stable models (in fact the depicted system has been steady since the Tinduff hatchery opened in the late 90' and an annual planting system was introduced) we have enriched the original proposal.…”

Section: Computing Combined Probability Tablesmentioning

confidence: 99%

Inference reasoning on fishers' knowledge using Bayesian causal maps

Beaufort

Sedki

Fontenelle

2015

Ecological Informatics

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Scientists and managers are not the only holders of knowledge regarding environmental issues: other stakeholders such as farmers or fishers do have empirical and relevant knowledge. Thus, new approaches for knowledge representation in the case of multiple knowledge sources, but still enabling reasoning, are needed. Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying. If they offer an easy means to express individuals judgments, drawing inferences in cognitive maps remains a difficult task. Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate. To take advantage of each formalism and to overcome their drawbacks, Bayesian causal maps have been developed. In this approach, cognitive maps are used to build the network and obtain conditional probability tables. We propose here a complete framework applied on a real problem. From the different views of a group of shellfish dredgers about their activity, we derive a decision facilitating tool, enabling scenarios testing for fisheries management.qualitative modelling, cognitive maps, bayesian networks, fisher's knowledge, fisheries management, qualitative decision support 1

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“…Thus, the influence should have two values: one for the state X A = +, and one for the state X A = −. To consider this fact, [23] allows to label each influence with two values. However, we want only one value for each influence in the cognitive map, in order to keep the simplicity of the model.…”

Section: Semantics Of the Influencesmentioning

confidence: 99%