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Stéphane Loiseau - One of the best experts on this subject based on the ideXlab platform.

  • Validation of a Cognitive Map
    Proceedings of the 6th International Conference on Agents and Artificial Intelligence, 2014
    Co-Authors: Aymeric Le Dorze, David Genest, Laurent Garcia, Stéphane Loiseau
    Abstract:

    A Cognitive Map is a knowledge representation model. Knowledge is represented as a graph where nodes represent concepts and arcs represent influences between these concepts. Each influence has a value that quantifies it. Despite the fact that a Cognitive Map is quite simple to build, some influence values may contradict each other. This paper provides some quality criteria in order to validate a Cognitive Map. There are two kinds of quality criteria. The verification validates a Cognitive Map by computing its internal coherency. The test validates a Map from a set of constraints provided by the designer. These criteria indicate if a Map does or does not contain contradictions. We also propose a way to adapt these criteria according to the possible values that an influence can take

  • Probabilistic Cognitive Maps Semantics of a Cognitive Map when the Values are Assumed to be Probabilities
    2014
    Co-Authors: Aymeric Le Dorze, David Genest, Laurent Garcia, Béatrice Duval, Philippe Leray, Stéphane Loiseau
    Abstract:

    Cognitive Maps are a knowledge representation model that describes influences between concepts by a graph, where each influence is quantified by a value. The values are generally not formally defined. In this paper, we introduce a new Cognitive Map model, the probabilistic Cognitive Maps. In such Maps, the values of the influences are interpreted as probability values. We define formally the semantics of this model. We also provide an operation to compute the global influence of a concept on any other one, called the probabilistic propagated influence. To show that our model is valid, we propose a procedure to represent a probabilistic Cognitive Map as a Bayesian network. This new model strengthens Cognitive Maps by giving them strong semantics. Moreover, it acts as a bridge between Cognitive Maps and Bayesian networks.

  • ICAART (1) - Probabilistic Cognitive Maps Semantics of a Cognitive Map when the Values are Assumed to be Probabilities
    2014
    Co-Authors: Aymeric Le Dorze, David Genest, Laurent Garcia, Béatrice Duval, Philippe Leray, Stéphane Loiseau
    Abstract:

    Cognitive Maps are a knowledge representation model that describes influences between concepts by a graph, where each influence is quantified by a value. The values are generally not formally defined. In this paper, we introduce a new Cognitive Map model, the probabilistic Cognitive Maps. In such Maps, the values of the influences are interpreted as probability values. We define formally the semantics of this model. We also provide an operation to compute the global influence of a concept on any other one, called the probabilistic propagated influence. To show that our model is valid, we propose a procedure to represent a probabilistic Cognitive Map as a Bayesian network. This new model strengthens Cognitive Maps by giving them strong semantics. Moreover, it acts as a bridge between Cognitive Maps and Bayesian networks.

  • ONTOLOGICAL Cognitive Map
    International Journal on Artificial Intelligence Tools, 2009
    Co-Authors: Lionel Chauvin, David Genest, Stéphane Loiseau
    Abstract:

    A Cognitive Map model provides a graphical representation of an influence network between concepts. One drawback of this model is that large Cognitive Maps are difficult to exploit and understand. This paper introduces an ontological Cognitive Map model that enables the designer to organize concepts in an ontology. On the one hand, this model provides an ontological influence mechanism that shows the influence from any concept of the ontology to any other according to the Map. The Map is then easier to exploit. On the other hand, the ontology is used for providing a synthetical view of a Map. The Map is then easier to understand.

  • ICTAI (2) - Ontological Cognitive Map
    2008 20th IEEE International Conference on Tools with Artificial Intelligence, 2008
    Co-Authors: Lionel Chauvin, David Genest, Stéphane Loiseau
    Abstract:

    A Cognitive Map model provides a graphical representation of an influence network between concepts. One drawback of this model is that large Cognitive Maps are difficult to exploit and understand.This paper introduces an ontological Cognitive Map model that enables the designer to organize concepts in an ontology. On one hand, this model provides an ontological influence mechanism that shows the influence from any concept of the ontology to any other according to the Map. The Map is then easier to exploit. On the other hand, the ontology is used as a scale for providing a synthetical view of a Map. The Map is then easier to understand

Kuokkwee Wee - One of the best experts on this subject based on the ideXlab platform.

  • A method for root cause analysis with a Bayesian belief network and fuzzy Cognitive Map
    Expert Systems with Applications, 2015
    Co-Authors: Yit Yin Wee, Wooi Ping Cheah, Shing Chiang Tan, Kuokkwee Wee
    Abstract:

    People often want to know the root cause of things and events in certain application domains such as intrusion detection, medical diagnosis, and fault diagnosis. In many of these domains, a large amount of data is available. The problem is how to perform root cause analysis by leveraging the data asset at hand. Root cause analysis consists of two main functions, diagnosis of the root cause and prognosis of the effect. In this paper, a method for root cause analysis is proposed. In the first phase, a causal knowledge model is constructed by learning a Bayesian belief network (BBN) from data. BBN’s backward and forward inference mechanisms are used for the diagnosis and prognosis of the root cause. Despite its powerful reasoning capability, the representation of causal strength in BBN as a set of probability values in a conditional probability table (CPT) is not intuitive at all. It is at its worst when the number of probability values needed grows exponentially with the number of variables involved. Conversely, a fuzzy Cognitive Map (FCM) can provide an intuitive interface as the causal strength is simply represented by a single numerical value. Hence, in the second phase of the method, an intuitive interface using FCM is generated from the BBN-based causal knowledge model, applying the migration framework proposed and formulated in this paper

  • a method for root cause analysis with a bayesian belief network and fuzzy Cognitive Map
    Expert Systems With Applications, 2015
    Co-Authors: Yit Yin Wee, Wooi Ping Cheah, Shing Chiang Tan, Kuokkwee Wee
    Abstract:

    We verify experimentally the accuracy of learning BBN causal model from data.We demonstrate the effectiveness of root cause analysis using BBN.We formulate a method for migrating BBN to FCM.We show the intuitiveness of causal knowledge model represented using FCM. People often want to know the root cause of things and events in certain application domains such as intrusion detection, medical diagnosis, and fault diagnosis. In many of these domains, a large amount of data is available. The problem is how to perform root cause analysis by leveraging the data asset at hand. Root cause analysis consists of two main functions, diagnosis of the root cause and prognosis of the effect. In this paper, a method for root cause analysis is proposed. In the first phase, a causal knowledge model is constructed by learning a Bayesian belief network (BBN) from data. BBN's backward and forward inference mechanisms are used for the diagnosis and prognosis of the root cause. Despite its powerful reasoning capability, the representation of causal strength in BBN as a set of probability values in a conditional probability table (CPT) is not intuitive at all. It is at its worst when the number of probability values needed grows exponentially with the number of variables involved. Conversely, a fuzzy Cognitive Map (FCM) can provide an intuitive interface as the causal strength is simply represented by a single numerical value. Hence, in the second phase of the method, an intuitive interface using FCM is generated from the BBN-based causal knowledge model, applying the migration framework proposed and formulated in this paper.

Philippe De Maeyer - One of the best experts on this subject based on the ideXlab platform.

Wladyslaw Homenda - One of the best experts on this subject based on the ideXlab platform.

  • clustering techniques for fuzzy Cognitive Map design for time series modeling
    Neurocomputing, 2017
    Co-Authors: Wladyslaw Homenda, Agnieszka Jastrzebska
    Abstract:

    Abstract This study presents an approach to time series modeling with Fuzzy Cognitive Maps. In the paper we focus on initial modeling phase: Map nodes selection. The research objective was to introduce algorithmic means to evaluate Fuzzy Cognitive Map design before training phase. We posed a hypothesis that application of cluster validity indexes could serve us in this endeavor. In order to validate the proposed approach we have conducted a suite of experiments on various time series, both synthetic and real-world. Five cluster validity indexes turned out to be especially valuable in our study. Results show that Fuzzy Cognitive Maps designed using one of the five selected indexes have superior quality. First, they are easy to interpret, because Map nodes are related with the underlying data points. Second, after we train such Maps, it turns out that the numerical quality of their predictions outrivals Maps with other designs.

  • Joining Concept’s Based Fuzzy Cognitive Map Model with Moving Window Technique for Time Series Modeling
    2014
    Co-Authors: Wladyslaw Homenda, Agnieszka Jastrzebska, Witold Pedrycz
    Abstract:

    In the article we present a technique for time series modeling that joins concepts based Fuzzy Cognitive Map design with moving window approach. Proposed method first extracts concepts that generalize the underlying time series. Next, we form a Map that consists of several layers representing consecutive time points. In each layer we place concepts obtained in the previous step. Fuzzified time series is passed to the Map according to the moving window scheme. We investigate two most important aspects of this procedure: division into concepts and window size and their influence on model’s accuracy. Firstly, we show that extraction of concepts plays a big role. Fitted models have low errors. Unfortunately, it is not always possible to extract appropriate number of concepts. The choice of the number of concepts is a compromise between model size and accuracy. Secondly, we show that increasing window size improves modeling accuracy.

  • Fuzzy Cognitive Map Reconstruction
    Proceedings of the 6th International Conference on Agents and Artificial Intelligence, 2014
    Co-Authors: Wladyslaw Homenda, Agnieszka Jastrzebska, Witold Pedrycz
    Abstract:

    The paper is focused on fuzzy Cognitive Maps - abstract soft computing models, which can be applied to model complex systems with uncertainty. The authors present two distinct methodologies for fuzzy Cognitive Map reconstruction. Both theoretical and practical issues involved in the process of a Map reconstruction are discussed. Among researched and described aspects are: Map sizes, data dimensionality, distortions, optimization procedure, etc. Theoretical results are supported by a series of experiments, that allow to evaluate the quality of the developed approach. The authors compare both procedures characteristics and discuss practical issues, that are entailed in the developed methodology. The goal of this study is to investigate theoretical and practical problems, that are relevant in the fuzzy Cognitive Map reconstruction process. Proposed two methodologies for FCM reconstruction are based on gradient learning. A series of experiments allows to illustrate important characteristics of the fuzzy Cognitive Map reconstruction procedure.

Yuji Naya - One of the best experts on this subject based on the ideXlab platform.

  • medial prefrontal cortex represents the object based Cognitive Map when remembering an egocentric target location
    Cerebral Cortex, 2020
    Co-Authors: Bo Zhang, Yuji Naya
    Abstract:

    A Cognitive Map, representing an environment around oneself, is necessary for spatial navigation. However, compared with its constituent elements such as individual landmarks, neural substrates of coherent spatial information, which consists in a relationship among the individual elements, remain largely unknown. The present study investigated how the brain codes Map-like representations in a virtual environment specified by the relative positions of three objects. Representational similarity analysis revealed an object-based spatial representation in the hippocampus (HPC) when participants located themselves within the environment, while the medial prefrontal cortex (mPFC) represented it when they recollected a target object's location relative to their self-body. During recollection, task-dependent functional connectivity increased between the two areas implying exchange of self-location and target location signals between the HPC and mPFC. Together, the object-based Cognitive Map, whose coherent spatial information could be formed by objects, may be recruited in the HPC and mPFC for complementary functions during navigation, which may generalize to other aspects of cognition, such as navigating social interactions.

  • medial prefrontal cortex represents the object based Cognitive Map when remembering an egocentric target location
    bioRxiv, 2019
    Co-Authors: Bo Zhang, Yuji Naya
    Abstract:

    Abstract A Cognitive Map, representing an environment around oneself, is necessary for spatial navigation. However, compared with its constituent elements such as individual landmarks, neural substrates of coherent spatial information remain largely unknown. The present study investigated how the brain codes Map-like representations in a virtual environment specified by the relative positions of three objects. Representational similarity analysis revealed an object-based spatial representation in the hippocampus (HPC) when participants located themselves within the environment, while the medial prefrontal cortex (mPFC) represented it when they recollected a target object’s location relative to their self-body. During recollection, task-dependent functional connectivity increased between the two areas implying exchange of self- and target-location signals between the HPC and mPFC. Together, the coherent Cognitive Map, which could be formed by objects, may be recruited in the HPC and mPFC for complementary functions during navigation, which may generalize to other aspects of cognition, such as navigating social interactions.

  • object based Cognitive Map in the human hippocampus and medial prefrontal cortex
    bioRxiv, 2019
    Co-Authors: Bo Zhang, Yuji Naya
    Abstract:

    A Cognitive Map, representing an environment around oneself, is necessary for spatial navigation. However, compared with its constituent elements such as individual landmarks, neural substrates of the coherent spatial information remain largely unknown. The present study investigated how the brain codes Map-like representations in a virtual environment specified by relative positions of three objects. Representational similarity analysis revealed the object-based spatial environment in the hippocampus (HPC) when participants located their self-positions within it, while the medial prefrontal cortex (mPFC) represented it when they recollected a target object location relative to their self-body. During the recollection, task-dependent functional connectivity increased between the two areas implying exchange of self- and target-location signals between HPC and mPFC. Together, the coherent Cognitive Map may be recruited in HPC and mPFC for complementary functions when we relate ourselves with a target object including person for navigation, and presumably for social interactions.