The Experts below are selected from a list of 22056 Experts worldwide ranked by ideXlab platform

Ramon Lopez De Mantaras - One of the best experts on this subject based on the ideXlab platform.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    International Syposium on Methodologies for Intelligent Systems, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This paper outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. The aim of this work is to define a general systematic method to verify the effective collaboration among the members of a team and to compare the different multi-agent behaviours, using external observations of a Multi-Agent System. Observing and analysing the behavior of a such system is a difficult task. Our approach allows to learn sequential behaviours from raw multi-agent observations of a dynamic, complex environment, represented by a set of sequences expressed in first-order logic. In order to discover the underlying knowledge to characterise team behaviours, we propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences. We compared the performance of two soccer teams in a simulated environment, each based on very different behavioural approaches: While one uses a more deliberative strategy, the other one uses a pure reactive one.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    Lecture Notes in Computer Science, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This report outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a Relational Representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.

Grazia Bombini - One of the best experts on this subject based on the ideXlab platform.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    International Syposium on Methodologies for Intelligent Systems, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This paper outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. The aim of this work is to define a general systematic method to verify the effective collaboration among the members of a team and to compare the different multi-agent behaviours, using external observations of a Multi-Agent System. Observing and analysing the behavior of a such system is a difficult task. Our approach allows to learn sequential behaviours from raw multi-agent observations of a dynamic, complex environment, represented by a set of sequences expressed in first-order logic. In order to discover the underlying knowledge to characterise team behaviours, we propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences. We compared the performance of two soccer teams in a simulated environment, each based on very different behavioural approaches: While one uses a more deliberative strategy, the other one uses a pure reactive one.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    Lecture Notes in Computer Science, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This report outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a Relational Representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.

Danushka Bollegala - One of the best experts on this subject based on the ideXlab platform.

  • why does pairdiff work a mathematical analysis of bilinear Relational compositional operators for analogy detection
    International Conference on Computational Linguistics, 2017
    Co-Authors: Huda Hakami, Kohei Hayashi, Danushka Bollegala
    Abstract:

    Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion and Relational information retrieval. A simple, yet surprisingly accurate method for representing a relation between two words is to compute the vector offset (PairDiff) between their corresponding word embeddings. Despite the empirical success, it remains unclear as to whether PairDiff is the best operator for obtaining a Relational Representation from word embeddings. We conduct a theoretical analysis of generalised bilinear operators that can be used to measure the l2 Relational distance between two word-pairs. We show that, if the word embed- dings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where PairDiff is a special case. For numerous word embedding types, we empirically verify the uncorrelation assumption, demonstrating the general applicability of our theoretical result. Moreover, we experimentally discover PairDiff from the bilinear Relational compositional operator on several benchmark analogy datasets.

  • why pairdiff works a mathematical analysis of bilinear Relational compositional operators for analogy detection
    arXiv: Computation and Language, 2017
    Co-Authors: Huda Hakami, Danushka Bollegala, Hayashi Kohei
    Abstract:

    Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion and Relational information retrieval. A simple, yet surprisingly accurate method for representing a relation between two words is to compute the vector offset (\PairDiff) between their corresponding word embeddings. Despite the empirical success, it remains unclear as to whether \PairDiff is the best operator for obtaining a Relational Representation from word embeddings. We conduct a theoretical analysis of generalised bilinear operators that can be used to measure the $\ell_{2}$ Relational distance between two word-pairs. We show that, if the word embeddings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where \PairDiff is a special case. For numerous word embedding types, we empirically verify the uncorrelation assumption, demonstrating the general applicability of our theoretical result. Moreover, we experimentally discover \PairDiff from the bilinear relation composition operator on several benchmark analogy datasets.

Raquel Ros - One of the best experts on this subject based on the ideXlab platform.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    International Syposium on Methodologies for Intelligent Systems, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This paper outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. The aim of this work is to define a general systematic method to verify the effective collaboration among the members of a team and to compare the different multi-agent behaviours, using external observations of a Multi-Agent System. Observing and analysing the behavior of a such system is a difficult task. Our approach allows to learn sequential behaviours from raw multi-agent observations of a dynamic, complex environment, represented by a set of sequences expressed in first-order logic. In order to discover the underlying knowledge to characterise team behaviours, we propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences. We compared the performance of two soccer teams in a simulated environment, each based on very different behavioural approaches: While one uses a more deliberative strategy, the other one uses a pure reactive one.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    Lecture Notes in Computer Science, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This report outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a Relational Representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.

Stefano Ferilli - One of the best experts on this subject based on the ideXlab platform.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    International Syposium on Methodologies for Intelligent Systems, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This paper outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. The aim of this work is to define a general systematic method to verify the effective collaboration among the members of a team and to compare the different multi-agent behaviours, using external observations of a Multi-Agent System. Observing and analysing the behavior of a such system is a difficult task. Our approach allows to learn sequential behaviours from raw multi-agent observations of a dynamic, complex environment, represented by a set of sequences expressed in first-order logic. In order to discover the underlying knowledge to characterise team behaviours, we propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences. We compared the performance of two soccer teams in a simulated environment, each based on very different behavioural approaches: While one uses a more deliberative strategy, the other one uses a pure reactive one.

  • analysing the behaviour of robot teams through Relational sequential pattern mining
    Lecture Notes in Computer Science, 2011
    Co-Authors: Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez De Mantaras
    Abstract:

    This report outlines the use of a Relational Representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a Relational Representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a Relational learning algorithm to mine meaningful frequent patterns among the Relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.