This paper investigates the application of Evolutionary Computation to the induction of generalis... more This paper investigates the application of Evolutionary Computation to the induction of generalised policies. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural evolution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy iteration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantages (including optimisation of rule order within a policy) of our system.
L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training ... more L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training data. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domain-specific and is used in conjunction with an inference mechanism that stipulates which rule to apply and which variable bindings to implement in order to formulate plans for problems within that domain.
An overview of the application of evolutionary computation to fuzzy knowledge discovery is presen... more An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques ...
This paper investigates the application of Evolutionary Co mputation to the induction of generali... more This paper investigates the application of Evolutionary Co mputation to the induction of generalised policies. A policy s here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural ev olution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy it eration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantag es (including optimisation of rule order within a policy) of ou r
M. Galea, Q. Shen and V. Singh. Encouraging Complementary Fuzzy Rules within Iterative Rule Learn... more M. Galea, Q. Shen and V. Singh. Encouraging Complementary Fuzzy Rules within Iterative Rule Learning. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 15-22.
Modern scientific collaborations have opened up the opportunity to solve complex problems that re... more Modern scientific collaborations have opened up the opportunity to solve complex problems that require both multidisciplinary expertise and large-scale computational experiments. These experiments typically consist of a sequence of processing steps that need to be executed on selected computing platforms. Execution poses a challenge, however, due to (1) the complexity and diversity of applications , (2) the diversity of analysis goals , (3) the heterogeneity of computing platforms , and (4) the volume and distribution of data . A common strategy to make these in silico experiments more manageable is to model them as workflows and to use a workflow management system to organize their execution. This article looks at the overall challenge posed by a new order of scientific experiments and the systems they need to be run on, and examines how this challenge can be addressed by workflows and workflow management systems. It proposes a taxonomy of workflow management system (WMS) character...
Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population... more Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population-based algorithms (SPBAs) such as Ant Colony Optimisation and genetic algorithms. Several SPBAs are run in succession with the result of each being a rule added to an emerging final ruleset. Between SPBA runs, cases in the training set that are covered by the newly evolved rule are generally removed, so as to encourage the next SPBA to find good rules describing the remaining cases. This paper compares this IRL variant with another variant that instead weights cases between iterations. The latter approach results in improved classification accuracy and an increased robustness to parameter value changes.
Simultaneous Ant Colony Optimization Algorithms for Learning Linguistic Fuzzy Rules
Studies in Computational Intelligence, 2006
... knowledge, refine an incomplete or inaccurate domain theory, provide confidence in theautomat... more ... knowledge, refine an incomplete or inaccurate domain theory, provide confidence in theautomated system now ... to core data mining tasks such as clustering (eg [16, 17]), feature selection (eg [18 ... relates to line (3), and the sixth to line (4): 1. An appropriate problem representation ...
2015 IEEE 11th International Conference on e-Science, 2015
The VERCE project has pioneered an e-Infrastructure to support researchers using established simu... more The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.
This paper investigates the application of Evolutionary Computation to the induction of generalis... more This paper investigates the application of Evolutionary Computation to the induction of generalised policies. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural evolution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy iteration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantages (including optimisation of rule order within a policy) of our system.
Evolutionary-based learning of generalised policies for AI planning domains
... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Gl... more ... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Glasgow G1 1XH, UK [email protected] ... The expectation is that if a learned policy πn performs well on problems drawn from random walks of length n, then it will provide ...
Data-intensive architecture for scientific knowledge discovery
Distributed and Parallel Databases, 2012
ABSTRACT This paper presents a data-intensive architecture that demonstrates the ability to suppo... more ABSTRACT This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.
Evolutionary-based learning of generalised policies for AI planning domains
... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Gl... more ... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Glasgow G1 1XH, UK [email protected] ... The expectation is that if a learned policy πn performs well on problems drawn from random walks of length n, then it will provide ...
L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training ... more L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training data. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domain-specific and is used in conjunction with an inference mechanism that stipulates which rule to apply and which variable bindings to implement in order to formulate plans for problems within that domain.
This paper investigates the application of Evolutionary Computation to the induction of generalis... more This paper investigates the application of Evolutionary Computation to the induction of generalised policies. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural evolution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy iteration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantages (including optimisation of rule order within a policy) of our system.
This paper investigates the application of Evolutionary Computation to the induction of generalis... more This paper investigates the application of Evolutionary Computation to the induction of generalised policies. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural evolution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy iteration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantages (including optimisation of rule order within a policy) of our system.
L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training ... more L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training data. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domain-specific and is used in conjunction with an inference mechanism that stipulates which rule to apply and which variable bindings to implement in order to formulate plans for problems within that domain.
An overview of the application of evolutionary computation to fuzzy knowledge discovery is presen... more An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques ...
This paper investigates the application of Evolutionary Co mputation to the induction of generali... more This paper investigates the application of Evolutionary Co mputation to the induction of generalised policies. A policy s here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural ev olution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy it eration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantag es (including optimisation of rule order within a policy) of ou r
M. Galea, Q. Shen and V. Singh. Encouraging Complementary Fuzzy Rules within Iterative Rule Learn... more M. Galea, Q. Shen and V. Singh. Encouraging Complementary Fuzzy Rules within Iterative Rule Learning. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 15-22.
Modern scientific collaborations have opened up the opportunity to solve complex problems that re... more Modern scientific collaborations have opened up the opportunity to solve complex problems that require both multidisciplinary expertise and large-scale computational experiments. These experiments typically consist of a sequence of processing steps that need to be executed on selected computing platforms. Execution poses a challenge, however, due to (1) the complexity and diversity of applications , (2) the diversity of analysis goals , (3) the heterogeneity of computing platforms , and (4) the volume and distribution of data . A common strategy to make these in silico experiments more manageable is to model them as workflows and to use a workflow management system to organize their execution. This article looks at the overall challenge posed by a new order of scientific experiments and the systems they need to be run on, and examines how this challenge can be addressed by workflows and workflow management systems. It proposes a taxonomy of workflow management system (WMS) character...
Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population... more Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population-based algorithms (SPBAs) such as Ant Colony Optimisation and genetic algorithms. Several SPBAs are run in succession with the result of each being a rule added to an emerging final ruleset. Between SPBA runs, cases in the training set that are covered by the newly evolved rule are generally removed, so as to encourage the next SPBA to find good rules describing the remaining cases. This paper compares this IRL variant with another variant that instead weights cases between iterations. The latter approach results in improved classification accuracy and an increased robustness to parameter value changes.
Simultaneous Ant Colony Optimization Algorithms for Learning Linguistic Fuzzy Rules
Studies in Computational Intelligence, 2006
... knowledge, refine an incomplete or inaccurate domain theory, provide confidence in theautomat... more ... knowledge, refine an incomplete or inaccurate domain theory, provide confidence in theautomated system now ... to core data mining tasks such as clustering (eg [16, 17]), feature selection (eg [18 ... relates to line (3), and the sixth to line (4): 1. An appropriate problem representation ...
2015 IEEE 11th International Conference on e-Science, 2015
The VERCE project has pioneered an e-Infrastructure to support researchers using established simu... more The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.
This paper investigates the application of Evolutionary Computation to the induction of generalis... more This paper investigates the application of Evolutionary Computation to the induction of generalised policies. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural evolution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy iteration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantages (including optimisation of rule order within a policy) of our system.
Evolutionary-based learning of generalised policies for AI planning domains
... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Gl... more ... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Glasgow G1 1XH, UK [email protected] ... The expectation is that if a learned policy πn performs well on problems drawn from random walks of length n, then it will provide ...
Data-intensive architecture for scientific knowledge discovery
Distributed and Parallel Databases, 2012
ABSTRACT This paper presents a data-intensive architecture that demonstrates the ability to suppo... more ABSTRACT This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.
Evolutionary-based learning of generalised policies for AI planning domains
... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Gl... more ... Michelle Galea Department of Computer & Information Sciences University of Strathclyde Glasgow G1 1XH, UK [email protected] ... The expectation is that if a learned policy πn performs well on problems drawn from random walks of length n, then it will provide ...
L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training ... more L2Plan2 is an evolution-inspired system for inducing generalised planning policies from training data. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domain-specific and is used in conjunction with an inference mechanism that stipulates which rule to apply and which variable bindings to implement in order to formulate plans for problems within that domain.
This paper investigates the application of Evolutionary Computation to the induction of generalis... more This paper investigates the application of Evolutionary Computation to the induction of generalised policies. A policy is here defined as a list of rules that specify which actions to be performed under which conditions. A policy is domainspecific and is used in conjunction with an inference mechanism (to decide which rule to apply) to formulate plans for problems within that domain. Evolutionary Computation is concerned with the design and application of stochastic population-based iterative methods inspired by natural evolution. This work illustrates how it may be applied to the induction of policies, compares the results on one domain with those obtained by a state-of-the-art approximate policy iteration approach, and highlights both the current limitations (such as a simplistic knowledge representation) and the advantages (including optimisation of rule order within a policy) of our system.
Uploads
Papers by Michelle Galea