4 research outputs found

    An on-line Pittsburgh LCS for the Three-Cornered Coevolution Framework

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    The Three-Cornered Coevolution Framework describes a method that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation agent and two classification agents) learn and adapt to the changes of the problems without human involvement. Here, artificial problems can be generated in concert with classification agents in order to provide insight into their relationships. Previous work on the Two-Cornered Coevolution Framework provided foundation for implementing the system that was able to set-up the problem’s difficulty appropriately while triggering the coevolutionary process. However, the triggering process was set manually without utilising the third agent as proposed in the original framework to perform this task. Previous work on the Three-Cornered Coevolution introduced the third agent (a new classification agent) to trigger the coevolutionary process within the system, where its functionality and effect on the system requires investigation. This paper details the implementation for this case; two classification agents that use different styles of learning techniques (e.g. supervised versus reinforcement learning techniques) is adapted in the classification agents to learn the various classification problems. Ultimately, Learning Classifier System (LCS) is chosen to be implemented in the participating agents. LCS has several potential characteristics, such as interpretability, generalization capability and variations in representation, that are suitable for the system. Experiments show that the Pittsburgh-style LCS with the adaptation of Tabu Search technique in S capable to autonomously adjust the problem’s difficulty and generate a wide range of problems for classification. The adaptation of A-PLUS to an ‘on-line’ system is successful implemented. Further, the classification agents (i.e. R and I) are able to solve the classification tasks where the classification performance are varied. The Three-Cornered Coevolution Framework offers a great potential for autonomous learning and provides useful insight into coevolution learning over the standard studies of pattern recognition. The system is capable of autonomously generating various problems, learning and providing insight into each learning system’s ability by determining the problem domains where they perform relatively well. This is in contrast to humans having to determine the problem domains.</p

    Three-cornered coevolution learning classifier systems for classification

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    This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation agent and two classification agents) learn and adapt to the changes of the problems without human involvement. In existing pattern classification systems, humans usually play a major role in creating and controlling the problem domain. In particular, humans set up and tune the problem’s difficulty. A motivation of the work for this thesis is to design and develop an automatic pattern generation and classification system that can generate various sets of exemplars to be learned from and perform the classification tasks autonomously. The system should be able to automatically adjust the problem’s difficulty based on the learners’ ability to learn (e.g. determining features in the problem that affect the learners’ performance in order to generate various problems for classification at different levels of difficulty). Further, the system should be capable of addressing the classification tasks through coevolution (coadaptive evolution), where the participating agents learn and adapt to the changes of the problems without human participation. Ultimately, Learning Classifier System (LCS) is chosen to be implemented in the participating agents. LCS has several potential characteristics, such as interpretability, generalisation capability and variations in representation, that are suitable for the system. The work can be broken down into three main phases. Phase 1 is to develop an automated evolvable problem generator to autonomously generate various problems for classification, Phase 2 is to develop the Two-Cornered Coevolution System for classification, and Phase 3 is to develop the Three-Cornered Coevolution System for classification. Phase 1 is necessary in order to create a set of problem domains for classification (i.e. image-based data or artificial data) that can be generated automatically, where the difficulty levels of the problem can be adjusted and tuned. Phase 2 is needed to investigate the generation agent’s ability to autonomously tune and adjust the problem’s difficulty based on the classification agent’s performance. Phase 2 is a standard coevolution system, where two different agents evolve to adapt to the changes of the problem. The classification agent evolves to learn various classification problems, while the generation agent evolves to tune and adjust the problem’s difficulty based on the learner’s ability to learn. Phase 3 is the final research goal. This phase develops a new coevolution system where three different agents evolve to adapt to the changes of the problem. Both of the classification agents evolve to learn various classification problems, while the generation agent evolves to tune and adjust the problem’s difficulty based on the classification agents’ ability to learn. The classification agents use different styles of learning techniques (i.e. supervised or reinforcement learning techniques) to learn the problems. Based on the classification agents’ ability (i.e. the difference in performance between the classification agents) the generation agent adjusts and creates various problems for classification at different levels of difficulty (i.e. various ‘hard’ problems). The Three-Cornered Coevolution System offers a great potential for autonomous learning and provides useful insight into coevolution learning over the standard studies of pattern recognition. The system is capable of autonomously generating various problems, learning and providing insight into each learning system’s ability by determining the problem domains where they perform relatively well. This is in contrast to humans having to determine the problem domains
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