Machine Learning and Agents

Jędrzejowicz, Piotr

doi:10.1007/978-3-642-22000-5_2

Cited by 8 publications

(4 citation statements)

References 56 publications

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“…In the intersection between multi-agent systems and machine learning we find the so-called agent-based machine learning techniques where agent technology is applied to solve machine learning problems. According to Jedrzejowicz (Jedrzejowicz, 2011), there are several ways in which the research of machine learning can profit from the application of agent technology:…”

Section: Agent-based Machine Learningmentioning

confidence: 99%

“…According to (Jedrzejowicz, 2011), agent technology has brought to machine learning several capabilities including parallel computation, scalability and interoperability. In general, agent based solutions can be used to develop more flexible machine learning tools.…”

Section: Agent-based Machine Learningmentioning

confidence: 99%

“…In general, agent based solutions can be used to develop more flexible machine learning tools. For the state of the art of agent-based machine learning see (Jedrzejowicz, 2011).…”

Section: Agent-based Machine Learningmentioning

confidence: 99%

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Learning, Agents and Formal Languages - State of the Art

Becerra-Bonache¹,

Jiménez-López²

2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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The paper presents the state of the art of machine learning, agent technologies and formal languages, not considering them as isolated research areas, but focusing on the relationship among them. First, we consider the relationship between learning and agents. Second, the relationship between machine learning and formal languages. And third, the relationship between agents and formal languages. Finally, we point to some promising directions on the intersection among these three areas.

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Section: Agent-based Machine Learningmentioning

confidence: 99%

Section: Agent-based Machine Learningmentioning

confidence: 99%

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Learning, Agents and Formal Languages - State of the Art

Becerra-Bonache¹,

Jiménez-López²

2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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“…During the last decade, developments in the fields of ML and agent technologies have, in some respect, become complementary and researchers from both fields have seen ample opportunities to profit from solutions proposed by each other [51]. The central goal of ML is to construct a complete intelligent agent that can perceive its environment, generate plans, execute those plans and communicate with other agents [63].…”

Section: Machine Learning and Agentsmentioning

confidence: 99%

Three-cornered coevolution learning classifier systems for classification

Marzukhi¹

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<p>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.</p>

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