For Real! XCS with Continuous-Valued Inputs

Many real-world problems are not conveniently expressed using the ternary representation typically used by Learning Classifier Systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations recently proposed for XCS, together with their associated operators and find evidence of considerable representational and operator bias. We propose a new interval-based representation that is more straightforward than the previous ones and analyse its bias. The representations presented and their analysis are also applicable to other Learning Classifier System architectures. We discuss limitations of the real multiplexer problem, a benchmark problem used for Learning Classifier Systems that have a continuous-valued representation, and propose a new test problem, the checkerboard problem, that matches many classes of real-world problem more closely than the real multiplexer. Representations and operators are compared, using both the real multiplexer and checkerboard problems and we find that representational, operator and sampling bias all affect the performance of XCS in continuous-valued environments

Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning

PhDThis thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then generalised about. This thesis presents two new implementations of the classic temporal difference (TD) reinforcement learning algorithm Sarsa that utilise fuzzy logic principles for approximation, FQ Sarsa and Fuzzy Sarsa. The effectiveness of these two fuzzy reinforcement learning algorithms is investigated in the context of an agent marketplace. It presents a practical investigation into the design of fuzzy membership functions and tile coding schemas. A critical analysis of the fuzzy algorithms to a related technique in function approximation, a coarse coding approach called tile coding is given in the context of three different simulation environments; the mountain-car problem, a predator/prey gridworld and an agent marketplace. A further comparison between Fuzzy Sarsa and tile coding in the context of the nonstationary environments of the agent marketplace and predator/prey gridworld is presented. This thesis shows that the Fuzzy Sarsa algorithm achieves a significant reduction of state space over traditional Sarsa, without loss of the finer detail that the FQ Sarsa algorithm experiences. It also shows that Fuzzy Sarsa and gradient descent Sarsa(λ) with tile coding learn similar levels of distinction against a stationary strategy. Finally, this thesis demonstrates that Fuzzy Sarsa performs better in a competitive multiagent domain than the tile coding solution

Controlled self-organisation using learning classifier systems

The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed

On cognition, adaptation and homeostasis : analysis and synthesis of bio-inspired computational tools applied to robot autonomous navigation

Orientadores: Fernando Jose Von Zuben, Patricia Amancio VargasDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Este trabalho tem como objetivos principais estudar, desenvolver e aplicar duas ferramentas computacionais bio-inspiradas em navegação autônoma de robôs. A primeira delas é representada pelos Sistemas Classificadores com Aprendizado, sendo que utilizou-se uma versão da proposta original, baseada em energia, e uma versão baseada em precisão. Adicionalmente, apresenta-se uma análise do processo de evolução das regras de inferência e da população final obtida. A segunda ferramenta trata de um modelo denominado sistema homeostático artificial evolutivo, composto por duas redes neurais artificiais recorrentes do tipo NSGasNets e um sistema endócrino artificial. O ajuste dos parâmetros do sistema é feito por meio de evolução, reduzindo-se a necessidade de codificação e parametrização a priori. São feitas análises de suas peculiaridades e de sua capacidade de adaptação. A motivação das duas propostas está no emprego conjunto de evolução e aprendizado, etapas consideradas fundamentais para a síntese de sistemas complexos adaptativos e modelagem computacional de processos cognitivos. Os experimentos visando validar as propostas envolvem simulação computacional em ambientes virtuais e implementações em um robô real do tipo Khepera II.Abstract: The objectives of this work are to study, develop and apply two bio-inspired computational tools in robot autonomous navigation. The first tool is represented by Learning Classifier Systems, using the strength-based and the accuracy-based models. Additionally, the rule evolution mechanisms and the final evolved populations are analyzed. The second tool is a model called evolutionary artificial homeostatic system, composed of two NSGasNet recurrent artificial neural networks and an artificial endocrine system. The parameters adjustment is made by means of evolution, reducing the necessity of a priori coding and parametrization. Analysis of the system's peculiarities and its adaptation capability are made. The motivation of both proposals is on the concurrent use of evolution and learning, steps considered fundamental for the synthesis of complex adaptive systems and the computational modeling of cognitive processes. The experiments, which aim to validate both proposals, involve computational simulation in virtual environments and implementations on real Khepera II robots.MestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Human inspired robotic path planning and heterogeneous robotic mapping

One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains

Heuristic-based Genetic Operation in Classifier Systems

This thesis focuses on improving the Accuracy-based Learning Classifier System (XCS), a Machine Learning technique that attempts to build general and accurate rules. Adapted from the induction concept, a new approach named Rule Combining (RC) draws conclusions from the experience. It learns more efficient in terms of speed and space requirement compared to the original XCS that employs Darwinian genetic operation. Furthermore, RC allows an additional capability of performing feature selection