Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Coevolutive adaptation of fitness landscape for solving the testing problem

IEEE International Conference on Systems, Man, and Cybernetics. Nashville, TN, 8-11 October 2000A general framework, called Uniform Coevolution, is introduced to overcome the testing problem in evolutionary computation methods. This framework is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with two different problems: the robot navigation problem and the density parity problem in cellular automata. In both test cases using evolutive methods, the examples used in the learning process biased the solutions found. The main characteristics of the Uniform Coevolution method are that it smoothes the fitness landscape and, that it obtains “ideal learner examples”. Results using uniform coevolution show a high value of generality, compared with non co-evolutive approaches

Learning in Strategic Alliances

{Excerpt} Strategic alliances that bring organizations together promise unique opportunities for partners. The reality is often otherwise. Successful strategic alliances manage the partnership, not just the agreement,for collaborative advantage. Above all, they also pay attentionto learning priorities in alliance evolution. The resource-based view of the firm that gained currency in the mid-1980s considered that the competitive advantage of an organization rests on the application of the strategic resources at its disposal. These days, orthodoxy recognizes the merits of the dynamic, knowledge-based capabilities underpinning the positions organizations occupy in a sector or market. Strategic alliances—meaning cooperative agreements between two or more organizations—are a means to enhance strategic resources: self-sufficiency is becoming increasingly difficult in a complex, uncertain, and discontinuous external environment that calls for focus and flexibility in equal measure. Everywhere, organizations are discovering that they cannot “go” it alone and must now often turn to others to survive

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence