GRDT: Enhancing model-based learning for its application in robot navigation

One central point ofmachine learning in general and inductive logic programming in special is the search space of the algorithms, de ned by the control structure of the algorithms and additional knowledge. Since the sensible search space di ers from domain to domain, a exible way to describe this space is desired. To demonstrate problems occuring while using existing algorithms, we introduce learning tasks in a real world domain: concept learning for navigation of autonomous mobile robots. We point out di erences between three existing algorithms used within this framework and their results. Since all of these algorithms have problems in solving the tasks, we developed grdt (grammar based rule discovery tool), an algorithm combining their ideas and techniques. In grdt atwo level description language is used for describing the hypothesis space. A grammar is used to de ne a set of second order rule schemata and these schemata then de ne the hypothesis space itself.

On-line Inference of Off-line Learned Operational Concepts

Learning in robotics has received more and more attention in recent years. It eases bridging the gap between low-level sensor data and high-level concepts. A high-level representation language is necessary in order to support the communication between robot and user in both directions when the robot navigates in unknown environments. Controlling robots in terms of a high-level representation formalism like first-order logic is often said to be too slow. In this paper, we present a performance system capable of inferring previously learned high-level concepts from the sensory input of a mobile robot on-line in real-time. Furthermore, we describe an inference engine tailor-made to the requirements of our representation

On-line Inference of Off-line Learned Operational Concepts

. Learning in robotics has received more and more attention in recent years. It eases bridging the gap between low-level sensor data and high-level concepts. A high-level representation language is necessary in order to support the communication between robot and user in both directions when the robot navigates in unknown environments. Controlling robots in terms of a high-level representation formalism like first-order logic is often said to be too slow. In this paper, we present a performance system capable of inferring previously learned high-level concepts from the sensory input of a mobile robot on-line in real-time. Furthermore, we describe an inference engine tailor-made to the requirements of our representation. 1 Learning in robotics In recent years, machine learning becomes more and more an important topic in robotics. Learning eases adaptation of robots to different tasks, to different environments, and different robot settings. Learning can be applied in very different way..

Representing, Learning, and Executing Operational Concepts

. On the one hand side operational concepts enable the user to get information about what a robot has been done and on the other hand side, they enable the user to control the robot. In this way, they function as elements of a high level command language, easy to understand by human users. Operational concepts should be general enough to be applied in different but similar environments like office rooms even if the environment is totally unknown. They combine sensing and action of a mobile robot situated in the real world without the need of additional environmental knowledge. In this paper, we complete the representation of operational concepts and show, how this work is combined with results presented previously. Furthermore, we sketch the learning of concept descriptions to reach a high adaptivity of the robot and we suggest the application of these concepts for planning and control. Key Words. Operational concepts, mobile robots, combining sensing and action, adaptivity, inductive logic programming

Towards Concept Formation Grounded On Perception And Action Of A Mobile Robot

. The recognition of objects and, hence, their descriptions must be grounded in the environment in terms of sensor data. We argue, why the concepts, used to classify perceived objects and used to perform actions on these objects, should integrate action-oriented perceptual features and perception-oriented action features. We present a grounded symbolic representation for these concepts. Moreover, the concepts should be learned. We show a logic-oriented approach to learning grounded concepts. Key Words. Operational concepts, inductive logic programming, combining sensing and action, symbol grounding 1. INTRODUCTION Up to now, application programming of a robot is a time consuming task. The programming is carried out at a very low level. The goal position of the individual commands must be given by exact real world coordinates. This has three undesired consequences. First, even if the new application is very similar to the one before, the control program must be rewritten completely. Se..

Human-Agent Interaction and Machine Learning

. Human-Agent Interaction as a specific area of Human-Computer Interaction is of primary importance for the development of systems that should cooperate with humans. The ability to learn, i.e., to adapt to preferences, abilities and behaviour of a user and to peculiarities of the task at hand, should provide for both a wider range of application and a higher degree of acceptance of agent technology. In this paper, we discuss the role of Machine Learning as a basic technology for human-agent interaction and motivate the need for interdisciplinary approaches to solve problems related to communication with artificial agents for task specification, teaching, or information retrieval purposes. 1 Introduction The concept of "intelligent agents" has recently found growing interest both in theory-oriented research as well as in applications such as robotics, manufacturing, information retrieval, and human-computer interaction. Especially, the idea of agents as "intelligent systems" that act i..

Grdt: Enhancing Model-Based Learning for Its Application in Robot Navigation

One central point of machine learning in general and inductive logic programming in special is the search space of the algorithms, defined by the control structure of the algorithms and additional knowledge. Since the sensible search space differs from domain to domain, a flexible way to describe this space is desired. To demonstrate problems occuring while using existing algorithms, we introduce learning tasks in a real world domain: concept learning for navigation of autonomous mobile robots. We point out differences between three existing algorithms used within this framework and their results. Since all of these algorithms have problems in solving the tasks, we developed grdt (grammar based rule discovery tool), an algorithm combining their ideas and techniques. In grdt a two level description language is used for describing the hypothesis space. A grammar is used to define a set of second order rule schemata and these schemata then define the hypothesis space itself. 1 Introductio..