A semantically-rich policy-based approach to robot control (original) (raw)
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OWL-POLAR: Semantic Policies for Agent Reasoning
Policies are declarations of constraints on the behaviour of components within distributed systems, and are often used to capture norms within agent-based systems. A few machine-processable representations for policies have been proposed, but they tend to be either limited in the types of policies that can be expressed or limited by the complexity of associated reasoning mechanisms. In this paper, we argue for a language that sufficiently expresses the types of policies essential in practical systems, and which enables both policy-governed decision-making and policy analysis within the bounds of decidability. We then propose an OWL-based representation of policies that meets these criteria using and a reasoning mechanism that uses a novel combination of ontology consistency checking and query answering. In this way, agent-based systems can be developed that operate flexibly and effectively in policy-constrainted environments.
OWL-POLAR: A framework for semantic policy representation and reasoning
2011
In a distributed system, the actions of one component may lead to severe failures in the system as a whole. To govern such systems, constraints are placed on the behaviour of components to avoid such undesirable actions. Policies or norms are declarations of soft constraints regulating what is prohibited, permitted or obliged within a distributed system. These constraints provide systems-level means to mitigate against failures.
An Ontology-Based Representation for Policy-Governed Adjustable Autonomy
2000
Policies are a means to dynamically regulate the behavior of system components without changing code nor requiring the cooperation of the components being governed. By changing policies, a system can be continuously adjusted to accommodate variations in externally imposed constraints and environmental conditions. KAoS policy and domain services rely on an OWL ontology of the computational environment, application context, and
An OWL-based XACML Policy Framework
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A review and comparison of ontology-based approaches to robot autonomy
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Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.
Ontology for autonomous robotics
2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2017
Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology's strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.
2005
Policies are being increasingly used for controlling the behavior of complex multi-agent systems. The use of policies allows administrators to regulate agent behavior without changing source code or requiring the consent or cooperation of the agents being governed. However, policy-based control can sometimes encounter difficulties when applied to agents that act in pervasive environments characterized by frequent and unpredictable changes. In such cases, we cannot always specify policies a priori to handle any operative run time situation, but instead require continuous adjustments to allow agents to behave in a contextually appropriate manner. To address these issues, some policy approaches for governing agents in pervasive environments specify policies in a way that is both context-based and semantically-rich. Two approaches have been used in recent research: an ontology-based approach that relies heavily on the expressive features of Description Logic (DL) languages, and a rule-based approach that encodes policies as Logic Programming (LP) rules. The aim of this paper is to analyze the emerging directions for the specification of semantically-rich context-based policies, highlighting their advantages and drawbacks. Based on our analysis we describe a hybrid approach that exploits the expressive capabilities of both DL and LP approaches.
Kaa: Policy-based explorations of a richer model for adjustable autonomy
2005
Though adjustable autonomy is hardly a new topic in agent systems, there has been a general lack of consensus on terminology and basic concepts. In this paper, we describe the multi-dimensional nature of adjustable autonomy and give examples of how various dimensions might be adjusted in order to enhance performance of human-agent teams. We then introduce Kaa (KAoS adjustable autonomy), which extends our previous work on KAoS policy and domain services to provide a policy-based capability for adjustable autonomy based on this richer notion of adjustable autonomy. The current implementation of Kaa uses a combination of ontologies represented in OWL and influence-diagram-based decision-theoretic algorithms to determine what if any changes should be made in agent autonomy in a given context. We have demonstrated Kaa as part of ONRsponsored research to improve naval de-mining operations through more effective human-robot interaction. A brief comparison among alternate approaches to adjustable autonomy is provided.