Knowledge representation and reasoning

Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets. Examples of knowledge representation formalisms include semantic nets, systems architecture, frames, rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. History The earliest work in computerized knowledge representati

Flexible subtyping relations for component-oriented formalisms and their verification

David Huerzeler

In this thesis we address the problem of safe substitutability in mobile component-oriented formalisms. We try to give a solution for different definitions of "safe" through a new notion of subtyping based on flexibility and observation. The aim is to have a definition which is applicable to a large numbers of existing formalisms, and which itself may be seen as an abstraction of many existing definitions of subtyping. In a similar way, we also try to see the verification of such relations in a way that may be applicable to many formalisms, and which may be seen as "containing" many existing techniques. Our hope is that our work may link these subtype definitions or verification techniques to formalisms they were not originally designed for. The contribution of this thesis can be divided in several points. First of all, the full formal definition of a "descriptive" component-oriented formalism based on concurrency, non determinism, complex composition and mobility. Also, the definition of a general flexible notion of subtyping, as well as a more particular example. Verification techniques for these subtyping relations are also grouped under the concept of component transformation, which is shown to include many other works. Finally, to illustrate the main concepts of our work, we provide a case study with a model of a "mobile" drinks dispenser.

EPFL2004

Open Robotics Research Using Web-based Knowledge Services

Aude Billard, Zhou Fang, Nadia Barbara Figueroa Fernandez, Ana-Lucia Ureche

In this paper we discuss how the combination of modern technologies in "big data" storage and management, knowledge representation and processing, cloud-based computation, and web technology can help the robotics community to establish and strengthen an open research discipline. We describe how we made the demonstrator of a EU project review openly available to the research community. Specifically, we recorded episodic memories with rich semantic annotations during a pizza preparation experiment in autonomous robot manipulation. Afterwards, we released them as an open knowledge base using the cloud-and web-based robot knowledge service OPENEASE. We discuss several ways on how this open data can be used to validate our experimental reports and to tackle novel challenging research problems.

Ieee2016

Requirements for building an ontology for autonomous robots

Howard Li, Kamilo Andres Melo Becerra

Purpose - IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots. Design/methodology/approach - For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making. Findings - AuR has identified the core concepts and domains needed to create an ontology for autonomous robots. Practical implications - AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance. Originality/value - Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors'knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.

Emerald Group Publishing Ltd2016

Requirements for building an ontology for autonomous robots

Howard Li, Kamilo Andres Melo Becerra

Emerald Group Publishing Ltd2016