Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Abstract Interpretation Based Formal Methods and Future Challenges

Abstract: In order to contribute to the solution of the software reliability problem, tools have been designed to analyze statically the run-time behavior of programs. Because the correctness problem is undecidable, some form of approximation is needed. The purpose of abstract interpretation is to formalize this idea of approximation. We illustrate informally the application of abstraction to the semantics of programming languages as well as to static program analysis. The main point is that in order to reason or compute about a complex system, some information must be lost, that is the observation of executions must be either partial or at a high level of abstraction. A few challenges for static program analysis by abstract interpretation are finally briefly discussed. The electronic version of this paper includes a comparison with other formal methods: typing, model-checking and deductive methods.

Semantic Representation and Recognition of Continued and Recursive Human Activities

Abstract: This paper describes a methodology for automated recognition of complex human activities. The paper proposes a general framework which reliably recognizes high-level human actions and human-human interactions. Our approach is a description-based approach, which enables a user to encode the structure of a high-level human activity as a formal representation. Recognition of human activities is done by semantically matching constructed representations with actual observations. The methodology uses a context-free grammar (CFG) based representation scheme as a formal syntax for representing composite activities. Our CFG-based representation enables us to define complex human activities based on simpler activities or movements. Our system takes advantage of both statistical recognition techniques from computer vision and knowledge representation concepts from traditional artificial intelligence. In the low-level of the system, image sequences are processed to extract poses and gestures. Based on the recognition of gestures, the high-level of the system hierarchically recognizes composite actions and interactions occurring in a sequence of image frames. The concept of hallucinations and a probabilistic semantic-level recognition algorithm is introduced to cope with imperfect lower-layers. As a result, the system recognizes human activities including `fighting' and `assault', which are high-level activities that previous systems had difficulties. The experimental results show that our system reliably recognizes sequences of complex human activities with a high recognition rate.

Classification in the KL-ONE knowledge representation system

Abstract: KL-ONE lets one define and use a class of descriptive terms called Concepts, where each Concept denotes a set of objects A subsumption relation between Concepts is defined which is related to set inclusion by way of a semantics for Concepts. This subsumption relation defines a partial order on Concepts, and KL-ONE organizes all Concepts into a taxonomy that reflects this partial order. Classification is a process that takes a new Concept and determines other Concepts that either subsume it or that it subsumes, thereby determining the location for the new Concept within a given taxonomy. We discuss these issues and demonstrate some uses of the classification algorithm.

Knowledge Representation and Metaphor

Abstract: 1. The Literal and the Metaphoric.- 2. Views of Metaphor.- 3. Knowledge Representation.- 4. Representation Schemes and Conceptual Graphs.- 5. The Dynamic Type Hierarchy Theory of Metaphor.- 6. Computational Approaches to Metaphor.- 7. The Nature and Structure of Semantic Hierarchies.- 8. Language Games, Open Texture and Family Resemblances.- 9. Programming the Dynamic Type Hierarchy.- Author Index.

Review: Modelling with knowledge: A review of emerging semantic approaches to environmental modelling

Abstract: Models, and to a lesser extent datasets, embody sophisticated statements of environmental knowledge. Yet, the knowledge they incorporate is rarely self-contained enough for them to be understood and used - by humans or machines - without the modeller's mediation. This severely limits the options in reusing environmental models and connecting them to datasets or other models. The notion of ''declarative modelling'' has been suggested as a remedy to help design, communicate, share and integrate models. Yet, not all these objectives have been achieved by declarative modelling in its current implementations. Semantically aware environmental modelling is a way of designing, implementing and deploying environmental datasets and models based on the independent, standardized formalization of the underlying environmental science. It can be seen as the result of merging the rationale of declarative modelling with modern knowledge representation theory, through the mediation of the integrative vision of a Semantic Web. In this paper, we review the present and preview the future of semantic modelling in environmental science: from the mediation approach, where formal knowledge is the key to automatic integration of datasets, models and analytical pipelines, to the knowledge-driven approach, where the knowledge is the key not only to integration, but also to overcoming scale and paradigm differences and to novel potentials for model design and automated knowledge discovery.