A Toolkit for Constraint-Based Inference Engines

Abstract Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them became difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control preferable. In this work a fuzzy logic controller is introduced with the idea of split-horizon; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FTEs). Computer simulations are used to verify the performance of the controller. Three simulation cases are introduced: radial, compound, and damping. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing them in a relatively reasonable time.

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Using Object-Oriented Frame Language for Realizing Discrete Event Simulation

Volume 3: 22nd Design Automation Conference ◽

10.1115/96-detc/dac-1042 ◽

1996 ◽

Author(s):

Ming Dong ◽

Jianzhong Cha ◽

Mingcheng E

Keyword(s):

Knowledge Base ◽

Discrete Event Simulation ◽

Discrete Event ◽

Object Oriented ◽

Simulation Models ◽

Activity Cycle ◽

Event Simulation ◽

Knowledge Based ◽

Inference Engines ◽

Reasoning About Knowledge

Abstract In this paper, we realize knowledge-based discrete event simulation model’s representation, reasoning and implementation by means of object-oriented(OO) frame language. Firstly, a classes library of simulation models is built by using the OO frame language. And then, behaviours of simulation models can be generated by inference engines reasoning about knowledge base. Lastly, activity cycle diagrams can be used to construct simulation network logic models by connecting the components classes of simulation models. This kind of knowledge-based simulation models can effectively solve the modeling problems of complex and ill-structure systems.

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Matching Dynamic Demands of Mobile Users with Dynamic Service Offers

Mobile Computing ◽

10.4018/978-1-60566-054-7.ch250 ◽

2009 ◽

pp. 3404-3420

Author(s):

Bernhard Holtkamp ◽

Norbert Weißenberg ◽

Manfred Wojciechowski

Keyword(s):

Reference Model ◽

Mobile Users ◽

Conceptual Approach ◽

Domain Specific ◽

Inference Engines ◽

Application Ontology ◽

Service Oriented ◽

Semantic Reference ◽

Upper Level

This chapter describes the use of ontologies for personalized situation-aware information and service supply of mobile users in different application domains. A modular application ontology, composed of upper-level ontologies such as location and time ontologies and of domain-specific ontologies, acts as a semantic reference model for a compatible description of user demands and service offers in a service-oriented information- logistical platform. The authors point out that the practical deployment of the platform proved the viability of the conceptual approach and exhibited the need for a more performant implementation of inference engines in mobile multi-user scenarios. Furthermore, the authors hope that understanding the underlying concepts and domain-specific application constraints will help researchers and practitioners building more sophisticated applications not only in the domains tackled in this chapter but also transferring the concepts to other domains.

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Adimen-SUMO

International Journal on Semantic Web and Information Systems ◽

10.4018/jswis.2012100105 ◽

2012 ◽

Vol 8 (4) ◽

pp. 80-116 ◽

Cited By ~ 14

Author(s):

Javier Àlvez ◽

Paqui Lucio ◽

German Rigau

Keyword(s):

Language Processing ◽

Intelligent Systems ◽

Knowledge Engineering ◽

Formal Reasoning ◽

Design Decisions ◽

First Order ◽

Theorem Provers ◽

Inference Engines ◽

Web Infrastructure ◽

Important Design

In this paper, the authors present Adimen-SUMO, an operational ontology to be used by first-order theorem provers in intelligent systems that require sophisticated reasoning capabilities (e.g. Natural Language Processing, Knowledge Engineering, Semantic Web infrastructure, etc.). Adimen-SUMO has been obtained by automatically translating around 88% of the original axioms of SUMO (Suggested Upper Merged Ontology). Their main interest is to present in a practical way the advantages of using first-order theorem provers during the design and development of first-order ontologies. First-order theorem provers are applied as inference engines for reengineering a large and complex ontology in order to allow for formal reasoning. In particular, the authors’ study focuses on providing first-order reasoning support to SUMO. During the process, they detect, explain and repair several important design flaws and problems of the SUMO axiomatization. As a by-product, they also provide general design decisions and good practices for creating operational first-order ontologies of any kind.

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ADDING PROBABILITIES AND RULES TO OWL LITE SUBSETS BASED ON PROBABILISTIC DATALOG

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488506003819 ◽

2006 ◽

Vol 14 (01) ◽

pp. 17-41 ◽

Cited By ~ 19

Author(s):

HENRIK NOTTELMANN ◽

NORBERT FUHR

Keyword(s):

Information Retrieval ◽

Description Logics ◽

Axiomatic Approach ◽

Description Language ◽

Horn Clauses ◽

Inference Engines ◽

Retrieval Systems ◽

Information Retrieval Systems ◽

Horn Rules ◽

Advanced Applications

This paper proposes two probabilistic extensions of variants of the OWL Lite description language, which are essential for advanced applications like information retrieval. The first step follows the axiomatic approach of combining description logics and Horn clauses: Subsets of OWL Lite are mapped in a sound and complete way onto Horn predicate logics (Datalog variants). Compared to earlier approaches, a larger fraction of OWL Lite can be transformed by switching to Datalog with equality in the head; however, some OWL Lite constructs cannot be transformed completely into Datalog. By using probabilistic Datalog, the new probabilistic OWL Lite subsets (both with support for Horn rules) are defined, and the semantics are given by the semantics of the corresponding probabilistic Datalog program. As inference engines for probabilistic Datalog are available, description logics and information retrieval systems can easily be combined.

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