Uncertainty management in expert systems

In robot control, mathematical equations describing dynamic behaviors of robots are usually complicated. Additionally, the components such as inertial and friction parameters appearing in these equations are very difficult to determine exactly. With robots having complex structure such as parallel robots, MRM robots etc., the derivation of dynamic equations becomes more difficult and sometimes cannot obtain analytically. In those cases, controlling robot based on its equations of motion is quite hard. Applying fuzzy logic for robot control can overcome the mentioned drawbacks. This is because fuzzy control algorithm gives favorable condition to deal with the lack of adequation as well as inaccuracy of components in robot's dynamic equations. Furthermore, the fuzzy rules are created by clauses which based on human logic, so it is easily to understand and implement. This paper discusses the application of fuzzy logic for controlling MRM robots. To compare the results obtained from fuzzy control, this paper are also adressed the use of the computed torque algorithm to control MRM robots.

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“…[14]. Logical mapping is considered as membership function of fuzzy set F. Classic set X is considered as base set of fuzzy set F.…”

Section: Fuzzy Controlmentioning

confidence: 99%

“…Composite lawsystem: A composite law system is described by n clause: [14] Defuzzification: Common defuzzification methods are maxima method and barycentric method.…”

Section: Linguistic Variable: Apart From Describing Variable By Physimentioning

confidence: 99%

Application of Fuzzy Logic for Controlling Mechanism of Relative Manipulation Robot (Mrm Robot)

Khôi

Toan

2016

JST

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“…Inference is the act of drawing a conclusion by deductive reasoning from given facts. The algorithmic conclusion drawn is also called an inference [Ng and Abramson, 1990]. The laws of valid inference are studied in the field of logic.…”

Section: The Inference Enginementioning

confidence: 99%

Automated Geo-Spatial System for Generalized Assessment of Socio-Economic Vulnerability due to Landslide in a Region

Bhattacharya

Ghosh

Boccardo

et al. 2013

European Journal of Remote Sensing

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The paper explains a system to assess automatically vulnerability due to landslide on socioeconomy of a region by categorizing landslide hazard using spatial as well as temporal causative factors. The expert system has input, understanding, expert & output modules & uses digital spatial data of causative factors of landslide. Input accepts thematic images of contributing factors for landslides, Understanding module interprets to extract relevant information as required by expert module consisting of a Knowledge Base & Inference strategy categorizing region into different susceptibilities of landslide. Overlaid on socioeconomic parameters in output module for vulnerability maps of landslide on population, forestry, urban, rural, agriculture separately to ascertain the impact of landslide on socioeconomy of the Tehri-Garhwal region lower Himalayas, India.

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“…However probabilistic interpretation given to CF is incoherent with probability theory [11]. On the other hand, the conditional independence between evidence and rules actions in LR is rarely satisfied in real applications and LR-based expert systems have poor performance [16]. -Fuzzy logic (FL) [21] to capture the uncertainty and imprecision by associating variable values to fuzzy sets, but in essence, FL is not conceived to deal with incomplete data or to express relations between variables in the knowledge base as in OO frameworks.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Business Rules Uncertainty Management with Probabilistic Relational Models

Agli¹,

Bonnard²,

Gonzales³

et al. 2016

Rule Technologies. Research, Tools, and Applications

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Abstract. Object-oriented Business Rules Management Systems (OO-BRMS) are a complex applications platform that provide tools for automating day-to-day business decisions. To allow more sophisticated and realistic decision-making, these tools must enable Business Rules (BRs) to handle uncertainties in the domain. For this purpose, several approaches have been proposed, but most of them rely on heuristic models that unfortunately have shortcomings and limitations. In this paper we present a solution allowing modern OO-BRMS to effectively integrate probabilistic reasoning for uncertainty management. This solution has a coupling approach with Probabilistic Relational Models (PRMs) and facilitates the inter-operability, hence, the separation between business and probabilistic logic. We apply our approach to an existing BRMS and discuss implications of the knowledge base dynamicity on the probabilistic inference.

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Uncertainty management in expert systems

Cited by 125 publications

References 31 publications

Application of Fuzzy Logic for Controlling Mechanism of Relative Manipulation Robot (Mrm Robot)

Application of Fuzzy Logic for Controlling Mechanism of Relative Manipulation Robot (Mrm Robot)

Automated Geo-Spatial System for Generalized Assessment of Socio-Economic Vulnerability due to Landslide in a Region

Business Rules Uncertainty Management with Probabilistic Relational Models

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