Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems

A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts) can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model) when it is compared to the second part (the fuzzy facts) for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs). The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler).Fuzzy Unification Tree, Dynamic Discrimination of Fuzzy Sets, DKMS, FRCOM

Approximate reasoning using terminological models

Term Subsumption Systems (TSS) form a knowledge-representation scheme in AI that can express the defining characteristics of concepts through a formal language that has a well-defined semantics and incorporates a reasoning mechanism that can deduce whether one concept subsumes another. However, TSS's have very limited ability to deal with the issue of uncertainty in knowledge bases. The objective of this research is to address issues in combining approximate reasoning with term subsumption systems. To do this, we have extended an existing AI architecture (CLASP) that is built on the top of a term subsumption system (LOOM). First, the assertional component of LOOM has been extended for asserting and representing uncertain propositions. Second, we have extended the pattern matcher of CLASP for plausible rule-based inferences. Third, an approximate reasoning model has been added to facilitate various kinds of approximate reasoning. And finally, the issue of inconsistency in truth values due to inheritance is addressed using justification of those values. This architecture enhances the reasoning capabilities of expert systems by providing support for reasoning under uncertainty using knowledge captured in TSS. Also, as definitional knowledge is explicit and separate from heuristic knowledge for plausible inferences, the maintainability of expert systems could be improved

Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations

Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the "knowledge acquisition" problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. We use a simple QA example from a children's story to motivate Braid's design and explain how the various components work together to produce a coherent logical explanation. Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results while providing frame-based explanations.Comment: Accepted at AAAI-202

Semantic Inference on Heterogeneous E-Marketplace Activities

An electronic marketplace (e-marketplace) is a common business information space populated with many entities of different system types. Each of them has its own context of how to process activities. This leads to heterogeneous e-marketplace activities, which are difficult to make interoperable and inferred from one entity to another. This study solves this problem by proposing a concept of separation strategy and implementing it through providing a semantic inference engine with a novel inference algorithm. The solution, called the RuleXPM approach, enables one to semantically infer a next e-marketplace activity across multiple contexts/domains. Experiments show that the cross-context/cross-domain semantic inference is achievable. This paper is an understanding of many aspects related to heterogeneous activity inference

Second CLIPS Conference Proceedings, volume 2

Papers presented at the 2nd C Language Integrated Production System (CLIPS) Conference held at the Lyndon B. Johnson Space Center (JSC) on 23-25 September 1991 are documented in these proceedings. CLIPS is an expert system tool developed by the Software Technology Branch at NASA JSC and is used at over 4000 sites by government, industry, and business. During the three days of the conference, over 40 papers were presented by experts from NASA, Department of Defense, other government agencies, universities, and industry

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

The application of expert systems in parenteral nutrition

Total Parenteral Nutrition (TPN) is a medical technique used to provide a patient\u27s nutritional requirements via intravenous feeding. Critically ill patients must have adequate nutrition but must also have a stable physiology compensated for or treated by drugs. Several factors such as the complex nature of the TPN solution, the cost of the ingredients and the possible interaction of nutrient and drugs has led to the development of small expert system to assist the hospital medical staff in formulating the TPN constituents and assist the pharmacy staff in producing the final solution. This text will describe a small knowledge-based diagnostic system which when combined with conventional programming techniques has led to tangible benefits within a hospital Intensive Care Unit and Pharmacy

A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1

We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control

Utilização de motores de regras em sistemas informáticos

Dissertação de mestrado integrado em Engenharia de ComunicaçõesNos dias que correm os requisitos de flexibilidade e adaptabilidade aos quais os sistemas informáticos estão sujeitos, são cada vez maiores. Neste cenário, os sistemas desenvolvidos recorrendo a programação convencional, muitas das vezes, tem dificuldades em acompanhar as constantes mudanças de requisitos exigidas. Uma forma de dotar os sistemas da flexibilidade de que necessitam, é integrando motores de regras nestes. Neste trabalho foi desenvolvido um simulador de um sistema baseado em um motor de regras. O sistema representado pelo simulador, tem como objectivo possibilitar que um conjunto de nós em uma rede local (rede interior), consiga partilhar as suas conexões a Internet (rede exterior) com os seus pares. Sendo o motor de regras a componente utilizada por cada nó para decidir qual a conexão de um dos seus pares, este pretende para o seu acesso à rede exterior. As decisões as quais o motor de regras de cada nó chega são consequência directa do conjunto de regras que este utiliza. Regras estas que podem ser definidas e alteradas sem alterar o sistema, fornecendo assim a flexibilidade necessária à adaptação do sistema a diferentes requisitos.In our days the flexibility and adaptability requisites to which informatics systems are subjected, are becoming increasingly greater. In this scenario, the systems that are developed using conventional programming, sometimes, have difficulties in keeping up whit the constant change of the requisites. One of the possible ways, to give systems the flexibility they need, it’s by integrating rule engines on them. In this work we developed a simulator of a system that is based on a rule engine. The system that the simulator represents, allows for a group of network nodes in a local network (interior network), to share their connections to the Internet (exterior network), with their peers. In this system the rule engine is the component the each node uses to decide which connection of his peers it wants for his access to exterior network. The decisions to which, the rule engine arrives of each node arrives, are the direct consequence of set of rule that it uses. It is possible to define and alter the set of rules that a rule engine uses, without changing the system itself, this provides the needed flexibility, for the system to be adjusted to different requisites