Imparting human-like commonsense to machines is a long-term goal in the artificial intelligence *** achieve this goal,constructing large-scale commonsense knowledge resources is an important *** recent years,due to in...
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Imparting human-like commonsense to machines is a long-term goal in the artificial intelligence *** achieve this goal,constructing large-scale commonsense knowledge resources is an important *** recent years,due to increasing demand,commonsense knowledge has become a rapidly growing research field,resulting in a surge of new acquisition methods and corresponding *** advances have empowered a variety of downstream AI ***,constructing large-scale commonsense knowledge resources remains an ongoing and challenging *** is still difficult to efficiently collect large-scale,high-quality commonsense *** this paper,we systematically review recent advances in commonsense knowledge acquisition methods and resources,providing a comprehensive summary of recent research scope,the characteristics of different resources,and unsolved challenges.
This paper presents comparison of time cost of three proof searching strategies in a creative expert system. Initially, model of the creative expert system and inference algorithm are proposed. The algorithm searches ...
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ISBN:
(数字)9783319544724
ISBN:
(纸本)9783319544724
This paper presents comparison of time cost of three proof searching strategies in a creative expert system. Initially, model of the creative expert system and inference algorithm are proposed. The algorithm searches for a proof up to a given maximal depth, using one of the following strategies: finding all possible proofs, finding the first proof by depth-first and finding the first proof by breadth-first. Calculation time is measured in inference scenarios from a casting domain. Creativity of the expert system is achieved thanks to integration of inference and machine learning. The learning algorithm can be automatically executed during inference process, because its execution is formalized as a complex inference rule. Such a rule can be fired during inference process. During execution, training data is prepared from facts already stored in the knowledge base and new implications are learned from it. These implications can be used in the inference process. Therefore, it is possible to infer decisions in cases not covered by the knowledge base explicitly.
The intention of this work is to show how logic of plausible reasoning (LPR) can be successfully used for solving complex decision problems. In the following sections a decision involve the use of LPR is described, an...
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ISBN:
(纸本)9781509047260
The intention of this work is to show how logic of plausible reasoning (LPR) can be successfully used for solving complex decision problems. In the following sections a decision involve the use of LPR is described, and then its operation was illustrated on the example of an expertize concerning the choice of technology for making metal products. A knowledge base on the examined group of materials is presented and practical functioning of the system for the choice of a metal processing technology is disclosed. Typical scenarios of the system usage arc presented, serving at the same time as a tool to verify its functionality. In particular, it is expected to create a module allowing automatic generation of rules to the knowledge base, and introduce machine learning to achieve optimal parameters of the inference process. The approach proposed in this study can be applied to a broad class of metal products, but in every case it should take into account the specific nature of a particular group of products and technological parameters of the materials used.
This paper presents an idea of a creative expert system. It is based on inference and machine learning integration. Execution of learning algorithm is automatic because it is formalized as applying a complex inference...
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ISBN:
(纸本)9783319444031;9783319444024
This paper presents an idea of a creative expert system. It is based on inference and machine learning integration. Execution of learning algorithm is automatic because it is formalized as applying a complex inference rule. Firing such a rule generates intrinsically new knowledge: rules are learned from training data, which consists of facts stored already in the knowledge base. This new knowledge may be used in the same inference chain to derive a decision. Complex rules may also represent other procedural activities, like searching databases. Such a solution makes the reasoning process more creative and allows to continue reasoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate model and inference algorithm are proposed. The idea is tested on a decision support system in a casting domain.
The conceptual basics of using ontologies in the process of developing information systems were summarized and analyzed in the paper in the article. Author consider systematic and at the same time cognitive approach t...
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ISBN:
(纸本)9783319184739;9783319184722
The conceptual basics of using ontologies in the process of developing information systems were summarized and analyzed in the paper in the article. Author consider systematic and at the same time cognitive approach to development of distributed information systems. Here the ontology is used for managing, presentation and integration of diverse knowledge in a unified semantic model multifunctional system. As well, the article describes the composition of a single ontology as the basis of the semantic structure of the knowledge base model of distributed information system. The author proposes the principles of system-cognitive analysis of object-oriented domain knowledge and ontological synthesis algorithm and formalization description of models of the semantic structure of objects, processes and classes of problems the problem space of knowledge.
The main advantages of the semantic networks as formalism for knowledgerepresentation are well known: simplicity, naturalness, visionless, and clarity. However, they have the following disadvantages: poor representat...
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The main advantages of the semantic networks as formalism for knowledgerepresentation are well known: simplicity, naturalness, visionless, and clarity. However, they have the following disadvantages: poor representation of arbitrary relations, insufficient expressiveness, unclear semantics, difficult implementation of some operations, and difficult control of the inheritance. In the present paper, the formal definition of a new kind of semantic network, called Priority Semantic Network (PSN) is given. On this basis, an algorithm of transformation of PSN into a list of concepts and semantic links is presented. The architecture and user interface of a tool, in which this algorithm is embedded, are also discussed. (c) 2004 Elsevier B.V. All rights reserved.
The topology induced by binary relations is used to generalize the basic rough set concepts. The suggested topological structure opens up the way for applying rich amount of topological facts and methods in the proces...
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The topology induced by binary relations is used to generalize the basic rough set concepts. The suggested topological structure opens up the way for applying rich amount of topological facts and methods in the process of granular computing, in particular, the notion of topological membership functions is introduced that integrates the concept of rough and fuzzy sets. (c) 2005 Elsevier Inc. All rights reserved.
Describes several intelligent software technologies developed for the Soviet computer project START. Gamma-SETL, a very high-level programming system; TIGRIS, a toolbox for user interfaces; ***, a technology for custo...
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Describes several intelligent software technologies developed for the Soviet computer project START. Gamma-SETL, a very high-level programming system; TIGRIS, a toolbox for user interfaces; ***, a technology for customized information systems; LINGUA.F, a factory of natural language interfaces; InterBASE, a shell for constructing NLI to popular commercial database management systems.
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