Total views : 299

Method of Consequences Inference from New Facts in Case of an Incomplete Knowledge Base


  • Vyatka State University, 36, Moskovskaya St., Kirov - 610000, Russian Federation


Objectives: Incomplete information contained in a knowledge base is one of the serious problems hampering the achievement of goals in the field of applied artificial intelligence. This paper provides a content-related statement and a formal description of the problem of consequences inference in view of an incomplete knowledge base. Methods/Statistical Analysis: The author proposed the original method that enables not only to detect consequences from new facts, but also determine additional facts that are needed for consequences inference. The special features of implementation of all stages of the methods are illustrated by an example. To confirm the correctness of the proposed method, a program of consequence inference in the propositional calculus has been developed. Findings: The major advantage of the suggested method is the parallel execution of disjuncts division operations in logical inference procedures. This will significantly (several times) improve the performance of inference engine implemented on the basis of this method. Application/Improvements: The method of consequences inference from new facts in case of an incomplete knowledge base can be applied to reduce the time period required for solving problems in the following areas: logical forecasting of situations development; fault detection; acquisition (accumulation and assimilation) of knowledge, etc.


Disjuncts Division, Incomplete Knowledge Based, Logical Consequence, Propositional Calculus.

Full Text:

 |  (PDF views: 187)


  • Russell SJ, Norvig P. Artificial intelligence: a modern approach. 3rd ed. Upper Saddle River, New Jersey: Prentice Hall. 1994; 946 pp.
  • Brachman RJ, Levesque HJ. Knowledge representation and reasoning. Amsterdam etc.: Elsevier. 1996; 1:255–87.
  • Lu YZ. Industrial intelligent control: fundamentals and applications. Chichester, UK: John Wiley & Sons. 1996 346 pp.
  • Ulucay V, Sahin M, Olgun N, Oztekin O, Emniyet A. Generalized Fuzzy σ - Algebra and Generalized Fuzzy Measure on Soft Sets. Indian Journal of Science and Technology. 2016; 9(4):1–7.
  • Bundy A. Catalogue of artificial intelligence techniques. 3rd ed. Berlin: Springer-Verlag. 1990; 179 pp.
  • Console L, Theseider DD, Torasso P. On the relationship between abduction and deduction. J Logic Computation. 1991; 1(5):661–90.
  • Moayeri M, Shahvarani A, Behzadi MH, Hosseinzadeh-Lotfi F. Comparison of Fuzzy AHP and Fuzzy TOPSIS Methods for Math Teachers Selection. Indian Journal of Science and Technology. 2015; 8(13):1–10.
  • Suganthi R, Kamalakannan P. Exceptional Patterns with Clustering Items in Multiple Databases. Indian Journal of Science and Technology. 2015; 8(31):1–10.
  • Ovchinnikova E, Gordon AS, Hobbs JR. Abduction for discourse interpretation: a probabilistic framework. In: Proceedings of the Joint Symposium on Semantic Processing; Italy. 2013; 1–132.
  • Vagin VN, Zagoryanskaya AA. Systems of argumentation and abduction inference. J Comput Syst Sci Int. 2004; 43(1):117–29.
  • Vagin VN, Khotimchuk KJ. Abduction inference methods in problems of job planning in complex objects. J Comput Syst Sci Int. 2010; 49(5):773–90.
  • Paul G. AI approaches to abduction. In editors. Abductive Reasoning and Learning. Dordrecht, NL: Springer. 2000; 35–98.
  • Caferra R. Logic for computer science and artificial intelligence. London: Wiley-ISTE. 2011; 1–311.
  • Kakas A, Kowalski R, Toni F. The role of abduction in logic programming. In: Handbook of Logic in AI and Logic Programming, Oxford: Oxford University Press. 1998; 5:235–324.
  • Rotella F, Ferilli S. Probabilistic abductive logic programming: a joint approach of logic and probability. In.: Proceedings of XIII Conference of the Italian Association for Artificial Intelligence Turin, Italy. 2013 Dec 4-6. p. 46–51.
  • Kuzmin EA. Logic of Interval Uncertainty. Modern Applied Science. 2014; 8(5):158–62.
  • Dolzhenkova ML, Strabykin DA. The problem of abductive inference of consequences. Scientific and Technical Volga Region Bulletin. 2015; 1:74–7.
  • Strabykin DA. Parallel computation method for abductive knowledge-based inference. J Comput Syst Sci Int. 2000; 39(5):766–71.
  • Strabykin DA. Logical method for predicting situation development based on abductive inference. J Comput Syst Sci Int. 2013; 52(5):759–63.
  • Yoon HJ, Wang BH, Lim JS. Prediction of Time Series Microarray Data using Neurofuzzy Networks. Indian Journal of Science and Technology. 2015; 8(26):1–5.
  • Armoni A. Healthcare information systems: challenges of the new millennium. Hershey, PA, USA: IGI Global Chapter The use of artificial intelligence techniques and applications in the medical domain. 2000; 129–48.
  • Cai ZX. Intelligent control: principles, techniques and applications Singapore: World Scientific Publishing Co. Pte. Ltd. 1998; 468 pp.
  • Vasant P. Handbook of research on artificial intelligence. Techniques and algorithms. Hershey, PA, USA: IGI Global. 2014; 796 pp.
  • Meltsov VYU. High-performance systems of deductive inference: monograph. Yelm, WA, USA: Science Book Publishing House. 2014; 216 pp.
  • Kutepov VP, Kumachev MM. Parallel logical inference on computer systems. Sci Tech Inf Proc. 2013 Dec; 40(6):403–13.
  • Proceedings of 29th International Parallel and Distributed Processing Symposium Workshops; Hyderabad, India. Los Alamitos, California: IEEE Computer Society; ISBN. Available from: accessed: 29/05/2015.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.