نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناسی ارشد مهندسی دانش و علوم تصمیم دانشگاه ازاد اسلامی واحد قزوین
2 کارشناس ارشد مدیریت بازرگانی - بازار یابی شرکت توزیع نیروی برق اهواز
3 استادیار گروه مدیریت صنعتی دانشگاه آزاد اسلامی واحد قزوین
چکیده
تصمیم گیری یکی از مهمترین موضوعات مورد بررسی در تحقیقات نظامی بشمار می رود. یکی از چالش های موجود در
این بحث وجود عدم قطعیت در محیط های جنگی می باشد که تاثیرات مخربی بر کیفیت و دقت تصمیم گیری می گذارد.
در مقاله هروی و همکارانش، چاپ شده در سال 2193 ، بکارگیری ترکیبی از دو موضوع عامل های شناختی و دسته بندی
براساس قوانین وابستگی فازی به عنوان زمینه های موثر و پرکاربرد، توانسته بود تا حدودی این مسئله را کمرنگ کرده و
سعی در کاهش عدم قطعیت داشته باشد. ولی هم چنان در شرایط حساس و بحرانی، نیاز به سرعت عمل بیشتر با حذف
قوانین نامعتبر و ناکارای استخراج شده در اتخاذ تصمیمهای موثرتر قابل انکار نیست.
هدف این مقاله، بهره گیری از ظرفیت های الگوریتم ژنتیک در انتخاب قوانین واقعبینانهتر به عنوان یک روش فراابتکاری
در تکمیل روش قبلی بصورت ترکیبی، برای کاهش هرچه بیشتر عدم قطعیت در تصمیم گیری ها می باشد. نتایج تجربی
بدست آمده در مقایسه با روش پیشین، به روشنی نشان می دهد که این ترکیب علاوهبر مزیتهای روش قبل، بدلیل کاهش
هرچه بیشتر قوانین تولید شده برای اتخاذ تصمیم، قابل فهم تر، دقیقتر و ریسک پذیری عاقلانهتری دارد.
کلیدواژه‌ها
عنوان مقاله [English]
Improved Effective Management of the Uncertainty in Army Decision Making using Cognitive Agents, Classification based on Fuzzy Association Rules and Genetic Rule Selection
نویسندگان [English]
- Mojtaba Heravi 1
- Tabassom Azimi galeh 2
- Hessam Zandhessami 3
چکیده [English]
Decision making (DM) is an important problem in most of the army
operations. One of the challenging issues in this area is uncertainty in wars
with uncertain information which causes many destructive effects on the
results of strategies in battlefields. In the Heravi et al. article’s, published in
the year 2013, utilizing a combination of Cognitive Agent (CA) and
Classification based on Fuzzy Association Rules (CFAR) as the most
effective and widely used methods, was able to relatively reduce this
problem and tried to reduce uncertainty. But still in critical condition, can’t
deny the need to act quickly and remove most invalid and inefficient rules
extracted in the effective decisions.
This paper aims to utilize the capabilities of Genetic Algorithm (GA) in a
more realistic selection rules as a meta-heuristic way to combine
complementary methods to minimize the uncertainty in DM. In comparison
with previous method, experimental results achieved, clearly show that this
combination in addition to the advantages of the previous method, due to the
further reduction of production rules for DM, are more understandable and
accurate and has more rational risk acceptance.
کلیدواژه‌ها [English]
- Decision making
- Uncertainty Management
- Asymmetric Warfare
- Cognitive Agent
- Classification based on Fuzzy Association Rules
- Genetic Rule Selection
architecture for intelligent multi-agent systems, Annual Reviews in
Control, no.29, pp.87–99.
Anandhavalli M., Suraj Kumar Sudhanshu, Ayush Kumar and Ghose
M.K., (2009). Optimized association rule mining using genetic algorithm,
Advances in Information Mining, Volume 1, Issue 2, pp.01-04.
Anderson B. F., Deane D. H., Hammond K. R., and McClelland G.
H., (1981). Concepts in judgment and decision research, New York:
Praeger.
Anderson J.R., (1996). Act: a simple theory of complex cognition,
American Psychologist, vol.51, pp. 355–365.
Ascough II J.C., Maier H.R., Ravalico J.K., and Strudley M.W.,
(2008). Future research challenges for incorporation of uncertainty in
environmental and ecological decision-making, Ecological Modelling.
Bratman M. E., Israel D. J., and Pollack M. E., (1988). Plans and
resource-bounded practical reasoning, Computational Intelligence,
Vol.4, pp. 349-355.
Castro Sidney de, Marietto Maria das Graças Bruno, França Robson
dos Santos, and Botelho Wagner Tanaka, (2012). A Multi-Agent System
for Information Management and Augmented Reality in Asymmetric
Wars, The 7th International Conference for Internet Technology and
Secured Transactions.
Chen Zuoliang, and Chen Guoqing, (2008). Building an Associative
Classifier Based on Fuzzy Association Rules, International Journal of
Computational Intelligence Systems, vol.1, no.3, pp. 262-273.
Cil Ibrahim, and Mala Murat, (2010). A multi-agent architecture for
modelling and simulation of small military unit combat in asymmetric
warfare, Expert Systems with Applications, vol.37, pp.1331-1343.
Cordesman A., (2002). Terrorism, asymmetric warfare, and weapons
of mass destruction: Defending the US homeland, Praeger Publishers.
Evertsz R., Ritter F.E., Busetta P., and Pedrotti M., (2008). Realistic
behaviour variation in a BDI-based cognitive architecture, In:
Proceedings of SimTecT. SIAA Ltd, Melbourne, Australia.
Friedenberg J. and Silverman G., (2006). Cognitive Science: The
Study of Mind, Sage Publications.
Ghosh Soumadip, Biswas Sushanta, Sarkar Debasree and Sarkar
Partha Pratim, (2010). Mining Frequent Itemsets Using Genetic
Algorithm, International Journal of Artificial Intelligence &
Applications (IJAIA), Vol.1, No.4.
Gilbert N., and Troitzsch K.G., (2005). Simulation for the Social
Scientist, 2nd ed. Open University Press, MacGraw Hill Education.
Goldberg D. E., (1989). Genetic Algorithms in Search Optimization
and Machine Learning, Addison-Wesley, Reading, MA.
Heravi M. and Azimi Galeh T., (2016). Impact of using Gray
Relational Analysis as part of Multiple Attribute Decision Making on the
Organization's Knowledge Management, to improve the Evaluation and
Ranking of Individuals based on Emotional Intelligence, International
Journal of Advanced Biotechnology and Research (IJBR), Vol.7, Special
Issue.3, pp. 926–935.
Heravi M. and Setayeshi S., (2014). Intelligent and Fast Recognition
of Heart Disease based on Synergy of Linear Neural Network and
Logistic Regression Method, J. Mazand Univ Med Sci, Vol.24(112), pp.
78–87.
Heravi M. and Setayeshi S., (2015). Speed up and more Quality in
Recognition of Heart Disease based on a Learner Machine (Hybrid-
Linear) and the Effect of Pareto’s Law on the Training Model, The First
Conference on Novel Approaches of Biomedical Engineering in
Cardiovascular Diseases.
Heravi M., Akramizadeh A., Pourakbar M. and Menhaj M.B., (2013).
A Hybrid Method for Effective Management of the Uncertainty in Army
Decision Making using Cognitive Agents and Classification based on
Fuzzy Association Rules, in Proc. of the 13th Iranian Int. Conf. on
Fuzzy Systems (IFSC), IEEE Publication, pp.1-6.
Heravi M., Azimi galeh T. and Menhaj M.B., (2015a). Modeling of
an Associative Classifier based on Fuzzy Association Rules, in Proc. of
the International Conference on Research in Science and Technology,
Kualalampur, Malaysia.
Heravi M., Azimi galeh T. and Zandhessami H., (2015b). Improving
the Efficiency of a Classifier based on Fuzzy Association Rules using
Genetic Rule Selection, in Proc. of the International Conference on
Research in Science and Technology, Kualalampur, Malaysia.
Heravi M., Azimi galeh T. and Zandhessami H., (2016). Management
of Assessment and Prioritization in the Selection of Suppliers of
Electrical Equipment for Electric Power Distribution Companies using
Multi Attribute Value Theory (MAVT) as part of a Multi Criteria
Decision Making (MCDM), 2nd International conference on Research in
Science and Technology (Istanbul – Turkey).
Hindriks K., de Boer F., van der Hoek W., and Meyer J.-J., (1999).
Agent programming in 3APL, Autonomous Agents and Multi-Agent
Systems, vol.2 (4), pp. 357–401.
Holland J. H, (1992). Adaptation in Natural and Artificial Systems,
MIT Press, Cambridge, MA.
Ishibuchi H., Murata T., and Turksen I. B., (1997). Single-objective
and two-objective genetic algorithms for selecting linguistic rules for
pattern classification problems, Fuzzy Sets and Systems, vol. 89, no.2,
pp.135-149.
Ishibuchi H., Nakashima T., and Murata T., (2001). Three-objective
genetics-based machine learning for linguistic rule extraction,
Information Sciences, vol. 136, no. 1-4, pp.109-133.
Klein J., (2003). Breve: a 3d simulation environment for the
simulation of decentralized systems and artificial life, In: Artificial Life
VIII: Proceedings of the Eighth International Conference on Artificial
Life Complex Adaptive Systems. MIT Press, Cambridge, MA, USA, pp.
329–334.
Koza J. R., (1992). Genetic Programming: On the Programming of
Computers by Means of Natural Selection (Complex Adaptive Systems),
The MIT Press.
Laird J.E., (2008). Extending the soar cognitive architecture, In:
Proceeding of the 2008 Conference on Artificial General Intelligence.
IOS Press, Amsterdam, The Netherlands, pp. 224–235.
Langley P., and Choi D., (2006). A unified cognitive architecture for
physical agents, In: AAAI’06: Proceedings of the 21st National
Conference on Artificial Intelligence. AAAI Press, pp. 1469–1474.
Lawniczak Anna T., and Stefano Bruno N. Di, (2012). Computational
intelligence based architecture for cognitive agents, Procedia Computer
Science, vol.1, pp.2227-2235.
Lu J. and Cheng W., (2007). A genetic-algorithm-based routing
optimization scheme for overlay network, In Proceedings of the 3rd
International Conference on Natural Computation, Washington, DC,
USA. IEEE Computer Society Press, pp. 421-425.
Nguyen Loan T.T., Vo Bay, Hong Tzung-Pei and Thanh Hoang Chi,
(2012). Classification based on association rules: A lattice-based
approach, Expert Systems with Applications, Vol.39, pp.11357–11366.
Nguyen Loan T.T., Vo Bay, Hong Tzung-Pei and Thanh Hoang Chi,
(2013). CAR-Miner: An efficient algorithm for mining class-association
rules, Expert Systems with Applications, Vol.40, pp. 2305–2311.
North M., Collier N., and Vos J., (2006). Experiences creating three
implementations of the repast agent modeling toolkit, ACM Transactions
on Modeling and Computer Simulation, vol.16 (1).
Russell S. and Norvig P., (2009). Artificial Intelligence: A Modern
Approach, 3rd ed., Prentice-Hall, pp.40-46.
Sun R., (2007). Cognitive social simulation incorporating cognitive
architectures, IEEE Intelligent Systems, vol.22 (5), pp. 33–39.
Takahashi S., Sallach D., and Rouchier J., (2007). Advancing Social
Simulation: The First World Congress, Springer-Verlag, Berlin.
Wang Xin, and et al., (2012). Mining axiomatic fuzzy set association
rules for classification problems, European Journal of Operational
Research, Vol.218, pp. 202–210.
Weiss Gerhard, (1999). Multi agent Systems: A Modern Approach to
Distributed Modern Approach to Artificial Intelligence, The MIT Press.
Weyns Danny, (2010). Architecture Based Design of Multi-Agent
Systems, Springer Heidelberg Dordrecht London New York, pp.35-39.
Xu J., and Chen H., (2005). Criminal network analysis and
visualization, Communications of the ACM (CACM), vol.48, pp. 100–
107.
Yang A., Curtis Abbas, H. A., and Sarker R, (2008). NCMAA: A
network centric multi-agent architecture for modeling complex adaptive
systems, The Artificial Life and Adaptive Robotics Laboratory Technical
Report Series.
Yuan B. and Gallagher M., (2003). Playing in Continuous Spaces:
Some Analysis and Extension of Population-Based Incremental
Learning, IEEE Evolutionary Computation
)