Building a Belief–Desire–Intention Agent for Modeling Neural Networks

REFERENCES

• Alechina, N., M. Dastani, B. Logana, and J. J. Meyer. 2011. Reasoning about plan revision in BDI agent programs. Theoretical Computer Science 412:6115–34. doi:10.1016/j.tcs.2011.05.052.
   Web of Science ®Google Scholar
• Bosse, T., Z. A. Memon, and J. Treur. 2011. A recursive BDI agent model for theory of mind and its applications. Applied Artificial Intelligence 25:1–44. doi:10.1080/08839514.2010.529259.
   Web of Science ®Google Scholar
• Casali, A., L. Godo, and C. Sierra. 2011. A graded BDI agent model to represent and reason about preferences. Artificial Intelligence 175:1468–78. doi:10.1016/j.artint.2010.12.006.
   Web of Science ®Google Scholar
• Castanedo, F., J. García, M. A. Patricio, and J. M. Molina. 2011. A multi-agent architecture based on the BDI model for data fusion in visual sensor network’s. Journal of Intelligent & Robotic Systems 62:299–328. doi:10.1007/s10846-010-9448-1.
   Web of Science ®Google Scholar
• Chang, L.-C., P.-A. Chen, and F.-J. Chang. 2012. Reinforced two-step-ahead weight adjustment technique for online training of recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems 23:1269–78. doi:10.1109/TNNLS.2012.2200695.
   PubMed Web of Science ®Google Scholar
• Chen, S., X. Hong, B. L. Luk, and C. J. Harris. 2009. Non-linear system identification using particle swarm optimisation tuned radial basis function models. International Journal of Bio-Inspired Computation 1:246–58. doi:10.1504/IJBIC.2009.024723.
   Google Scholar
• Cohen, K., S. Siegel, J. Seidel, S. Aradag, and T. McLaughlin. 2012. Nonlinear estimation of transient flow field low dimensional states using artificial neural nets. Expert Systems with Applications 39:1264–72. doi:10.1016/j.eswa.2011.07.135.
   Web of Science ®Google Scholar
• Derras, B., P.-Y. Bard, F. Cotton, and A. Bekkouche. 2012. Adapting the neural network approach to PGA prediction: An example based on the KiK-net data. Bulletin of the Seismological Society of America 102:1446–61. doi:10.1785/0120110088.
   Web of Science ®Google Scholar
• Formato, R. A. 2009. Central force optimisation: A new gradient-like metaheuristic for multidimensional search and optimisation. International Journal of Bio-Inspired Computation 1:217–38. doi:10.1504/IJBIC.2009.024721.
   Google Scholar
• Kumar, R., D. Sharma, and A. Kumar. 2009. A new hybrid multi-agent-based particle swarm optimisation technique. International Journal of Bio-Inspired Computation 1:259–69. doi:10.1504/IJBIC.2009.024724.
   Google Scholar
• Li, W., R. Raskin, and M. F. Goodchild. 2012. Semantic similarity measurement based on knowledge mining: An artificial neural net approach. International Journal of Geographical Information Science 26:1415–35. doi:10.1080/13658816.2011.635595.
   Web of Science ®Google Scholar
• Ludermir, T. B., M. C. P. de Souto, and W. R. de Oliveira. 2009. On a hybrid weightless neural system. International Journal of Bio-Inspired Computation 1:93–104. doi:10.1504/IJBIC.2009.022778.
   Google Scholar
• Lurgi, M., and D. Robertson. 2011. Evolution in ecological agent systems. International Journal of Bio-Inspired Computation 3:331–45. doi:10.1504/IJBIC.2011.043622.
   Google Scholar
• Mikic Fonte, A., J. C. Burguillo, and M. L. Nistal. 2012. An intelligent tutoring module controlled by BDI agents for an e-learning platform. Expert Systems with Applications 39:7546–54. doi:10.1016/j.eswa.2012.01.161.
   Web of Science ®Google Scholar
• Mozaffari, A., and A. Fathi. 2012. Identifying the behaviour of laser solid freeform fabrication system using aggregated neural network and the great salmon run optimisation algorithm. International Journal of Bio-Inspired Computation 4:330–43. doi:10.1504/IJBIC.2012.049901.
   Google Scholar
• Mukun, C., and M. Y. Kiang. 2012. BDI agent architecture for multi-strategy selection in automated negotiation. Journal of Universal Computer Science 18:1379–404.
   Web of Science ®Google Scholar
• Nourafza, N., S. Setayeshi, and A. Khadem-Zadeh. 2012. A novel approach to accelerate the convergence speed of a stochastic multi-agent system using recurrent neural nets. Neural Computing and Applications 21:2015–21. doi:10.1007/s00521-011-0624-4.
   Web of Science ®Google Scholar
• Sardina, S., and L. Padgham. 2011. A BDI agent programming language with failure handling, declarative goals, and planning. Autonomous Agents and Multi-Agent Systems 23:18–70. doi:10.1007/s10458-010-9130-9.
   Web of Science ®Google Scholar
• Steunebrink, B. R., M. Dastani, and J. J. Meyer. 2012. A formal model of emotion triggers: An approach for BDI agents. Synthese 185:83–129. doi:10.1007/s11229-011-0004-8.
   Web of Science ®Google Scholar
• Tsai, M.-S., and Y.-T. Pan. 2011. Application of BDI-based intelligent multi-agent systems for distribution system service restoration planning. European Transactions on Electrical Power 21:1783–801. doi:10.1002/etep.v21.5.
   Web of Science ®Google Scholar
• Vasile, M. 2009. A memetic multi-agent collaborative search for space trajectory optimisation. International Journal of Bio-Inspired Computation 1:186–97. doi:10.1504/IJBIC.2009.023814.
   Google Scholar
• Wang, R.-C., L. Chen, and H.-B. Jiang. 2012. Integrated control of semi-active suspension and electric power steering based on multi-agent system. International Journal of Bio-Inspired Computation 4:73–78. doi:10.1504/IJBIC.2012.047175.
   Google Scholar
• Wilges, B., G. P. Mateus, S. M. Nassar, and R. C. Bastos. 2012. Integration of BDI agent with fuzzy knowledge logic in a virtual learning environment. IEEE Latin America Transactions 10:1370–1376.
   Google Scholar
• Wu, L., K. Su, A. Sattar, Q. Chen, J. Su, and W. Wu. 2012. A complete first-order temporal BDI logic for forest multi-agent systems. Knowledge-Based Systems 27:343–51. doi:10.1016/j.knosys.2011.11.006.
   Web of Science ®Google Scholar
• Yadav, N., C. Zhou, S. Sardina, and R. Rönnquist. 2010. A BDI agent system for the cow herding domain. Annals of Mathematics and Artificial Intelligence 59:313–33. Doi:10.1007/s10472-010-9182-1.
   Web of Science ®Google Scholar
• Zhong, Y., L. Wang, C. Wang, and H. Zhang. 2012. Multi-agent simulated annealing algorithm based on differential evolution algorithm. International Journal of Bio-Inspired Computation 4:217–28. Doi:10.1504/IJBIC.2012.048062.
   Google Scholar