How to cite this paper
Amini, M & Fathian, M. (2021). Optimizing bid search in large outcome spaces for automated multi-issue negotiations using meta-heuristic methods.Decision Science Letters , 10(1), 1-20.
Refrences
Agrawal, M. K., & Chari, K. (2009). Learning negotiation support systems in competitive negotiations: A study of negotiation behaviors and system impacts. International Journal of Intelligent Information Technologies, 5(1), 1–23.
Amini, M., Fathian, M., & Ghazanfari, M. (2020). A boa-based adaptive strategy with multi-party perspective for automated multilateral negotiations. Applied Intelligence, 1–31.
Baarslag, T. (2016). Exploring the Strategy Space of Negotiating Agents: A Framework for Bidding, Learning and Accepting in Automated Negotiation. Springer.
Baarslag, T., Aydogan, R., Hindriks, K. V., Fujita, K., Ito, T., & Jonker, C. M. (2015). The automated negotiating agents competition, 2010–2015. AI Magazine, 36(4), 115–118.
Baarslag, T., Hindriks, K., Hendrikx, M., Dirkzwager, A., & Jonker, C. (2014). Decoupling negotiating agents to explore the space of negotiation strategies. In Novel insights in agent-based complex automated negotiation (pp. 61–83).Springer.
Baarslag, T., Hindriks, K., & Jonker, C. (2014). Effective acceptance conditions in real-time automated negotiation. Decision Support Systems, 60, 68–77.
Burke, E. K., Kendall, G., et al. (2014). Search methodologies (second ed.). Springer.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT press.
Davis, L. (1991). Handbook of genetic algorithms.
De Jonge, D., & Sierra, C. (2016). Gangster: an automated negotiator applying genetic algorithms. In Recent advances in agent-based complex automated negotiation (pp. 225–234). Springer.
Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical computer science, 344(2-3), 243–278.
Dowsland, K. A. (2014). Introduction. In Search methodologies (pp. 1–17). Springer.
Dueck, G., & Scheuer, T. (1990). Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. Journal of Computational Physics, 90(1), 161–175.
El-Ashmawi, W. H., Abd Elminaam, D. S., Nabil, A. M., & Eldesouky, E. (2020). A chaotic owl search algorithm based bilateral negotiation model. Ain Shams Engineering Journal.
Faratin, P., Sierra, C., & Jennings, N. R. (1998). Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems, 24(3-4), 159–182.
Fatima, S. S., Wooldridge, M., & Jennings, N. R. (2001). Optimal negotiation strategies for agents with incomplete information. In International workshop on agent theories, architectures, and languages (pp. 377–392).
Fatima, S. S., Wooldridge, M., & Jennings, N. R. (2002). Multi-issue negotiation under time constraints. In Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 1 (pp. 143–150).
Fujita, K., Aydogan, R., Baarslag, T., Ito, T., & Jonker, C. (2016). The fifth automated negotiating agents competition (anac 2014). In Recent advances in agent-based complex automated negotiation (pp. 211–224). Springer.
Gendreau, M., Potvin, J.-Y., et al. (2019). Handbook of metaheuristics (third ed.). Springer.
Glover, F. (1997). Tabu search and adaptive memory programming—advances, applications and challenges. In Interfaces in computer science and operations research (pp. 1–75). Springer.
Hoos, H. H., & Stützle, T. (2004). Stochastic local search: Foundations and applications. Elsevier.
Jain, M., Maurya, S., Rani, A., & Singh, V. (2018). Owl search algorithm: a novel nature inspired heuristic paradigm for global optimization. Journal of Intelligent & Fuzzy Systems, 34(3), 1573–1582.
Jennings, N. R., Faratin, P., Lomuscio, A. R., Parsons, S., Sierra, C., & Wooldridge, M. (2001). Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation, 10(2), 199–215.
Kadono, Y. (2016). Agent yk: An efficient estimation of opponent’s intention with stepped limited concessions. In Recent advances in agent-based complex automated negotiation (pp. 279–283). Springer.
Kennedy, J. (2006). Swarm intelligence. In Handbook of nature-inspired and innovative computing (pp. 187–219). Springer.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of icnn’95-international conference on neural networks (Vol. 4, pp. 1942–1948).
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Knuth, D. E. (1998). The art of computer programming: Volume 3: Sorting and searching. Addison-Wesley Professional.
Marsa-Maestre, I., Klein, M., Jonker, C. M., & Aydogan, R. (2014). From problems to protocols: Towards a negotiation handbook. Decision Support Systems, 60, 39–54.
Mitchell, M. (1998). An introduction to genetic algorithms. MIT press.
Mitra, D., Romeo, F., & Sangiovanni-Vincentelli, A. (1985). Convergence and finite-time behavior of simulated annealing. In 1985 24th IEEE conference on decision and control (pp. 761–767). IEEE.
Mladenovic, N., & Hansen, P. (1997). Variable neighborhood search. Computers & operations research, 24(11), 1097–1100.
Niimi, M., & Ito, T. (2016). AgentM. In Recent advances in agent-based complex automated negotiation (pp. 235–240). Springer.
Osborne, M. J. (2004). An introduction to game theory (Vol. 3, No. 3). New York: Oxford university press.
Raiffa, H. (2007). Negotiation analysis: The science and art of collaborative decision making. Harvard University Press.
Sato, M., & Ito, T. (2016). Whaleagent: Hardheaded strategy and conceder strategy based on the heuristics. In Recent advances in agent-based complex automated negotiation (pp. 273-278). Springer, Cham.
Talbi, E.-G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons.
Yang, X.-S. (2017). Nature-inspired algorithms and applied optimization (Vol. 744). Springer.
Amini, M., Fathian, M., & Ghazanfari, M. (2020). A boa-based adaptive strategy with multi-party perspective for automated multilateral negotiations. Applied Intelligence, 1–31.
Baarslag, T. (2016). Exploring the Strategy Space of Negotiating Agents: A Framework for Bidding, Learning and Accepting in Automated Negotiation. Springer.
Baarslag, T., Aydogan, R., Hindriks, K. V., Fujita, K., Ito, T., & Jonker, C. M. (2015). The automated negotiating agents competition, 2010–2015. AI Magazine, 36(4), 115–118.
Baarslag, T., Hindriks, K., Hendrikx, M., Dirkzwager, A., & Jonker, C. (2014). Decoupling negotiating agents to explore the space of negotiation strategies. In Novel insights in agent-based complex automated negotiation (pp. 61–83).Springer.
Baarslag, T., Hindriks, K., & Jonker, C. (2014). Effective acceptance conditions in real-time automated negotiation. Decision Support Systems, 60, 68–77.
Burke, E. K., Kendall, G., et al. (2014). Search methodologies (second ed.). Springer.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT press.
Davis, L. (1991). Handbook of genetic algorithms.
De Jonge, D., & Sierra, C. (2016). Gangster: an automated negotiator applying genetic algorithms. In Recent advances in agent-based complex automated negotiation (pp. 225–234). Springer.
Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical computer science, 344(2-3), 243–278.
Dowsland, K. A. (2014). Introduction. In Search methodologies (pp. 1–17). Springer.
Dueck, G., & Scheuer, T. (1990). Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. Journal of Computational Physics, 90(1), 161–175.
El-Ashmawi, W. H., Abd Elminaam, D. S., Nabil, A. M., & Eldesouky, E. (2020). A chaotic owl search algorithm based bilateral negotiation model. Ain Shams Engineering Journal.
Faratin, P., Sierra, C., & Jennings, N. R. (1998). Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems, 24(3-4), 159–182.
Fatima, S. S., Wooldridge, M., & Jennings, N. R. (2001). Optimal negotiation strategies for agents with incomplete information. In International workshop on agent theories, architectures, and languages (pp. 377–392).
Fatima, S. S., Wooldridge, M., & Jennings, N. R. (2002). Multi-issue negotiation under time constraints. In Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 1 (pp. 143–150).
Fujita, K., Aydogan, R., Baarslag, T., Ito, T., & Jonker, C. (2016). The fifth automated negotiating agents competition (anac 2014). In Recent advances in agent-based complex automated negotiation (pp. 211–224). Springer.
Gendreau, M., Potvin, J.-Y., et al. (2019). Handbook of metaheuristics (third ed.). Springer.
Glover, F. (1997). Tabu search and adaptive memory programming—advances, applications and challenges. In Interfaces in computer science and operations research (pp. 1–75). Springer.
Hoos, H. H., & Stützle, T. (2004). Stochastic local search: Foundations and applications. Elsevier.
Jain, M., Maurya, S., Rani, A., & Singh, V. (2018). Owl search algorithm: a novel nature inspired heuristic paradigm for global optimization. Journal of Intelligent & Fuzzy Systems, 34(3), 1573–1582.
Jennings, N. R., Faratin, P., Lomuscio, A. R., Parsons, S., Sierra, C., & Wooldridge, M. (2001). Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation, 10(2), 199–215.
Kadono, Y. (2016). Agent yk: An efficient estimation of opponent’s intention with stepped limited concessions. In Recent advances in agent-based complex automated negotiation (pp. 279–283). Springer.
Kennedy, J. (2006). Swarm intelligence. In Handbook of nature-inspired and innovative computing (pp. 187–219). Springer.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of icnn’95-international conference on neural networks (Vol. 4, pp. 1942–1948).
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Knuth, D. E. (1998). The art of computer programming: Volume 3: Sorting and searching. Addison-Wesley Professional.
Marsa-Maestre, I., Klein, M., Jonker, C. M., & Aydogan, R. (2014). From problems to protocols: Towards a negotiation handbook. Decision Support Systems, 60, 39–54.
Mitchell, M. (1998). An introduction to genetic algorithms. MIT press.
Mitra, D., Romeo, F., & Sangiovanni-Vincentelli, A. (1985). Convergence and finite-time behavior of simulated annealing. In 1985 24th IEEE conference on decision and control (pp. 761–767). IEEE.
Mladenovic, N., & Hansen, P. (1997). Variable neighborhood search. Computers & operations research, 24(11), 1097–1100.
Niimi, M., & Ito, T. (2016). AgentM. In Recent advances in agent-based complex automated negotiation (pp. 235–240). Springer.
Osborne, M. J. (2004). An introduction to game theory (Vol. 3, No. 3). New York: Oxford university press.
Raiffa, H. (2007). Negotiation analysis: The science and art of collaborative decision making. Harvard University Press.
Sato, M., & Ito, T. (2016). Whaleagent: Hardheaded strategy and conceder strategy based on the heuristics. In Recent advances in agent-based complex automated negotiation (pp. 273-278). Springer, Cham.
Talbi, E.-G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons.
Yang, X.-S. (2017). Nature-inspired algorithms and applied optimization (Vol. 744). Springer.