Brainstorming Optimization with Multi Agent System
Preethi. V1, Akash2, Siddharth Singh3, Himanshu Joshi4
1Preethi. V, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Akash, Computer Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Siddharth Singh, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Himanshu Joshi, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 76-77 | Volume-8 Issue-5, June 2019 | Retrieval Number: D6643048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A multi Agent System may be seen as a collection of collaborative agents. They can communicate and cooperate with other agents, while keeping their identity unique. They usually negotiate with their peer to reach mutually acceptable agreements during cooperative problem solving. Brainstorming optimization (BSO) algorithms was developed by Madison Avenue and published it in his 1953 book named Applied Imagination. Those algorithms can further be optimized not just for humans but with Multi Agent System. These algorithms can be used to decrease the difference between the problem solving skills of humans and AI. The conventional protocols can be replaced for finding the optimum solution according to all the agents defined in MAS. As all type of human to machine conversations are A to B conversations and machine can only reply to the human but lacks the true interaction. So algorithms should be designed for machines to have interaction in machine to machine or we can say AI to AI.
A communication model should be designed between multiple AI or multi agent AI, so that they can communicate in real time. That communication model should be SI (Swarm Intelligent) because that communication should have human traits
Keywords: AI, BSO (Brainstorming Optimization), Multi-agent, SI, Swarm Intelligence
Scope of the Article: Discrete Optimization