Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review

Typically, hydrological problems require approaches capable of describing and simulating part of the hydrological system, or the environmental consequences of natural or anthropic actions. Tools such as Multiagent System (MAS) and Rainfall-Runoff Model (RRM) have been used to help researchers to develop and better understand water systems. Thus, this study presents a Systematic Literature Review (SLR) on the joint use of MAS and RRM tools, in the context of hydrological problems. SLR was performed based on a protocol defined from the research question. Initially, 79 papers were found among six bibliographic databases. This total was reduced over four stages of selection, according to exclusion criteria. In the end, three papers were considered satisfactory within the scope of the research, where they were summarized, analyzed, and compared. While the MAS and RRM tools can interact with their results in a coupled model, SLR showed that there are still major challenges to be explored concerning the dynamics between them, as the steps of scales and validation. However, the coupling of MAS and RRM can provide an interesting alternative tool to analyse decision-making about water resources management systems.

Experience Weighted Learning in Multiagent Systems

Agents face challenges to achieve adaptability and stability when interacting with dynamic counterparts in a complex multiagent system (MAS). To strike a balance between these two goals, this paper proposes a learning algorithm for heterogeneous agents with bounded rationality. It integrates reinforcement learning as well as fictitious play to evaluate the historical information and adopt mechanisms in evolutionary game to adapt to uncertainty, which is referred to as experience weighted learning (EWL) in this paper. We have conducted multiagent simulations to test the performance of EWL in various games. The results demonstrate that the average payoff of EWL exceeds that of the baseline in all 4 games. In addition, we find that most of the EWL agents converge to pure strategy and become stable finally. Furthermore, we test the impact of 2 import parameters, respectively. The results show that the performance of EWL is quite stable and there is a potential to improve its performance by parameter optimization.

Study about Law Multi-Issue Automatic Negotiation Method Based on Artificial Intelligence and Multiagent Evolutionary Algorithm

Multiagent System (MAS) is a self-learning intelligent system formed by many single agents. Each agent in the MAS works independently of the other, and they have all the characteristics of an agent system. It can respond to changes and countermeasures based on its own external environmental conditions. When solving a complex problem, multiple agents can form a group to solve the problem together. In this paper, the agent’s evolutionary algorithm is integrated into the actual problem—multi-issue autonegotiation of law. According to this problem, the autonegotiation solution process and corresponding model are designed. In addition, a new type of solution is proposed for multiple legal issues. Compared with traditional solutions, the applicability has great advantages. Among them, the autonegotiation result of all the agent’s total utility can be quickly found. In the changing environment, this article focuses on the multiagent system negotiation problem. According to the distributed information sharing of multiple agents, even if the case reveals incomplete information, the multiagent can be generated while ignoring the incomplete information. Optimal solution is proposed. The experimental results show that the success rate of the system in analyzing multiple legal issues and autonegotiations reached 67.56% under the condition of incomplete information from the outside world.