Given that a negotiation outcome is determined to a large extent by the successive offers exchanged by negotiating agents, it is useful to analyze dynamic patterns of the bidding, what Raiffa calls the "negotiation dance". Patterns in such exchanges may provide additional insight into the strategies used by the agents. The current practice of evaluating a negotiation strategy, however, is to primarily focus on fairness and quality aspects of the agreement. There is a lack of tools and methods that facilitate a precise analysis of the negotiation dynamics. To fill this gap, this paper introduces a method for analysis based on a classification of negotiation steps.The method provides the basic tools to perform a detailed and quantified analysis of a negotiation between two agents in terms of dynamic properties of the negotiation trace. The method can be applied to well-designed tournaments, but can also be used to analyze single 1-on-1 negotiation.Example findings of applying the method to analyze the ABMP and Trade-Off strategies show that sensitivity to the preferences of the opponent is independent, respectively dependent, on a correct model of that opponent. Furthermore, the results illustrate that having domain knowledge is not always enough to avoid making unintentional steps.
Operational instructions have a major role in improving water delivery performance in irrigation canals. Of different delivery systems, on-request systems have higher flexibility than rotational ones and can be applied to present irrigation networks with minor changes. The main challenge in on-request systems is determining appropriate operational instructions. The main objective of this paper is formulating a fuzzy Sarsa learning (FSL) system for learning operational instructions. As a result of its properties, including being model free and generalizable, FSL can learn in a limited number of situations and suggest operational instructions for a wide range of conditions. The FSL steps in an irrigation system include present depths observation and flows, selection and execution of the operational instructions, observing new depths and flows, and assigning reward to the applied operational instructions and learning accordingly. The physical and hydraulic data of the East Aghili Canal are used for evaluation of our FSL. The results support the suitability of FSL in terms of performance with respect to generalization and convenience of deployment; the total operation time decreased from 23.1 h in conventional operation to 5.76 h in FSL, the efficiency and adequacy indicators obtained were close to desired values, and water level deviations were less than 10%.
Because there is time and financial resources limit in ergonomic control activities, a fuzzy prioritization approach such as Fuzzy TOPSIS ART can be used to take advantage of the available resources and control risks to as low as reasonably practicable (ALARP) level.
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