Reward and Penalty Functions in Automated Negotiation

Abstract

Automated negotiation is very important for organizing decentralized systems such as e-business, p2p systems, cloud computing, and so on. During the course of a negotiation, reward and penalty can be used to increase the chance of reaching agreements between negotiating agents, but have not been applied into automated negotiation systems well, especially integrating both in a single negotiation system. Thus, in this work we make an effort to reveal how the reward increases the acceptability of an offer and how the penalty decreases the deniability of an offer. More specifically, our study shows that the degree, to which a reward and a penalty influence the outcome, depends on the greedy degree for the reward and the creditable degree on the penalty. Therefore, if we know an offeree's utilities of accepting and denying an offer, the greedy degree for reward and the creditable degree on penalty, we can calculate how much reward and penalty the offerer agent needs to change the offeree's mind (i.e., from denying to accepting). C 2015 Wiley Periodicals, Inc.

Matrix games with missing, interval, and ambiguous lottery payoffs of pure strategy profiles and compound strategy profiles

“…For example, if a payoff actually is equal to money, then the number of possible payoffs are bounded and the difference between any two numbers should be not less than 0.01 unit. Then for an interval value such as [3,10], it contains 700 numbers, i.e., [3,10] = {3, 3.01, 3.02, … , 9.98, 9.99, 10}. Therefore, we just need to confirm the order of magnitude for the payoff function, then the interval-valued payoff is also discrete.…”

“…Generally speaking, decision making involved multiple self‐interested agents is more complicated than that of only one agent. This is because when multiple agents make decisions, they have to consider how other agents choose their strategies . The most fundamental solution concept in multi‐agents decision making (i.e., game theory) is Nash equilibrium, which is widely used for predicting the outcome of a strategic interaction in different research areas .…”

“…other agents choose their strategies. [1][2][3][4] The most fundamental solution concept in multi-agents decision making (i.e., game theory) is Nash equilibrium, 5 which is widely used for predicting the outcome of a strategic interaction in different research areas. 2,[6][7][8] Informally, a Nash equilibrium corresponds to a steady state of a matrix game (i.e., each player has no incentive to deviate the state).…”

“…Taking player 1 as an example, there are four different cases of ambiguous payoffs as follows (to make the question easier, without losing generality, we assume the payoffs of player 2 is precise): (i) Missing: Because they do not have the experience for hiking with wine, player 1 cannot be sure whether this is a good idea or not, so the payoff is missing (denoted as null in Table 1). (ii) Interval valued: The payoff of Chinese food in BBQ depends on the cooking skill, which is imprecise for player 1, so the payoff is in the range between 3 and 7 (denoted as interval [3,7] in Table 1). (iii) Compound strategy with other strategies: They only have had previous experience in enjoying Chinese food and wine together.…”

A Dempster‐Shafer theory and uninorm‐based framework of reasoning and multiattribute decision‐making for surveillance system

“…The concept of uninorm is introduced by Yager and Rybalov, and it is widely applied to many domains such as automated negotiation, fuzzy logic, market basket analysis, sequential decision‐making under uncertainty, fuzzy neural networks, and so on.…”