aPaRT: A Fast Meta-Heuristic Algorithm using Path-Relinking and Tabu Search for Allocating Machines to Operations in FJSP Problem

This paper proposes a multi-start local search algorithm that solves the flexible job-shop scheduling (FJSP) problem to minimize makespan. The proposed algorithm uses a path-relinking method to generate near optimal solutions. A heuristic parameter, $\alpha$, is used to assign machines to operations. Also, a tabu list is applied to avoid getting stuck at local optimums. The proposed algorithm is tested on two sets of benchmark problems (BRdata and Kacem) to make a comparison with the variable neighborhood search. The experimental results show that the proposed algorithm can produce promising solution in a shorter amount of time

Generating Pareto-Optimal Offers in Bilateral Automated Negotiation with One-Side Uncertain Importance Weights

Pareto efficiency is a seminal condition in the bargaining problem which leads autonomous agents to a Nash-equilibrium. This paper investigates the problem of the generating Pareto-optimal offers in bilateral multi-issues negotiation where an agent has incomplete information and the other one has perfect information. To this end, at first, the bilateral negotiation is modeled by split the pie game and alternating-offer protocol. Then, the properties of the Pareto-optimal offers are investigated. Finally, based on properties of the Pareto-optimal offers, an algorithmic solution for generating near-optimal offers with incomplete information is presented. The agent with incomplete information generates near-optimal offers in O(n łog n). The results indicate that, in the early rounds of the negotiation, the agent with incomplete information can generate near-optimal offers, but as time passes the agent can learn its opponents preferences and generate Pareto-optimal offers. The empirical analysis also indicates that the proposed algorithm outperform the smart random trade-offs (SRT) algorithm

Pareto-optimal algorithm in bilateral automated negotiation

In this paper we present a Pareto-optimal algorithm in bilateral automated negotiation where the negotiation is modeled by "split the pie" game and alternating-offer protocol. Pareto-optimality is the seminal condition in the bargaining problem which leads autonomous agents to the Nash-equilibrium. Generating Pareto-optimal offer in multi-issue bargaining is a computationally complex problem, specially, when autonomous agents have incomplete information about deadline, outside options and the opponent's preferences. Unfortunately, yet to date, there is no articulation that clearly describes an algorithm to generate offer in multi-issue negotiation with perfect information. To this end, we present the maximum greedy trade-offs (MGT) algorithm that generate offers at any aspiration-level in O(n) with assuming that the order of greedy choices is given, otherwise the complexity will be O(n bg n). We also provide analytical proof for the correctness of the maximum greedy trade-offs algorithm

A neural network-based model to learn agent's utility function

Learning opponents’ preferences has a great impact on the success of negotiation, specially, when there is partial information about opponents. This incomplete information can be effectively utilized by intelligent agents equipped with adaptive capacities to learn opponents’ preferences during negotiation. This paper present a neural network based model, named ANUE, to estimate negotiators’ utility function. ANUE’s structure is inspired from mathematical interpretation of utility function. We have also presented eight test cases to evaluate ANUE’s performance where test cases cover all possible form of incomplete information concerning utility function. As a future work, we evaluate ANUE with proposed test cases

Automated bilateral negotiation with incomplete information in the e-marketplace.

Automated negotiation is a basic element in multi-agent systems (MAS), which helps autonomous agents to find a mutual agreement by resolving conflicts. Research on automated negotiation can highly affect the quality of e-marketplaces where autonomous agents buy/sell on behalf of their owners. Pareto-efficiency is a seminal property of the negotiation outcome (an outcome is Pareto-optimal if there is no other outcome that makes an agent better off without making the other agent worse off). Unfortunately, reaching to a Pareto-optimal agreement is a complex problem, particularly when agents negotiate over multiple issues (such as price, warranty and delivery) with incomplete information about each other’s preferences. Although an extensive academic research has explored the single issue negotiation, much less research investigated the multi-issues negotiation with incomplete information. So far, using fuzzy similarity with smart trade-offs was a useful approach to generate near Pareto-optimal offers in multi-issues negotiation. In this approach, a pool of random offers helps an agent to find the most similar one with the last received offers. However, this approach has a high time-complexity. The main purpose of this thesis is to generate Pareto-optimal offers in multi issues bilateral negotiation with incomplete information. To study this problem,at first, negotiations should be grounded on a model that governs the interactions and determines relation between agents. Given this background, the following objectives are considered to be carried out in this study: (i) forming a multi-issues bilateral negotiation model by adapting existing single-issue models. (ii) generating Pareto-optimal offers with one-side incomplete information. (iii) generating Pareto-optimal offers with both-sides incomplete information. To fulfill the first objective, each negotiation issue is modeled by a split the pie of size 1 game where the total negotiation is a nonzero- sum game. In addition, the well-known alternating-offers protocol is used to govern the interactions. To generate Pareto-optimal offer with one-side incomplete information, at first,an algorithm is presented to generate multi-issue offers with perfect (complete)information. This algorithm is called maximum greedy trade-offs (MGT) and can generate offers at given aspiration-level (target utility) in O(n). The MGT algorithm is useful to explore the properties of the Pareto-optimal offers. This algorithm comes with some corollaries that form a learning approach in one-side incomplete problem. The advantage of the MGT algorithm is that it does not need the exact opponent’s preferences to generate Pareto-optimal offers, instead, it works with a greedy sequence. An agent with incomplete information can find an estimation of the optimal offer in early rounds of the negotiation, however as time passes, it can likely generate Pareto-optimal offer by learning the greedy greedy sequence. In this case, the agent with incomplete information can learn the greedy sequence in O(n log n). In one-side incomplete information problem, comparison between MGT algorithm and smart random trade-offs (SRT) algorithm indicates that MGT outperforms SRT. Finally, the problem of finding Pareto-optimal offers in both sides with incomplete information is investigated. In this case, agents need to be tailored by a learning capability that explores the opponent’s preferences. To this end, we have developed an incremental learning approach using soft-computing techniques to learn opponent’s preferences in multi-issue negotiation with incomplete information. In this learning approach, firstly, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, the process of searching the best fuzzy preferences that articulates the opponent’s intention is conducted by genetic algorithm. Whenever an agent receives an offer it forms a constraint and updates the fitness of individuals in the given population of preferences based on the degree of the constraint satisfaction. Experimental results show that our learning approach can estimate the opponent’s preferences effectively. Moreover, results indicate that agents equipped by this learning capability can generate Pareto-efficient offers by MGT algorithm. Results, in both-sides incomplete information problem, indicate that MGT out performs SRT. The reason is that, SRT algorithm is sensitive to the accuracy of the learned preferences while MGT algorithm can generate Pareto-optimal offers even with an approximation of the learned preferences

The Learning of an Opponent\u27s Approximate Preferences in Bilateral Automated Negotiation

Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent’s preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent’s preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent\u27s preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent\u27s intention. Experimental results showed that our learning approach can estimate the opponent’s preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility

A Fast Recommender System for Cold User Using Categorized Items

In recent years, recommender systems (RS) provide a considerable progress to users. RSs reduce the cost of a user’s time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active and their preferences are more difficult to detect. The aim of this study is to provide a new way to improve recall and precision in recommender systems for cold users. According to the available categories of items, prioritization of the proposed items is improved and then presented to the cold user. The obtained results show that in addition to increased speed of processing, recall and precision have an acceptable improvement

A review on soft computing techniques in automated negotiation

Automated negotiation offers new capability for buyers and sellers to efficiently trade goods and services in online markets. In high-dimensional real world negotiations, many agents may communicate with each other over multi-issue products. In this paper, we review soft-computing techniques used in e-negotiation. Although implementation of real world negotiations is very hard but using soft computing techniques can lead us to a suitable approximation in automated negotiation. Using a combination of soft computing techniques can decrease the complexity of high-dimensional negotiation