Bilateral Multi-issue Negotiation Research Articles

Agent-based bilateral multi-issue negotiation scheme for e-market transactions

Software agents are flexible, autonomous, and dynamic computational entities. For electronic market ( e-market) applications, the wide variety of choices to the consumers has introduced the problem of information searching. Meanwhile, there are also so many issues for the participants that require negotiations to find the best deal. In this paper, we present a bilateral multi-issue negotiation scheme, which deploys risk seeking/averse strategies and cost-oriented/benefit-oriented tactics to explore the negotiation space of possible trade-off for the one that is most likely to be acceptable. In particular, it focuses on the incorporation of the level of vagueness of preferences for those issues (either crisp or fuzzy) and multi-objective decision-making techniques. The goal of this research is to design an effective model and some constraints-based fuzzy rules and relax of these constraints to their prevailing transaction circumstances.

An adaptive genetic algorithm and its application in bilateral multi-issue negotiation

In multi-agent based E-commerce, how to make the negotiation agents gain satisfying result farthest and negotiate efficiently is a key issue. As for this problem, an adaptive genetic algorithm is presented and the algorithm is applied in bilateral multi-issue simultaneous bidding negotiation. In the system, agents can send their information including issue • issue weight • issue reservation price etc to a third party agent, and the third party agent then uses the adaptive genetic algorithm to give the optimal solution. In the experiments, the two methods are used to compare. One is the simple genetic algorithm (SGA); the second is the adaptive genetic algorithm (AGA). The SGA uses 218 runs to gain the satisfying result, while the AGA only uses 152 runs to gain the satisfying result. The experiments' results show that agents with AGA can negotiation more efficiently than SGA in multi-agent based e-commerce.

Computational logic-based agents

Computational logic provides a rigorous framework for designing, specifying, implementing and verifying autonomous agents that interact, communicate with one another and coordinate their activities in multi-agent systems and societies. In particular, computational logic can aid formalizing reasoning with agents’ mental attitudes such as beliefs, desires, intentions and goals, and agents’ social attitudes such as protocols, social commitments, obligations, norms and rules of behaviour. The use of computational logic aims at facilitating the development of agents that reason and act rationally while at the same time being able to verify the behaviour of these agents against their specification. In the last decade, computational logic-based agents have gained popularity and many contributions have been made in this field both from a theoretical and a practical viewpoint. Computational logic has been used to provide powerful techniques and tools to address a number of issues in multi-agent systems, ranging from agent communication, to agent programming languages, to argumentation, to decision making in a single and multi-agent context, to applying model checking for verifying agent systems. The further development and integration of these techniques and tools shows promise for playing a fundamental role in developing powerful and verifiable agent-based systems. This special issue is about using computational logic to deal with some key issues in multi-agent systems. The purpose is to report on recent developments in this area and discuss current challenges, limits, and future research opportunities. The problems discussed range from agent programming languages through a BDI agent-based programming language, to agent communication through a multi-issue bilateral negotiation, to logic programming-based agents realising a distributed abductive inference mechanism, to mental attitudes representation through a theory of intention representation, and automated, nondeterministic planning for extended reachability goals in agents.

Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces

We present a novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings. The approach exploits logic as communication language among agents, and optimization techniques in order to find Pareto-efficient agreements. We introduce $${\mathcal{P}}({\mathcal{N}})$$ , a propositional logic extended with concrete domains, which allows one to model relations among issues (both numerical and non-numerical ones) via logical entailment, differently from well-known approaches that describe issues as uncorrelated. Through $${\mathcal{P}}({\mathcal{N}})$$ it is possible to represent buyer's request, seller's supply and their respective preferences as formulas endowed with a formal semantics, e.g., if I spend more than 30000 � for a sedan then I want more than a two-years warranty and a GPS system included. We mix logic and utility theory in order to express preferences in a qualitative and quantitative way. We illustrate the theoretical framework, the logical language, the one-shot negotiation protocol we adopt, and show we are able to compute Pareto-efficient outcomes, using a mediator to solve an optimization problem. We prove the computational adequacy of our method by studying the complexity of the problem of finding Pareto-efficient solutions in our setting.

Multi-Issue Negotiation with Deadlines

This paper studies bilateral multi-issue negotiation between self-interested autonomous agents. Now, there are a number of different procedures that can be used for this process; the three main ones being the package deal procedure in which all the issues are bundled and discussed together, the simultaneous procedure in which the issues are discussed simultaneously but independently of each other, and the sequential procedure in which the issues are discussed one after another. Since each of them yields a different outcome, a key problem is to decide which one to use in which circumstances. Specifically, we consider this question for a model in which the agents have time constraints (in the form of both deadlines and discount factors) and information uncertainty (in that the agents do not know the opponent's utility function). For this model, we consider issues that are both independent and those that are interdependent and determine equilibria for each case for each procedure. In so doing, we show that the package deal is in fact the optimal procedure for each party. We then go on to show that, although the package deal may be computationally more complex than the other two procedures, it generates Pareto optimal outcomes (unlike the other two), it has similar earliest and latest possible times of agreement to the simultaneous procedure (which is better than the sequential procedure), and that it (like the other two procedures) generates a unique outcome only under certain conditions (which we define).

A Bayesian classifier for learning opponents’ preferences in multi-object automated negotiation

We present a classification method for learning an opponent’s preferences during a bilateral multi-issue negotiation. Similar candidate preference relations over the set of offers are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponent’s true preference relation lies in that class. Evidence used for classification decision-making is obtained by observing the opponent’s sequence of offers, and applying the concession assumption, which states that negotiators usually decrease their offer utilities as time passes in order to find a deal. Simple experiments show that the technique can find the correct class after very few offers and can select a preference relation that is likely to match closely with the opponent’s true preferences.

Propositional-logic approach to one-shot multi issue bilateral negotiation

Semantic-based e-marketplaces have emerged in recent years, to ease the initial phase of transaction in e-marketplaces, where demand/supply descriptions can be complex and expressive.With the - long term - aim of building an e-marketplace fully exploiting rich semantic descriptions in all stages of a transaction, in this paper we propose a semantic-oriented approach to multi issue bilateral negotiation, which exploits a simple propositional logic. We present the theoretical setting, the negotiation protocol, whose outcome is Pareto-efficient, and illustrate the behavior with a simple example.

Mediating a bilateral multi-issue negotiation

In decision-making, people rely on random selection processes, such as flipping a coin, to make decisions that involve so much uncertainty that they cannot be made rationally. In bilateral multi-issue negotiations, given even bargaining power and two-sided uncertainty, negotiators are frequently faced with difficult decisions that cannot be made using a rational strategy or even by flipping a coin. A deadlock, in which both negotiators refuse to act upon incomplete information, while simultaneously refusing to disclose more information, may arise; then, mediation is required. This paper proposes a mediation protocol that is based on the single negotiation text (SNT) device suggested by Roger Fisher. This protocol provides a deal-construction game to be played by both protagonists, not only ensuring a rational strategy but also producing better outcomes than would be achieved by flipping a coin.

A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments

This paper develops a fuzzy constraint based model for bilateral multi-issue negotiation in trading environments. In particular, we are concerned with the principled negotiation approach in which agents seek to strike a fair deal for both parties, but which, nevertheless, maximises their own payoff. Thus, there are elements of both competition and cooperation in the negotiation (hence semi-competitive environments). One of the key intuitions of the approach is that there is often more than one option that can satisfy the interests of both parties. So, if the opponent cannot accept an offer then the proponent should endeavour to find an alternative that is equally acceptable to it, but more acceptable to the opponent. That is, the agent should make a trade-off. Only if such a trade-off is not possible should the agent make a concession. Against this background, our model ensures the agents reach a deal that is fair (Pareto-optimal) for both parties if such a solution exists. Moreover, this is achieved by minimising the amount of private information that is revealed. The model uses prioritised fuzzy constraints to represent trade-offs between the different possible values of the negotiation issues and to indicate how concessions should be made when they are necessary. Also by using constraints to express negotiation proposals, the model can cover the negotiation space more efficiently since each exchange covers a region rather than a single point (which is what most existing models deal with). In addition, by incorporating the notion of a reward into our negotiation model, the agents can sometimes reach agreements that would not otherwise be possible.