An Introduction to MultiAgent Systems.

(2002). Getting to Yes: Negotiating Agreement without Giving in. Quality Management Journal: Vol. 9, No. 2, pp. 73-74.

Getting to Yes: Negotiating Agreement without Giving in

The Art and Science of Negotiation

The Art and Science of Negotiation.

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank's loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it's important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert's knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.

Hybrid credit ranking intelligent system using expert system and artificial neural networks

In this paper, an adaptive decision making approach of three families of tactics has been proposed for bilateral negotiation: the time dependent tactics, behavior dependent tactics, and time independent tactics These tactics are more adaptive to the environment's changes (reservation interval, time deadline, opponent behavior) The suggested time dependent tactics take advantage from round based time continuity and dynamics aspects (features) integrated in their modelling For suggested behavior dependent tactics, a new formalization based on the percentage of change was introduced, which helps agents to be more prudent in the environments with incomplete information comparing to previous behavior dependent tactics suggested by Faratin et al (Int J Robotics Auton Syst 24(3---4):159---182, 1998) Concerning the new family of tactics which are completely independent from time, the agents compute their offers based on their reservation interval These tactics are useful when there is no time deadline and, in addition, when the behavior of opponent agents doesn't follow any negotiation equilibrium Moreover, new experimental measures are suggested which are more useful for final evaluation The experiments conducted in this paper, prove the applicability of all three families of tactic

An adaptive approach for decision making tactics in automated negotiation

Finding adequate negotiation strategy with incomplete information, even in one to one negotiation, is a complex problem. Inspired from research works aiming to analyze human behavior and those on social negotiation psychology, integration of psychological aspects, with essential time parameter, is becoming necessary. For this purpose, first, one to one bargaining process, in which a buyer agent and a seller agent negotiate over single issue (price), is developed, where social and cognitive behaviors based on time (Faratin et al. 1998) and personality aspects are suggested. Second, experimental environments and measures, allowing a set of experiments, carried out for different negotiation deadlines, are detailed. Third, experimental results are analyzed with regard to time dependent behaviors. Results demonstrate that more increasing conciliatory aspects lead to increased agreement point (price) and decreased agreement time, and more increasing aggressive aspects lead to decreased agreement point and increased agreement time.

Time and personality dependent behaviors for agent negotiation with incomplete information

In recent decades, a number of trainers have used role-playing games to teach negotiation skills. The SISINE Project – funded by the EU Leonardo Program - has developed a teaching methodology making it possible to conduct this kind of approaches in a virtual environment. The teaching methodology exploits a specially-developed technology platform allowing a small community of players to communicate, to interact and to play online in order to acquire basic notions and rules about negotiation and how to apply this knowledge. A part of SISINE project has investigated Artificial Intelligence issued techniques in order to evaluate implementation’s possibility of computer-controlled “artificial players” embodying some intelligent behaviors. This chapter presents the first results of those investigations.

Arash Bahrammirzaee

Papers

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Hybrid credit ranking intelligent system using expert system and artificial neural networks

An adaptive approach for decision making tactics in automated negotiation

Time and personality dependent behaviors for agent negotiation with incomplete information

SISINE: a Negotiation Training Dedicated Multi-Player Role-Playing Platform Using Artificial Intelligence Skills