Emmanuel M. Tadjouddine

Incremental attribute based particle swarm optimization

An incremental-attribute based particle swarm optimization (IAPSO) which utilizes incremental learning strategy in function optimization is presented in this paper. Traditionally, particle swarm optimization (PSO) searches all the dimensions at the same time. Decomposition strategy is utilized in IAPSO to decompose the whole search space (D-dimension) into D numbers of one-dimensional space. In this approach, incremental learning strategy optimizes the function by searching the D-dimensional space one by one. Experimental results show that IAPSO gets more accurate and stable results than standard PSO in multimodal problems. IAPSO could avoid the “local optima”, i.e., it has better “exploration” ability than standard PSO.

A Proof-Carrying Code Approach to Certificate Auction Mechanisms

Whilst it can be highly desirable for software agents to engage in auctions, they are normally restricted to trading within known auctions, due to the complexity and heterogeneity of the auction rules within an e-commerce system. To allow for agents to deal with previously unseen protocols, we present a proof-carrying code approach using Coq wherein auction protocols can be specified and desirable properties be proven. This enables software agents to automatically certify claimed auction properties and assist them in their decision-making. We have illustrated our approach by specifying both the English and Vickrey auctions; have formalized different bidding strategies for agents; have certified that up to the valuation is the optimal strategy in English auction and truthful bidding is the optimal strategy in Vickrey auction for all agents. The formalization and certification are based on inductive definitions and constructions from within Coq. This work contributes to solving the problem of open societies of software agents moving between different institutions and seeking to make optimal decisions and will benefit those engaged in agent-mediated e-commerce.

A nonmonotone inexact Newton method for unconstrained optimization

It is well known that the Newton method has a second order rate of convergence and that it is widely used to solve optimization problems and nonlinear equations which arise from computational science, engineering analysis and other applications. However, two big disadvantages hinder its application: high computational cost for large scale problems and poor global performance in some complicated and difficult problems. Some inexact Newton methods have emerged over time. Among them, the Newton preconditioned conjugate gradient method is the most efficient and popular approach to overcome the first shortcoming while keeping rapid convergence. In this paper, we have improved the global performance of the inexact Newton method by developing a nonmonotone line search technique. We have also proved the global convergence of the proposed method under some conditions. Numerical experiments on a set of standard test problems are reported. They have shown that the proposed algorithm is promising.

Computational complexity of some intelligent computing systems

Purpose – As agent‐based systems are increasingly used to model real‐life applications such as the internet, electronic markets or disaster management scenarios, it is important to study the computational complexity of such usually combinatorial systems with respect to some desirable properties. The purpose of this paper is to consider two computational models: graphical games encoding the interactions between rational and selfish agents; and weighted directed acyclic graphs (DAG) for evaluating derivatives of numerical functions. The author studies the complexity of a certain number of search problems in both models.Design/methodology/approach – The authors approach is essentially theoretical, studying the problem of verifying game‐theoretic properties for graphical games representing interactions between self‐motivated and rational agents, as well as the problem of searching for an optimal elimination ordering in a weighted DAG for evaluating derivatives of functions represented by computer programs.Fi...

Calibration based on entropy minimization for a class of asset pricing models

Graphical abstractDisplay Omitted HighlightsWe have considered the problem of pricing goods in sequential auctions.Price intervals are formed by an ask-bid pair by the seller and buyer.A binomial tree model is developed to price the items on sale.The minimum entropy principle is used to calibrate the predicted prices.Numerical results have shown a calibration within an error of O(10-4). We consider the problem of calibrating pricing models based on the binomial tree method to market data in a network of auctions where agents are supposed to maximize a given utility function. The calibration is carried out using the minimum entropy principle to find a probability distribution that minimizes a weighted misfit between predicted and observed data. Numerical results from calibrating the mid prices from the bid-ask pairs of the buyer and seller to Taobao data demonstrated the feasibility of this approach in the case of pricing goods in a sequential auction. Further numerical test cases have been presented and have shown promising results. This work can equip those engaged in electronic trading with computational tools to improve their decision-making process in an uncertain environment.

A Dialectical Approach to Enable Decision Making in Online Trading

Software agents, acting on behalf of humans, have been identified as an important solution for future electronic markets. Such agents can make their own decisions given prior preferences and the market environment. These preferences can be described using web ontology languages (OWL), while the market can be represented in a machine-understandable way by utilizing the technique of Semantic Web Services (SWS). Besides, SWS enables agents to automatically discover, select, compose and invoke services. To extend the dependability and interactivity of SWS, we have utilized dialogue games and the proof-carrying code to enable buyers interact with sellers, so that interest properties for an online auction market can be automatically certified. Our decision making framework combines formal proofs with informal evidence collected by web services in a dialogue game between a seller and a buyer. We have implemented our approach and experimental results have demonstrated the feasibility as well as the validity of this framework as an enabler for a buyer agent to enter or not an online auction.

Foundational Certification of Code Transformations Using Automatic Differentiation

Automatic Dierentiation (AD) is concerned with the semantics augmentation of an input program representing a function to form a transformed program that computes the functions derivatives. To ensure the correctness of the AD transformed code (particularly for safety-critical applications), we aim at certifying the algebraic manipulations at the heart of the AD process. We have considered a WHILE-language, and have shown how such proofs can be constructed by using appropriate relational Hoare logic. In particular, we have shown how such inference rules can be constructed for both the forward- and reverse-mode AD by using an abductive logical reasoning.