An Intelligent Sales Assistant for Configurable Products

Molina, Martín

doi:10.1007/3-540-45490-x_78

Cited by 3 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some recent web application offerings are focused on providing advanced search services through virtual stores. In this context, [42] proposes an advanced type of software application that simulates a seller-buyer dialogue to dynamically customize a product to meet specific consumer needs. The proposed approach is based on a shared knowledge model that uses artificial intelligence and knowledge-based methods to simulate the customization process.…”

Section: Configurabilitymentioning

confidence: 99%

Configurable Intelligent Design Based on Hierarchical Imitation Models

et al. 2023

View full text Add to dashboard Cite

The deterministic AI system under review is an alternative to neural-network-based machine learning. In its application fields, which are science, technology, engineering, and business, the implementation of rule-based AI systems leads to benefits such as accuracy and correctness of design, and personalization of the process itself and the results. An algorithmic AI suite is based on design and logical imitation models alone, without creating and/or using Big Data and knowledge bases. Excessive complexity of configuration and high design resource capacity, which are inherent in deterministic systems, are balanced by a special methodology. A hierarchical modeling approach gives a quasi-dynamic network effect, symmetric to the analogous effect in neural networks. System performance is improved by deterministic reference training capable of modifying imitation models in online interaction with users. Such training, which serves as an alternative to neural machine learning, can be implemented by means of experimental partially empirical algorithms and system–user dialogues to build reference model libraries (portfolios). Partially empirical algorithms based on experimental design methods and system user dialogues are used to create reference model libraries (portfolios) that form a deterministic training system, which can be an alternative to neural machine learning. Estimated resources can be saved by using modified optimization techniques and by controlling the computational complexity of the algorithms. Since the proposed system in the considered layout has no analogues, and the relevant research and practical knowledge are extremely limited, special methods are required to implement this project. A gradual, phased implementation process involves the step-by-step formation of sets of algorithms with verification tests at each stage. Each test is performed using an iteration method, and each test includes test, tweak, and modification cycles. Final testing should lead to the development of an AI algorithm package, including related methodological and working papers.

show abstract

Section: Configurabilitymentioning

confidence: 99%

Configurable Intelligent Design Based on Hierarchical Imitation Models

et al. 2023

View full text Add to dashboard Cite

show abstract

“…This section presents a summary of the first and second phases. Information about the third one (symbolic representation) together with an application in the field of photography equipment can be found at [7]. .…”

Section: Modeling Commercial Knowledge Of Sales Assistantsmentioning

confidence: 99%

Modeling Commercial Knowledge to Develop Advanced Agent-Based Marketplaces for E-commerce

Molina

2001

Cooperative Information Agents V

Self Cite

View full text Add to dashboard Cite

This paper argues about the utility of advanced knowledge-based techniques to develop web-based applications that help consumers in finding products within marketplaces in e-commerce. In particular, we describe the idea of model-based approach to develop a shopping agent that dynamically configures a product according to the needs and preferences of customers. Finally, the paper summarizes the advantages provided by this approach.

show abstract

“…The issue of finding the appropriate bundles is, however, not limited to information goods. It also occurs outside of the realm of information goods, where a number of aspects of a complex product can be selected, such as for a PC [6], a trip [20], or photography equipment [13]. Until now, this has not been considered as part of a negotiation process, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

Somefun

Klos

Poutré

2004

Proceedings of the 6th International Conference on Electronic Commerce - ICEC '04

View full text Add to dashboard Cite

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process.The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have nonlinear preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.1 The gains from trade for a bundle are equal to the customer's 'valuation' of the bundle minus the shop's valuation of the bundle, which is his (minimum) price (cf. [12]).2 An offer constitutes a Pareto improvement over another offer whenever it makes one bargainer better off without making the other worse off. A bundle b ′ 'Pareto-dominates' another bundle b whenever switching from b to b ′ results in a Pareto improvement (cf. [12]).

show abstract

An Intelligent Sales Assistant for Configurable Products

Cited by 3 publications

References 4 publications

Configurable Intelligent Design Based on Hierarchical Imitation Models

Configurable Intelligent Design Based on Hierarchical Imitation Models

Modeling Commercial Knowledge to Develop Advanced Agent-Based Marketplaces for E-commerce

Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

Contact Info

Product

Resources

About