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A Graph Matching Algorithm to extend Wise Systems with Semantic

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Dahhani A. , Alloui I. , Monnet S. , Vernier F.

Annals of Computer Science and Information Systems

2023

tom Vol. 35

411--420

Software technology has exponentially evolved leading to the development of intelligent applications using artificial intelligence models and techniques. Such development impacts all scientific and social fields: home automation, medicine, communication, etc. To make those new applications useful to a larger number of people, researchers are working on how to integrate artificial intelligence into real world while respecting the notion of calm technology. This paper fits in the context of the development of intelligent systems termed ``wise systems'' that aim at satisfying the calm technology requirement. Those systems are based on the concept of ``Wise Object'': a software entity -- object, service, component, application, etc. -- able to learn by itself how it is expected to behave and how it is used by a human or another software entity. During its learning process, a Wise Object constructs a graph that represents its behavior and the way it is used. A major weakness of Wise Objects is that the numerical information that they generate is mostly meaningless to humans. Therefore the objective of the work presented in this paper is to extend Wise Objects with semantics that enable them communicate with humans whose attention will consequently be less involved. In this paper, we address the issue of how to relate two different views using two state-based formalisms: State Transition Graph for views generated by the Wise Objects and Input Output Symbolic Transition System for conceptual views. Our proposal extends previous work done to extend the generated information with the conceptual knowledge using a matching algorithm founded on graph morphism. The first version of the algorithm has several limitations and constraints on the graphs that make it difficult to use in realistic cases. In this paper, we propose to generalize the algorithm and raise those restrictions. To illustrate the complete process, the construction of a sample graph matching on a home-automation system is considered.

A chance-constraint approach for optimizing Social Engagement-based services

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Bierlaire M. , Fadda E. , Fotio Tiotsop L. , Manerba D.

Annals of Computer Science and Information Systems

2022

tom Vol. 30

301--304

Social Engagement is a novel business model whose goal is transforming final users of a service from passive components into active ones. In this framework, people are contacted by the decision-maker (generally a company) and they are asked to perform tasks in exchange for a reward. This paves the way to the interesting optimization problem of allocating the different types of workforce so as to minimize costs. Despite this problem has been investigated within the operations research community, there is no model that allows to solve it by explicitly and appropriately modeling the behavior of contacted candidates through consolidated concepts from utility theory. This work aims at filling this gap. We propose a stochastic optimization model including a chance constraint that puts in relation, under probabilistic terms, the candidate willingness to accept a task and the reward actually offered by the decision-maker. The proposed model aims at optimally deciding which user to contact, the amount of the reward proposed, and how many employees to use in order to minimize the total expected costs of the operations. A solution approach is proposed to address the formulated stochastic optimization problem and its computational efficiency and effectiveness are investigated through an extensive set of computational experiments.