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Institut de Recherche en Informatique de Toulouse, France

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28 Papers

Process Mining to Discover the Global Process from its Fragments’ Executions

Topic: Business Process Management and Engineering

Authors: Minh Khoi Nguyen , Hanh Nhi Tran and Ileana Ober

Affiliation: Institut de Recherche en Informatique de Toulouse, Toulouse, France

Digital Object Identifier: 10.5220/0011044800003176

Proceedings of ENASE 2022, ISBN: 978-989-758-568-5

Quick Abstract

Process analysis to improve performance and detect anomalies is important for process management. However, such an analysis requires a global process model that is sometimes hard to get, especially for complex processes involving various teams. This is also an obstacle for BAPE (Bottom-up Artifact-centric Process Environment), an artifact-centric process management environment that allows splitting a process into several fragments which are separately modeled and enacted by different actors. Thus, the knowledge about the whole process is distributed over the involved teams and an overview on the complete process is missing. This paper presents the integration of process mining into BAPE in order to construct the overall process model from the execution data of process fragments.

A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems

Topics: Multi-Agent Systems; Autonomous Systems

Authors: Guilhem Marcillaud ¹ , Valérie Camps ¹ , Stéphanie Combettes ¹ , Marie-Pierre Gleizes ¹ and Elsy Kaddoum ²

Affiliations: ¹ Institut de Recherche en Informatique de Toulouse, Université Toulouse III Paul Sabatier, Toulouse, France , ² Institut de Recherche en Informatique de Toulouse, Université Toulouse II Jean Jaures, Toulouse, France

Digital Object Identifier: 10.5220/0010298503530360

Proceedings of ICAART 2021, ISBN: 978-989-758-484-8

Quick Abstract

We propose a self-adaptive module, called LUDA (Learning Usefulness of DAta) to tackle the problem of cross-understanding in heterogeneous multiagent systems. In this work heterogeneity concerns the agents usage of information available under different reference frames. Our goal is to enable an agent to understand other agents information. To do this, we have built the LUDA module analysing redundant information to improve their accuracy. The closest domains addressing this problem are feature selection and data imputation. Our module is based on the relevant characteristics of these two domains, such as selecting a subset of relevant information and estimating the missing data value. Experiments are conducted using a large variety of synthetic datasets and a smart city real dataset to show the feasibility in a real scenario. The results show an accurate transformation of other information, an improvement of the information use and relevant computation time for agents decision making.

Integrating Shared Information into the Sensorial Mapping of Connected and Autonomous Vehicles

Topics: Multi-Agent Systems; Cooperation and Coordination; Autonomous Systems; Simulation

Authors: Filipo Perotto , Stephanie Combettes , Valerie Camps , Elsy Kaddoum , Guilhem Marcillaud , Pierre Glize and Marie-Pierre Gleizes

Affiliation: IRIT, University of Toulouse, France

Digital Object Identifier: 10.5220/0010387604540461

Proceedings of ICAART 2021, ISBN: 978-989-758-484-8

Quick Abstract

A connected and autonomous vehicle (CAV) needs to dynamically maintain a map of its environment. Even if the self-positioning and relative localization of static objects (roads, signs, poles, guard-rails, buildings, etc.) can be done with great precision thanks to the help of hd-maps, the detection of the dynamic objects on the scene (other vehicles, bicycles, pedestrians, animals, casual objects, etc.) must be made by the CAV itself based on the interpretation of its low-level sensors (radars, lidars, cameras, etc.). In addition to the need of representing those moving objects around it, the CAV (seen as an agent immersed in that traffic environment) must identify them and understand their behavior in order to anticipate their expected trajectories. The accuracy and completeness of this real-time map, necessary for safely planning its own maneuvers, can be improved by incorporating the information transmitted by other vehicles or entities within the surrounding neighborhood through V2X communications. The implementation of this cooperative perception can be seen as the last phase of perception fusion, after the in-vehicle signals (coming from its diverse sensors) have already been combined. In this position paper, we approach the problem of creating a coherent map of objects by selecting relevant information sent by the neighbor agents. This task requires correctly identifying the position of other communicant agents, based both on the own sensory perception and on the received information, and then correcting and completing the map of perceived objects with the communicated ones. For doing so, the precision and confidence on each information must be taken into account, as well as the trust and latency associated with each source. The broad objective is to model and simulate a fleet of vehicles with different levels of autonomy and cooperation, based on a multi-agent architecture, in order to study and improve road safety, traffic efficiency, and passenger comfort. In the paper, the problem is stated, a brief survey of the state-of-the-art on related topics is given, and the sketch of a solution is proposed.

Management of Intelligent Vehicles: Comparison and Analysis

Topics: Cooperation and Coordination; Autonomous Systems

Authors: Guilhem Marcillaud ¹ , Valerie Camps ¹ , Stéphanie Combettes ¹ , Marie-Pierre Gleizes ¹ and Elsy Kaddoum ²

Affiliations: ¹ Institut de Recherche en Informatique de Toulouse, Université Paul Sabatier, Toulouse,France , ² Institut de Recherche en Informatique de Toulouse, Université Jean Jaures, Toulouse, France

Digital Object Identifier: 10.5220/0009117802580265

Proceedings of ICAART 2020, ISBN: 978-989-758-395-7

Quick Abstract

The main purpose of Connected and Autonomous Vehicles (CAVs) is to ensure optimal safety while improving user comfort. Many studies address the problem of CAVs to improve specific driving situations (intersection management, traffic flow management, etc.). In this paper, we propose both a comparison of these systems according to a set of criteria and an analysis to assist the development of CAVs fleets. This analysis shows that, among other, the ad-hoc algorithms use similar data (position, speed, etc.) and that the decisions for vehicles are based on cooperative processing for specific situations. The objective of this paper is to provide a guide for the design of CAVs fleet capable of managing all traffic situations.

A Pattern-based Process Management System to Flexibly Execute Collaborative Tasks

Topics: Software Process Improvement; Model-Driven Engineering; Business Process Management and Engineering; Document and Workflow Management; Collaborative Software Engineering

Authors: Mamadou Cisse ¹ , Hanh Tran ² , Samba Diaw ³ , Bernard Coulette ⁴ and Alassane Bah ³

Affiliations: ¹ IRIT Laboratory, Jean Jaures University, Toulouse, France, UMMISCO, Cheikh Anta Diop University, Dakar and Senegal , ² IRIT Laboratory, Paul Sabatier University, Toulouse and France , ³ UMMISCO, Cheikh Anta Diop University, Dakar and Senegal , ⁴ IRIT Laboratory, Jean Jaures University, Toulouse and France

Digital Object Identifier: 10.5220/0007680702730280

Proceedings of ENASE 2019, ISBN: 978-989-758-375-9

Quick Abstract

Managing collaborations during the execution of a process is complex and challenging, especially for managing the collaboration inside a task having multiple instances performed by various actors. Existing process management approaches propose either a rigid control for conducting such collaborative tasks or no control at all. Aiming at a more flexible way to execute and control multi-instance tasks, we investigate a solution based on late-binding mechanism to allow actors choosing dynamically suitable strategies to perform their collaboration. First, we propose a process modeling language which focuses on describing multi-instance tasks and their dynamic instances at execution time. Second, this language is then used to represent patterns capturing reusable collaboration strategies. A prototype of a pattern-based Process Management System has been developed to demonstrate the possible flexible execution of collaborative tasks.

Related Ontology Subjects/Areas/Topics (6)

Cross-Feeding between Data and Software Engineering		Service-Oriented Software Engineering and Management		Software Engineering Methods and Techniques
Model-Driven Engineering		Software Engineering		Software Process Improvement

Multi-agent Systems for Estimating Missing Information in Smart Cities

Topics: Multi-Agent Systems; Ambient Intelligence; Cooperation and Coordination; Autonomous Systems; Mobile Agents; Self Organizing Systems

Authors: Davide Guastella ¹ , Valérie Camps ² and Marie-Pierre Gleizes ²

Affiliations: ¹ Institut de Recherche en Informatique de Toulouse, Université de Toulouse III - Paul Sabatier, France, Università degli Studi di Palermo and Italy , ² Institut de Recherche en Informatique de Toulouse, Université de Toulouse III - Paul Sabatier and France

Digital Object Identifier: 10.5220/0007381902140223

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

Smart cities aim at improving the quality of life of citizens. To do this, numerous ad-hoc sensors need to be deployed in a smart city to monitor the environmental state. Even if nowadays sensors are becoming more and more cheap their installation and maintenance costs increase rapidly with their number. This paper makes an inventory of the dimensions required for designing an intelligent system to support smart city initiatives. Then we propose a multi-agent based solution that uses a limited number of sensors to estimate at runtime missing information in smart cities using a limited number of sensors.

Related Ontology Subjects/Areas/Topics (15)

Agents	Cooperation and Coordination	Mobile Agents
Ambient Intelligence	Distributed and Mobile Software Systems	Multi-Agent Systems
Artificial Intelligence	Enterprise Information Systems	Self Organizing Systems
Artificial Intelligence and Decision Support Systems	Knowledge Engineering and Ontology Development	Software Engineering
Autonomous Systems	Knowledge-Based Systems	Symbolic Systems

Power Optimization by Cooling Photovoltaic Plants as a Dynamic Self-adaptive Regulation Problem

Topics: Multi-Agent Systems; Cooperation and Coordination; Autonomous Systems; Self Organizing Systems

Authors: Valerian Guivarch , Carole Bernon and Marie-Pierre Gleizes

Affiliation: Université de Toulouse, France

Digital Object Identifier: 10.5220/0006654502760281

Proceedings of ICAART 2018, ISBN: 978-989-758-275-2

Quick Abstract

This paper shows an approach to control cooling devices for photovoltaic plants in order to optimize the energy production thanks to a limited reserve of harvested rainwater. This is a complex problem, considering the dynamic environment and the interdependence of the parameters, such as the weather data and the state of the photovoltaic panels. Our claim is to design a system composed of autonomous components cooperating in order to obtain an emergent efficient control.

Related Ontology Subjects/Areas/Topics (13)

Agents	Distributed and Mobile Software Systems	Multi-Agent Systems
Artificial Intelligence	Enterprise Information Systems	Self Organizing Systems
Artificial Intelligence and Decision Support Systems	Knowledge Engineering and Ontology Development	Software Engineering
Autonomous Systems	Knowledge-Based Systems	Symbolic Systems
Cooperation and Coordination

Cooperative Multi-agent Approach for Computational Systems of Systems Architecting

Topics: Multi-Agent Systems; Autonomous Systems; Collective Intelligence; Self Organizing Systems

Authors: Teddy Bouziat , Stéphanie Combettes , Valérie Camps and Jeremy Boes

Affiliation: Institut de Recherche en Informatiques de Toulouse, France

Digital Object Identifier: 10.5220/0006190101740181

Proceedings of ICAART 2017, ISBN: 978-989-758-219-6

Quick Abstract

This paper addresses the modeling and design of Systems of Systems (SoS). It presents and illustrates a new generic model to describe formally such systems. This model is used to propose a SoS architecting approach based on adaptive multi-agent systems. In this approach, each component system composing the SoS uses a local cooperative decision process in order to interact with other systems and to collectively give rise to a relevant overall function at the SoS level. The proposed model as well as the proposed approach are instantiated with a simulated unmanned aerial vehicle scenario and compared with another approach dealing with collaboration between systems in a SoS.

Related Ontology Subjects/Areas/Topics (13)

Agents	Distributed and Mobile Software Systems	Multi-Agent Systems
Artificial Intelligence	Enterprise Information Systems	Self Organizing Systems
Artificial Intelligence and Decision Support Systems	Knowledge Engineering and Ontology Development	Software Engineering
Autonomous Systems	Knowledge-Based Systems	Symbolic Systems
Collective Intelligence

Lifelong Machine Learning with Adaptive Multi-Agent Systems

Topics: Multi-Agent Systems; Machine Learning; Ambient Intelligence; Industrial Applications of AI; Cooperation and Coordination; Autonomous Systems

Authors: Nicolas Verstaevel , Jérémy Boes , Julien Nigon , Dorian d'Amico and Marie-Pierre Gleizes

Affiliation: Université de Toulouse, France

Digital Object Identifier: 10.5220/0006247302750286

Proceedings of ICAART 2017, ISBN: 978-989-758-220-2

Quick Abstract

Sensors and actuators are progressively invading our everyday life as well as industrial processes. They form complex and pervasive systems usually called ”ambient systems” or ”cyber-physical systems”. These systems are supposed to efficiently perform various and dynamic tasks in an ever-changing environment. They need to be able to learn and to self-adapt throughout their life, because designers cannot specify a priori all the interactions and situations they will face. These are strong requirements that push the need for lifelong machine learning, where devices can learn models and behaviours during their whole lifetime and are able to transfer them to perform other tasks. This paper presents a multi-agent approach for lifelong machine learning.

Related Ontology Subjects/Areas/Topics (19)

Agents	Distributed and Mobile Software Systems	Knowledge-Based Systems
Ambient Intelligence	Enterprise Information Systems	Machine Learning
Artificial Intelligence	Evolutionary Computing	Multi-Agent Systems
Artificial Intelligence and Decision Support Systems	Industrial Applications of AI	Soft Computing
Autonomous Systems	Knowledge Discovery and Information Retrieval	Software Engineering
Computational Intelligence	Knowledge Engineering and Ontology Development	Symbolic Systems
Cooperation and Coordination

A Comparative Study of Two Egocentric-based User Profiling Algorithms - Experiment in Delicious

Topics: Web 2.0 and Social Networking Controls; User Profiling and Recommender Systems

Authors: Marie Françoise Canut ¹ , Manel Mezghani ² , Sirinya On-At ¹ , André Péninou ¹ and Florence Sèdes ¹

Affiliations: ¹ University of Toulouse, France , ² University of Toulouse and Sfax University, France

Digital Object Identifier: 10.5220/0005377006320639

Proceedings of ICEIS 2015, ISBN: 978-989-758-097-0

Quick Abstract

With the growing amount of social media contents, the user needs more accurate information that reflects his interests. We focus on deriving user’s profile and especially user’s interests, which are key elements to improve adaptive mechanisms in information systems (e.g. recommendation, customization). In this paper, we are interested in studying two approaches of user’s profile derivation from egocentric networks: individual-based approach and community-based approach. As these approaches have been previously applied in a co-author network and have shown their efficiency, we are interested in comparing them in the context of social annotations or tags. The motivation to use tagging information is that tags are proved relevant by many researches to describe user’s interests. The evaluation in Delicious social databases shows that the individual-based approach performs well when the semantic weight of user’s interests is taken more in consideration and the community-based approach performs better in the opposite case. We also take into consideration the dynamic of social tagging networks. To study the influence of time in the efficiency of the two user’s profile derivation approaches, we have applied a time-awareness method in our comparative study. The evaluation in Delicious demonstrates the importance of taking into account the dynamic of social tagging networks to improve effectiveness of the tag-based user profiling approaches.

Related Ontology Subjects/Areas/Topics (10)

Artificial Intelligence	Society, e-Business and e-Government	User Profiling and Recommender Systems
Enterprise Information Systems	Software Agents and Internet Computing	Web 2.0 and Social Networking Controls
Knowledge Discovery and Information Retrieval	Symbolic Systems	Web Information Systems and Technologies
Knowledge-Based Systems