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13 Papers

Extended Colored Traveling Salesperson for Modeling Multi-Agent Mission Planning Problems

Topics: Scheduling; Routing; Resource Allocation; OR in Transportation; New Applications of OR

Authors: Branko Miloradović , Baran Çürüklü , Mikael Ekström and Alessandro Papadopoulos

Affiliation: Division of Intelligent Future Technologies, Mälardalen University, Högskoleplan 1, 721 23 Västerås and Sweden

Digital Object Identifier: 10.5220/0007309002370244

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

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In recent years, multi-agent systems have been widely used in different missions, ranging from underwater to airborne. A mission typically involves a large number of agents and tasks, making it very hard for the human operator to create a good plan. A search for an optimal plan may take too long, and it is hard to make a time estimate of when the planner will finish. A Genetic algorithm based planner is proposed in order to overcome this issue. The contribution of this paper is threefold. First, an Integer Linear Programming (ILP) formulation of a novel Extensive Colored Traveling Salesperson Problem (ECTSP) is given. Second, a new objective function suitable for multi-agent mission planning problems is proposed. Finally, a reparation algorithm to allow usage of common variation operators for ECTSP has been developed.
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Applications

OR in Transportation

Routing

Methodologies and Technologies

Pattern Recognition

Scheduling

Operational Research

Resource Allocation

Software Engineering

Comparison Between Static and Dynamic Willingness to Interact in Adaptive Autonomous Agents

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

Authors: Mirgita Frasheri , Baran Cürüklü and Mikael Ekström

Affiliation: Mälardalen University, Sweden

Digital Object Identifier: 10.5220/0006648002580267

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

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Adaptive autonomy (AA) is a behavior that allows agents to change their autonomy levels by reasoning on their circumstances. Previous work has modeled AA through the willingness to interact, composed of willingness to ask and give assistance. The aim of this paper is to investigate, through computer simulations, the behavior of agents given the proposed computational model with respect to different initial configurations, and level of dependencies between agents. Dependency refers to the need for help that one agent has. Such need can be fulfilled by deciding to depend on other agents. Results show that, firstly, agents whose willingness to interact changes during run-time perform better compared to those with static willingness parameters, i.e. willingness with fixed values. Secondly, two strategies for updating the willingness are compared, (i) the same fixed value is updated on each interaction, (ii) update is done on the previous calculated value. The maximum number of completed tasks which need assistance is achieved for (i), given specific initial configurations.
Collapse Button Related Ontology Subjects/Areas/Topics (19)
Agent Models and Architectures

Cooperation and Coordination

Methodologies and Technologies

Agents

Distributed and Mobile Software Systems

Multi-Agent Systems

Artificial Intelligence

Enterprise Information Systems

Operational Research

Artificial Intelligence and Decision Support Systems

Information Systems Analysis and Specification

Simulation

Autonomous Systems

Knowledge Engineering and Ontology Development

Software Engineering

Bioinformatics

Knowledge-Based Systems

Symbolic Systems
Biomedical Engineering

Optimized Realization of Software Components with Flexible OpenCL Functionality

Topic: Component-Based Software Engineering

Authors: Gabriel Campeanu , Jan Carlson and Séverine Sentilles

Affiliation: Mälardalen University, Sweden

Digital Object Identifier: 10.5220/0006691500770088

Proceedings of ENASE 2018, ISBN: 978-989-758-300-1

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Today, the newly available embedded boards with GPUs provide a solution to satisfy the ever-increasing requirements of modern embedded systems. Component-based development is a well-known paradigm used to develop embedded systems. However, this paradigm lacks GPU support to address the specifics of these new boards. This leads to components that typically have reduced reusability, poor maintainability and portability. One way to tackle the existing shortcomings is through flexible components, i.e., platform-agnostic components that, at design time, offer the possibility to be executed either on CPU or GPU. The current realization of flexible components, i.e., as regular components with functionality tailored for the selected hardware, introduces additional overheads such as component communication overhead. In order to tackle the introduced overheads, our solution groups connected flexible components under a flexible group that conceptually behaves as a component. We introduce an algorithm to identify the existing groups in a given component-based system and the generation rules that automatically realizes groups as regular components. To evaluate the feasibility of the new concept, the flexible group is implemented using a state-of-the-practice component model (i.e., Rubus) and examined through the vision system of an underwater robot.
Collapse Button Related Ontology Subjects/Areas/Topics (4)
Applications and Software Development

Model-Driven Software Development

Software Engineering
Component-Based Software Engineering

Fault Tolerance Analysis for Dependable Autonomous Agents using Colored Time Petri Nets

Topics: Multi-Agent Systems; Agent Models and Architectures; Task Planning and Execution; Autonomous Systems; Group Decision Making; Robot and Multi-Robot Systems

Authors: Lan Anh Trinh , Baran Cürüklü and Mikael Ekström

Affiliation: Mälardalen University, Sweden

Digital Object Identifier: 10.5220/0006196002280235

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

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Fault tolerance has become more and more important in the development of autonomous systems with the aim to help the system to recover its normal activities even when some failures happen. Yet, one of the concerns is how to analyze the reliability of a fault tolerance mechanism with regards to the collaboration of multiple agents to complete a complicated task. To do so, an approach of fault tolerance analysis with the colored time Petri net framework is proposed in this work, where a task can be represented by a tree of different concurrent and dependent subtasks to assign to agents. Different subtasks and agents are modeled by color tokens in Petri network. The time values are added to evaluate the processing performance of the whole system with respect to its ability to solve a task with fault tolerance ability. The colored time Petri nets are then tested with simulation of centralized and distributed systems. Finally the experiments are performed to show the feasibility of the proposed approach. From the basics of this study, a generalized framework in the future can be developed to address the fault tolerance analysis for a set of agents working with a sophisticated plan to achieve a common target.
Collapse Button Related Ontology Subjects/Areas/Topics (15)
Agent Models and Architectures

Distributed and Mobile Software Systems

Multi-Agent Systems

Agents

Enterprise Information Systems

Robot and Multi-Robot Systems

Artificial Intelligence

Group Decision Making

Software Engineering

Artificial Intelligence and Decision Support Systems

Knowledge Engineering and Ontology Development

Symbolic Systems

Autonomous Systems

Knowledge-Based Systems

Task Planning and Execution

The GRADE Decision Canvas for Classification and Reflection on Architecture Decisions

Topics: Software and Systems Development Methodologies; Architectural Design and Frameworks

Authors: Efi Papatheocharous 1 , Kai Petersen 2 , Jakob Axelsson 1 , Claes Wohlin 2 , Jan Carlson 3 , Federico Ciccozzi 3 , Séverine Sentilles 3 and Antonio Cicchetti 3

Affiliations: 1 Swedish Institute of Computer Science, Sweden , 2 Blekinge Institute of Technology, Sweden , 3 Mälardalen University, Sweden

Digital Object Identifier: 10.5220/0006301301870194

Proceedings of ENASE 2017, ISBN: 978-989-758-250-9

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This paper introduces a decision canvas for capturing architecture decisions in software and systems engineering. The canvas leverages a dedicated taxonomy, denoted GRADE, meant for establishing the basics of the vocabulary for assessing and choosing architectural assets in the development of software-intensive systems. The canvas serves as a template for practitioners to discuss and document architecture decisions, i.e., capture, understand and communicate decisions among decision-makers and to others. It also serves as a way to reflect on past decision-making activities devoted to both tentative and concluding decisions in the development of software-intensive systems. The canvas has been assessed by means of preliminary internal and external evaluations with four scenarios. The results are promising as the canvas fulfills its intended objectives while satisfying most of the needs of the subjects participating in the evaluation.
Collapse Button Related Ontology Subjects/Areas/Topics (3)
Service-Oriented Software Engineering and Management

Software and Systems Development Methodologies

Software Engineering

Towards Collaborative Adaptive Autonomous Agents

Topics: Multi-Agent Systems; Agent Models and Architectures; Cooperation and Coordination

Authors: Mirgita Frasheri , Baran Cürüklü and Mikael Ekstroem

Affiliation: Mälardalen University, Sweden

Digital Object Identifier: 10.5220/0006195500780087

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

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Adaptive autonomy enables agents operating in an environment to change, or adapt, their autonomy levels by relying on tasks executed by others. Moreover, tasks could be delegated between agents, and as a result decision-making concerning them could also be delegated. In this work, adaptive autonomy is modeled through the willingness of agents to cooperate in order to complete abstract tasks, the latter with varying levels of dependencies between them. Furthermore, it is sustained that adaptive autonomy should be considered at an agent’s architectural level. Thus the aim of this paper is two-fold. Firstly, the initial concept of an agent architecture is proposed and discussed from an agent interaction perspective. Secondly, the relations between static values of willingness to help, dependencies between tasks and overall usefulness of the agents’ population are analysed. The results show that a unselfish population will complete more tasks than a selfish one for low dependency degrees. However, as the latter increases more tasks are dropped, and consequently the utility of the population degrades. Utility is measured by the number of tasks that the population completes during run-time. Finally, it is shown that agents are able to finish more tasks by dynamically changing their willingness to cooperate.
Collapse Button Related Ontology Subjects/Areas/Topics (12)
Agent Models and Architectures

Cooperation and Coordination

Knowledge-Based Systems

Agents

Distributed and Mobile Software Systems

Multi-Agent Systems

Artificial Intelligence

Enterprise Information Systems

Software Engineering

Artificial Intelligence and Decision Support Systems

Knowledge Engineering and Ontology Development

Symbolic Systems

An External Memory Algorithm for the Minimum Enclosing Ball Problem

Topics: Modeling and Algorithms; Geometric Computing; Fundamental Methods and Algorithms

Authors: Linus Källberg 1 , Evan Shellshear 2 and Thomas Larsson 1

Affiliations: 1 Mälardalen University, Sweden , 2 Fraunhofer Chalmers Centre, Sweden

Digital Object Identifier: 10.5220/0005675600810088

Proceedings of GRAPP 2016/VISIGRAPP, ISBN: 978-989-758-175-5

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In this article we present an external memory algorithm for computing the exact minimum enclosing ball of a massive set of points in any dimension. We test the performance of the algorithm on real-life three-dimensional data sets and demonstrate for the first time the practical efficiency of exact out-of-core algorithms. By use of simple heuristics, we achieve near-optimal I/O in all our test cases.
Collapse Button Related Ontology Subjects/Areas/Topics (5)
Computer Vision, Visualization and Computer Graphics

Geometric Computing

Modeling and Algorithms
Fundamental Methods and Algorithms

Geometry and Modeling

Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System

Topics: Machine Learning; Hybrid Systems

Authors: Miguel Leon 1 , Magnus Evestedt 2 and Ning Xiong 1

Affiliations: 1 Malardalen University, Sweden , 2 Prevas, Sweden

Digital Object Identifier: 10.5220/0005591300480054

Proceedings of ECTA 2015, ISBN: 978-989-758-157-1

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Accurate system modelling is an important prerequisite for optimized process control in modern industrial scenarios. The task of parameter identification for a model can be considered as an optimization problem of searching for a set of continuous parameters to minimize the discrepancy between the model outputs and true output values. Differential Evolution (DE), as a class of population-based and global search algorithms, has strong potential to be employed here to solve this problem. Nevertheless, the performance of DE is rather sensitive to its two running parameters: scaling factor and crossover rate. Improper setting of these two parameters may cause weak performance of DE in real applications. This paper presents a new adaptive algorithm for DE, which does not require good parameter values to be specified by users in advance. Our new algorithm is established by integration of greedy search into the original DE algorithm. Greedy search is conducted repeatedly during the running of DE to reach better parameter assignments in the neighborhood. We have applied our adaptive DE algorithm for process model identification in a Furnace Optimized Control System (FOCS). The experiment results revealed that our adaptive DE algorithm yielded process models that estimated temperatures inside a furnace more precisely than those produced by using the original DE algorithm.
Collapse Button Related Ontology Subjects/Areas/Topics (9)
Artificial Intelligence

Hybrid Systems

Machine Learning

Computational Intelligence

Knowledge Discovery and Information Retrieval

Soft Computing

Evolutionary Computing

Knowledge-Based Systems

Symbolic Systems

UTILIZATION AND PERFORMANCE CONSIDERATIONS IN RESOURCE OPTIMIZED STEREO MATCHING FOR REAL-TIME RECONFIGURABLE HARDWARE

Topics: Stereo Vision and Structure from Motion; Vision for Robotics

Authors: Fredrik Ekstrand , Carl Ahlberg , Mikael Ekström , Lars Asplund and Giacomo Spampinato

Affiliation: Mälardalen University, Sweden

Digital Object Identifier: 10.5220/0003867404150418

Proceedings of VISAPP 2012/VISIGRAPP, ISBN: 978-989-8565-04-4

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This paper presents a quantitative evaluation of a set of approaches for increasing the accuracy of an area-based stereo matching method. It is targeting real-time FPGA systems focused on low resource usage and maximized improvement per cost unit to enable concurrent processing. The approaches are applied to a resource optimized correspondence implementation and the individual and cumulative costs and improvements are assessed. A combination of the implemented approaches perform close to other area-matching implementations, but at substantially lower resource usage. Additionally, the limitation in image size associated with standard methods is removed. As fully piped complete on-chip solutions, all improvements are highly suitable for real-time stereo-vision systems.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Applications

Pattern Recognition

Software Engineering

Computer Vision, Visualization and Computer Graphics

Robotics

Stereo Vision and Structure from Motion
Motion, Tracking and Stereo Vision

PROJECT MONITORING AND CONTROL IN MODEL-DRIVEN AND COMPONENT-BASED DEVELOPMENT OF EMBEDDED SYSTEMS - The CARMA Principle and Preliminary Results

Topics: Software Process Improvement; Model-Driven Engineering; Component-Based Software Engineering; Software Development Methodologies; Software Quality Management

Authors: Rikard Land , Jan Carlson , Stig Larsson and Ivica Crnkovic

Affiliation: Mälardalen University, Sweden

Digital Object Identifier: 10.5220/0002997002530258

Proceedings of ENASE 2010, ISBN: 978-989-8425-21-8

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This position paper describes how the combination of the Model-Driven Development (MDD) and Component-Based Software Engineering (CBSE) paradigms can support project monitoring and control, and project risk reduction. The core principle for this is articulated and named CARMA, and our research agenda and preliminary results are described. Through interviews, industry input, process simulation, tool implementation and pilot projects, and describing an extension of CMMI, we are exploring the CARMA principle in order to provide guidelines for MDD/CBSE projects.
Collapse Button Related Ontology Subjects/Areas/Topics (11)
Applications and Software Development

Model-Driven Software Development

Software Engineering Methods and Techniques

Component-Based Software Engineering

Service-Oriented Software Engineering and Management

Software Process Improvement

Cross-Feeding between Data and Software Engineering

Software and Systems Development Methodologies

Software Quality Management
Model-Driven Engineering

Software Engineering

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