A Conceptual Generic Framework to Debugging in the Domain-Specific Modeling Languages for Multi-Agent Systems

Despite the existence of many agent programming environments and platforms, the developers may still encounter difficulties on implementing Multi-agent Systems (MASs) due to the complexity of agent features and agent interactions inside the MAS organizations. Working in a higher abstraction layer and modeling agent components within a model-driven engineering (MDE) process before going into depths of MAS implementation may facilitate MAS development. Perhaps the most popular way of applying MDE for MAS is based on creating Domain-specific Modeling Languages (DSMLs) with including appropriate integrated development environments (IDEs) in which both modeling and code generation for system-to-be-developed can be performed properly. Although IDEs of these MAS DSMLs provide some sort of checks on modeled systems according to the related DSML\u27s syntax and semantics descriptions, currently they do not have a built-in support for debugging these MAS models. That deficiency causes the agent developers not to be sure on the correctness of the prepared MAS model at the design phase. To help filling this gap, we introduce a conceptual generic debugging framework supporting the design of agent components inside the modeling environments of MAS DSMLs. The debugging framework is composed of 4 different metamodels and a simulator. Use of the proposed framework starts with modeling a MAS using a design language and transforming design model instances to a run-time model. According to the framework, the run-time model is simulated on a built-in simulator for debugging. The framework also provides a control mechanism for the simulation in the form of a simulation environment model

A Metamodel for Jason BDI Agents

In this paper, a metamodel, which can be used for modeling Belief-Desire-Intention (BDI) agents working on Jason platform, is introduced. The metamodel provides the modeling of agents with including their belief bases, plans, sets of events, rules and actions respectively. We believe that the work presented herein contributes to the current multi-agent system (MAS) metamodeling efforts by taking into account another BDI agent platform which is not considered in the existing platform-specific MAS modeling approaches. A graphical concrete syntax and a modeling tool based on the proposed metamodel are also developed in this study. MAS models can be checked according to the constraints originated from the Jason metamodel definitions and hence conformance of the instance models is supplied by utilizing the tool. Use of the syntax and the modeling tool are demonstrated with the design of a cleaning robot which is a well-known example of Jason BDI architecture

Engineering Multi-Agent Systems: State of Affairs and the Road Ahead

The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area

A cooperative system for metaheuristic algorithms

WOS:000602816000004Optimization problems are defined as the functions whereby the target is to find the optimum state depending on the parameters that have certain limitations. in the field of optimization, the aim is to find from among multiple alternative solutions the optimal solution or approximate solution that provides all the restrictions. Metaheuristic is an extremely effective method to find approximate solutions to optimization problems. However, when metaheuristics are used, there occurs an algorithm selection problem. This problem involves decision-making about which algorithm is to be used to solve the existing optimization problem with maximum performance. The objective of this study is to use a cooperative system that combines different metaheuristics to successfully deal with algorithm selection problems. An intelligent combination of different metaheuristics is expected to provide more flexible, more efficient and more robust approaches. However, such a combination requires less precision. The combination is generated through a methodology designed with soft computing. in addition to the algorithm selection problem, the adjustment of algorithm parameters has significant importance in obtaining good results. For this reason, the cooperative system proposed in this study offers fine-tuning of parameters based on soft computing techniques

Domain-specific modelling language for belief-desire-intention software agents

WOS: 000441398800008Development of software agents according to belief-desire-intention (BDI) model usually becomes challenging due to autonomy, distributedness, and openness of multi-agent systems (MAS). Hence, here, a domain-specific modelling language (DSML), called DSML4BDI, is introduced to support development of BDI agents. The syntax of the language provides the design of agent components required for the construction of the system according to the specifications of BDI architecture. The implementation of designed MAS on Jason BDI platform is also possible via model-to-text transformations built in the DSML. The comparative evaluation results showed that a significant amount of artefacts required for the exact MAS implementation can be automatically achieved by employing DSML4BDI. Moreover, time needed for developing a BDI agent system from scratch can be reduced to one-third in the case of using DSML4BDI. Finally, qualitative assessment, based on the developers' feedback, exposed how DSML4BDI facilitates development of BDI agents.Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [115E591]This work is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant no. 115E591

Enhancing BDI Agents Using Fuzzy Logic for CPS and IoT Interoperability Using the JaCa Platform

Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS’s network and communication capabilities. In the literature, software agents, particularly belief–desire–intention (BDI) agents, are considered options to program these heterogeneous and complex systems in various domains. Moreover, fuzzy logic is a method for handling uncertainties. Therefore, the enhancement of BDI with fuzzy logic can also be employed to improve the abilities, such that autonomy, pro-activity, and reasoning, which are essentials for intelligent systems. These features can be applied in CPSs and IoT interoperable systems. This study extends the CPSs and IoT interoperable systems using fuzzy logic and intelligent agents as symmetric paradigms that equally leverage these domains as well as benefit the agent & artifact approach. In this regard, the main contribution of this study is the integration approach, used to combine the CPS and IoT augmented with fuzzy logic using BDI agents. The study begins with constructing the design primitives from scratch and shows how Jason BDI agents can control the distributed CPS. The study then performs the artifact approach by encapsulating a fuzzy inference system, utilizing time-based reasoning, and benefiting from symmetric fuzzy functions. Lastly, the study applies the self-adaptiveness method and flexibility plan selection, considering the run-time MAPE-K model to tackle run-time uncertainty

Towards applying fuzzy systems in intelligent agent-based CPS : a case study

Cyber-physical Systems (CPS) are complex systems that interact with the physical world where instant external changes exist. To tackle these systems, in the literature, software agents are considered as an option and their enhancement with fuzzy logic can also be employed. Agent-oriented approaches and particularly Belief-Desire-Intention (BDI) architecture is used in different application areas. In all these domains, autonomy and proactive behaviour as well as the human-like reasoning are important aspects to enhance the intelligence of a system. These features can also be applied in CPS where the systems are complex. In this study, it is aimed to enhance a CPS using fuzzy logic for intelligent agents so that intelligent decision making can be applied to eliminate inaccurate calculations in the CPS. To evaluate the proposed fuzzy-BDI approach, we used a CPU fan control system. First, the system is tested assuming that the temperature sensor is ideal without including any sampling noise. We applied traditional BDI and fuzzy-BDI separately. Then, we tested data sampling process considering ∓ 5 error rate. We concluded that fuzzy-BDI approach for both ideal and noisy data showed considerable improvement.</p

Enhancing BDI agents using fuzzy logic for CPS and IoT interoperability using the JaCa platform

Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS’s network and communication capabilities. In the literature, software agents, particularly belief–desire–intention (BDI) agents, are considered options to program these heterogeneous and complex systems in various domains. Moreover, fuzzy logic is a method for handling uncertainties. Therefore, the enhancement of BDI with fuzzy logic can also be employed to improve the abilities, such that autonomy, pro-activity, and reasoning, which are essentials for intelligent systems. These features can be applied in CPSs and IoT interoperable systems. This study extends the CPSs and IoT interoperable systems using fuzzy logic and intelligent agents as symmetric paradigms that equally leverage these domains as well as benefit the&nbsp;agent &amp; artifact&nbsp;approach. In this regard, the main contribution of this study is the integration approach, used to combine the CPS and IoT augmented with fuzzy logic using BDI agents. The study begins with constructing the design primitives from scratch and shows how Jason BDI agents can control the distributed CPS. The study then performs the artifact approach by encapsulating a fuzzy inference system, utilizing time-based reasoning, and benefiting from symmetric fuzzy functions. Lastly, the study applies the self-adaptiveness method and flexibility plan selection, considering the run-time MAPE-K model to tackle run-time uncertainty.</p