Intelligent methods for embedded systems

Intelligent System

Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality.

2020

The multi-agent systems approach of knowledge level cooperation between autonomous agents promises significant benefits to the complex systems such as enhanced interoperability, scalability and reconfigurability. Nowadays, large intelligent systems are very complex. They process a huge amount of information and make very heavy calculations. Recently, new technologies in artificial intelligence, such as Agents can be useful to model these systems and allow them to gather the prior knowledge they need. Agents are autonomous, pro-active, communicative, goal-directed, flexible, capable of learning, and mobile. This paper discusses a formal approach of agent-based modeling for intelligent systems, which describes design level precautions, challenges and techniques using autonomous agents, as its fundamental modeling abstraction. We describe different architectures, based on multi-agents systems, proposed by the system architecture team of ENSEM, Hassan II University, in different domains: Governance, Risk and Compliance of Information Systems, distance learning and the remote control.

2016 IEEE International Conference on Electro Information Technology (EIT), 2016

Distributed real-time embedded (DRE) software systems such as are used to manage critical large-scale infrastructure are important systems to target for increased functionality and resiliency. DRE systems that can adapt to changes in the environment and/or changes in available resources are more robust to unexpected changes and extend both the systems' utility and lifespan. Artificial intelligence techniques are used for adaptation of software systems in general. However, they must meet certain requirements to be appropriate for use with DRE systems. Any artificial intelligence (AI) technique used in an adaptive DRE system should produce consistent results across a distributed system, operate in bounded time, make decisions autonomously, and gracefully handle and learn from previously unencountered environments. This paper surveys a variety of AI techniques, providing a brief overview of each method, evaluating how each technique fits the requirements of an adaptive DRE system, ...

Abstract Multiagent systems satisfy to design requirements for open physical complex systems. However, up to now, no method allows to build software/hardware hybrid multiagent systems. This paper introduces the DIAMOND method. We present its spiral life cycle which associates a multiagent oriented analysis iteration and a component based design iteration. The agent architecture is used as a pattern to build agents using components.

2007

Control systems, processing systems, communication systems and interactive systems can be seen as OPCS (open physical complex systems) because they are supported by new wireless technologies, they are more and more distributed and decentralized. They involve numerous software/hardware entities which enable logical/physical interactions between them and their shared environment.

International Journal of Agent-Oriented Software Engineering, 2015

Today, embedded applications become large scale and strongly constrained. They require a decentralised embedded intelligence generating challenges for embedded systems. A multi-agent approach is well suited to model and design decentralised embedded applications. It is naturally able to take up some of these challenges. But some specific points have to be introduced, enforced or improved in multi-agent approaches to reach all features and all requirements. In this article, we present a study of specific activities that can complement multi-agent paradigm in the 'embedded' context. We use our experience with the DIAMOND method to introduce and illustrate these features and activities.

1997

We are witnessing these days a dramatic change in computing and communication in the enterprise, public service networks, and even the home environment. This wave of change is the consequence of two new technology trends: Ø The distribution of application and data. The mainframe model of computing is being displaced by a distributed model of computing. In its first incarnation, this new model is called client/server, where an application is literally divided into two components which are physically separated, working together to solve a problem. In the more general case, the distributed model of computing allows for multiple co-operating pieces of an application that are distributed, possibly world-wide, to work together to solve a problem.

2018

2002

The task at hand is the design and implementation of real-time agents that are situated in a changeful, unpredictable, and time-constrained environment. Based on Neisser's human cognition model, we propose an architecture for real-time agents. This architecture consists of three components, namely perception, cognition, and action, which can be realized as a set of concurrent administrator and worker processes. These processes communicate and synchronize with one another for real-time performance.

Proceedings of the 30th International Conference on Software Engineering and Knowledge Engineering, 2018

Ubiquitous systems consider the use of electronic components for enhancing daily objects with some kind of computational intelligence for aiding users in their tasks pervasively. Ambient Intelligence (AmI) is a branch of ubiquitous computing that provides an environment full of interconnected devices and it can provide data communication, inference mechanism based on context information and collaboration among system's devices. Similarly, the Internet of Things (IoT) provides uniquely identified devices or things in a network for helping users in their activities. Multi-Agent Systems (MAS) are intelligent systems where agents are responsible for reasoning, competing and using resources to achieve desirable goals pro-actively and autonomously. Agents have been employed in some approaches and works during the last years, but none of them considered embedded MAS responsible for smart devices in an AmI system running over an IoT network. Besides, it is also interesting that agents of the embedded MAS can interact, sharing information with agents situated in another embedded MAS using the IoT network to learn from user's experiences. This paper proposes an architecture for the development of AmI systems using embedded MAS for interfacing with sensors and actuators in a heterogenous network using an IoT middleware.