The objective of this chapter is to revisit and explore how intelligent agents can be used in conjunction with modern Web technologies. We use JADE and BDI4JADE to expose cognitive agents using a BDI architecture as web services that can be integrated with different modern cloud-based services, such as Amazon AWS services and Google Home.
This paper proposes an architecture for sharing IoT Objects’ resources in the Internet of Things providing a model for its owners to expose devices, which can be consumed by clients inspired by the Sensor-as-a-Service model. The main idea relies on the fact that users, such as developers and researchers, do not always have access to the necessary hardware and resources. Exposing devices in IoT should impact these persons activities. Then, we present the Resource Management Architecture, where several IoT Objects endowed with sensors and actuators can be added to environments that are represented virtually in the architecture. The IoT Objects become available to be consumed by users through the use of applications. The architecture is composed of three layers: one representing devices, the cloud solution, and applications, and how they interact with each other. We also present a study case for testing the whole approach in a smart city scenario.
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.
Ambient Intelligent (AmI) environments dynamically provide contextual information to intelligent agents that interact with them. In such environments, could these agents cooperate to improve their goal achievement, considering multiple intentions from several agents? With multiple agents, cooperation will depend on each agent's own intentions. Agents adapt to dynamic changes in the environment using context-aware planning mechanisms such as the Contextual Planning System (CPS), which proposes an optimal plan for a single agent based on the current context. In this paper we present the Collective CPS (CCPS), an opportunistic cooperative planning mechanism for multiple agents in AmI environments. CCPS allows agents to partially delegate their own plans or to collaborate with other agents' plans during their execution, while retaining individual planning capabilities. A working scenario is shown for a realistic AmI environment, such as a smart Campus.
Context-aware systems are capable of perceiving the physical environment where they are deployed and adapt their behavior, depending on the available information and how it is processed. Ambient Intelligence (AmI) represents context-aware environments that react and respond to the requirements of people. While different models can be used to implement adaptive context-aware systems, BDI multiagent systems are especially suitable for that, due to their belief-based reasoning. Different BDI architectures, however, use different reasoning processes, therefore providing different adaptability levels. In each architecture, contextual information is adherent to a specific belief structure, and the context-related capabilities may vary. We propose a framework that can be used by BDI agents in a multi-architecture scenario in order to modularly acquire context-aware capabilities, such as learning, additional reasoning abilities, and interoperability. When this framework is combined with an existing BDI agent, the result is an augmented agent.
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