This article introduces the notion of environment programming in software multiagent systems (MAS) and describes a concrete computational and programming model based on the artifact abstraction and implemented by the CArtAgO framework. Environment programming accounts for conceiving the computational environment where agents are situated as a first-class abstraction for programming MAS, namely a part of the system that can be designed and programmed-aside to agents-to encapsulate functionalities that will be exploited by agents at runtime. From a programming and software engineering perspective, this is meant to improve the modularity, extensibility and reusability of the MAS as a software system. By adopting the A&A meta-model, we consider environments populated by a dynamic set of computational entities called artifacts, collected in workspaces. From the agent viewpoint, artifacts are first-class entities of their environment, representing resources and tools that they can dynamically instantiate, share and use to support individual and collective activities. From the MAS programmer viewpoint, artifacts are a first-class abstraction to shape and program functional environments that agents will exploit at runtime, including functionalities that concern agent interaction, coordination, organisation, and the interaction with the external environment. The article includes a description of the main concepts concerning artifact-based environments and related CArtAgO technology, as well as an overview of their application in MAS programming.

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Abstract-Agent modularisation is a main issue in agent and multi-agent system programming. Existing solutions typically propose some kinds of constructs -such as capabilities -to group and encapsulate in well-defined modules inside the agent different kinds of agent features, that depend on the architecture or model adopted-examples are goals, beliefs, intentions, skills. In this paper we introduce a further perspective, which can be considered complimentary to existing approaches, which accounts for externalizing some of such functionalities into the computational environment where agents are (logically) situated. In this perspective, agent modules are realised as suitably designed artifacts that agents can dynamically exploit as external tools to enhance their action repertoire and -more generally -their capability to execute tasks. Then, to let agent (and agent programmers) exploit such capabilities abstracting from the low-level mechanics of artifact management and use, we exploit the dual notion of internalization, which consists in dynamically consulting and automatically embedding high-level usage protocols described in artifact manuals as agent plans. The idea is discussed providing some practical examples of use, based on CArtAgO as technology for programming artifacts and Jason agent platform to program the agents.

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International audienceAgents and Artifacts model extended with organisation promotes artifact based environments aimed at supporting multi agent coordination and goal oriented interactions and communication. Nevertheless, the use of artifacts for organisational purposes constrains agents to be aware of complex structures described in an organisational specification. Considering this requirement, we propose "organisational embodiment rules" as a programmable layer for building embodied organisational artifacts (EOA) through their binding to environment artifacts. EOAs are aimed at transparently interceding with the organisational structures, and at enabling possibly organisation-unaware agents to seamlessly play in organisations with no need to deal with low level primitives of an organisational specification. We propose a formal description along with examples enlightening benefits of the proposed approach with respect to related ones

Artificial agents engaged in real world applications require accurate allocation strategies in order to better balance the use of their bounded resources. In particular, during their epistemic activities, they should be able to filter out all irrelevant information and just consider what is relevant for the current task that they are trying to solve. The aim of this work is to propose a mechanism of relevance-based belief update to be implemented in a BDI cognitive agent. This is in order to improve the performance of agents in information-rich environments. In the first part of the paper we present the formal and abstract model of the mechanism. In the second part we present its implementation in the Jason programming platform and we discuss its performance in simulation trials.