The paper introduces an Agent-Oriented Programming (AOP) framework based on the Belief-Desire-Intention (BDI) model of agency. The novelty of this framework is in relying on the Actor model, instantiating each intentional agent as an autonomous micro-system run by actors. The working hypothesis behind this choice is that defining the agents via actors results in a more fine-grained modular architecture and that the execution of agent-oriented programs is enhanced (in scalability as well as in performance) by relying on robust implementations of Actor models such as Akka. The framework is benchmarked and analyzed quantitatively and qualitatively against three other AOP frameworks: Jason, ASTRA and Sarl.

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This paper introduces a modular architecture for integrating norms in autonomous agents and multi-agent systems. As the interactions between norms and agents can be complex, this architecture utilizes multiple programmable components to model concepts such as adoption of personal and/or collective norms (possibly conflicting), interpretation and qualification as mappings between social and normative contexts, intentionally (non-)compliant behaviors, and resolution of conflicts between norms and desires (or other norms). The architecture revolves around normative advisors, that act as the bridge between intentional agents and the institutional reality. As a technical contribution, a running implementation of the architecture is presented based on the ASC2 (AgentScript) BDI framework and eFLINT normative reasoner.

Testing undeniably plays a central role in the daily practice of software engineering, and this explains why better and more efficient libraries and services are continuously made available to developers and designers. Could the MAS developers community similarly benefit from utilizing state-of-the-art testing approaches? The paper investigates the possibility of bringing modern software testing tools as those used in mainstream software engineering into multi-agent systems engineering. Our contribution explores and illustrates, by means of a concrete example, the possible interactions between the agent-based programming framework ASC2 (AgentScript Cross-Compiler) and various testing approaches (unit/agent testing, integration/system testing, continuous integration) and elaborate on how the design choices of ASC2 enable these interactions.

Current agent architectures implementing the belief-desire-intention (BDI) model consider agents which respond reactively to internal and external events by selecting the first-available plan. Priority between plans is hard-coded in the program, and so the reasons why a certain plan is preferred remain in the programmer's mind. Recent works that attempt to include explicit preferences in BDI agents treat preferences essentially as a rationale for planning tasks to be performed at run-time, thus disrupting the reactive nature of agents. In this paper we propose a method to include declarative preferences (i.e. concerning states of affairs) in the agent program, and to use them in a manner that preserves reactivity. To achieve this, the plan prioritization step is performed offline, by (a) generating all possible outcomes of situated plan executions, (b) selecting a relevant subset of situation/outcomes couplings as representative summary for each plan, (c) sorting the plans by evaluating summaries through the agent's preferences. The task of generating outcomes in several conditions is performed by translating the agent's procedural knowledge to an ASP program using discrete-event calculus.