The integration of Belief-Desire-Intention (BDI) agents with robotic systems offers a promising approach to programming autonomy and decision-making in complex environments. This paper extends an interface to combine the Jason agent programming language with the Robot Operating System (ROS) to control multiple robots. We evaluate this implementation through three case studies of increasing complexity and benchmark it against a functionally equivalent Python-based implementation using qualitative and quantitative metrics. The outcomes are directly compared with those of the Python baseline. Our study outlines the experimental design and evaluation methodology, aiming to clarify the trade-offs between agent-oriented and imperative approaches in multi-robot systems and to identify scenarios in which BDI-based models provide advantages in responsiveness, robustness, and scalability.
Large-language models (LLMs) incorporated into agentic systems present exciting opportunities to accelerate drug discovery. In this study, we examine the modularity of LLM-based agentic systems for drug discovery, i.e., whether parts of the system are interchangeable, a topic that has received limited attention in drug discovery. We compare the performance of different LLMs and the effectiveness of tool-calling agents versus code-generating agents. Our case study, comparing performance in orchestrating tools for chemistry and drug discovery using an LLM-as-a-judge score, shows that Claude-3.5-Sonnet, Claude-3.7-Sonnet and GPT-4o outperform alternative LLMs such as Llama-3.1-8B, Llama-3.1-70B, GPT-3.5-Turbo, and Nova-Micro. Although we confirm that code-generating agents outperform the tool-calling ones on average, we show that this is highly question- and model-dependent. Furthermore, the impact of replacing the system promptl is dependent on the question and model, underscoring that even in this particular domain one cannot replace components of the system without re-engineering. Our study highlights the necessity of further research into the modularity of LLM-based agentic systems to enable the development of reliable and modular solutions for real-world problems.
Business processes specify patterns of activities expected to occur in an organization to achieve a business goal. However, actual executions may deviate from the expected ones and not necessarily because of mistakes, but also due to adaptation to circumstances or circumvention of limitations. To cope with this issue, organizations rely on accountability frameworks to get reliable information about deviations to undertake corrective measures for their processes, goals, or organization. However, to be effectively carried out, accountability has to rely on additional structures to be put in place, such as approvals and checking activities, which come at a price. So, these additional structures should be parsimoniously introduced. The paper presents a strategy to identify the activities that are riskier than others, and for which it is reasonable to foresee accountability structures. This evaluation is based on execution logs, analyzing which activities are not executed as expected from the business process.
Achieving high realism in Virtual Reality (VR) simulations with many Virtual Humans (VH) is a complex task and involves many different and challenging aspects. This paper focuses on enhancing VH autonomy to enable intelligent behaviours and interaction with the environment. Drawing from research in autonomous intelligent agents and Multiagent Systems (MAS), we analyse which results that the MAS community already obtained can be used in VR to improve VH behaviour, and which requirements should simulation tools fulfill in order to support them. To showcase our vision, we present the JaCa-Unity library for bridging the JaCaMo platform and Unity, and we show how JaCa-Unity has been integrated in VEsNA , an ecosystem that allows Jason agents to interact with humans via a chatbot-like interface and to be immersed within a VR environment.
We propose the study of diffusion of two secrets in a heterogeneous environment, that is, there are two directed networks over the same set of agents. Our model combines two group identification processes, each with independent desiderata. Specifically, we have two types of targets: constructive, where we aim to expose a given group of agents to a secret, and destructive, where we aim to prevent a given group of agents from being exposed to a secret. To secure these targets, we can employ different control actions: either deleting an agent or introducing a previously latent agent. For all combinations of the aforementioned targets and actions, we provide a complexity classification. Since classical analysis mostly yields negative results, we adopt a parameterized perspective, considering (combinations of) natural parameters, such as the number of controlled agents or the number of rounds of the activation process. Taking a parameterized perspective, we provide several FPT and XP algorithms along with their respective lower bounds.
