In adversarial video games, human players and non-player characters (NPCs) compete to win certain goods or targets. To gain an advantage and create realistic behaviors, NPCs could cooperate by forming groups or coalitions. Surprisingly enough, they typically do not. Consequently, we have concentrated our efforts on providing game developers with the necessary tools to enhance virtual characters with teamwork capabilities. To start with, we address this challenge by observing that a classical Reconfiguration Algorithm, known in MAS, can serve as an umbrella for teamwork-related activities of NPCs. Reconfiguration amounts to intelligent replanning in response to changing circumstances. By analogy, our solution, inspired by the reconfiguration process, takes the control over the game and keeps it going by utilizing mechanisms that facilitate cooperation under adversarial conditions. As a proof of concept, we have developed, implemented, and tested Ara, the Adversarial Reconfiguration Algorithm, alongside an encompassing agent-oriented framework called Arag and a simple stealth video game called Treasure Hunt. Arag facilitates a flexible approach to aspects of teamwork such as belief representation, communication, reconfiguration, and task allocation. As demonstrated by Treasure Hunt, NPCs coordinated by Arag outperform those lacking cooperative capabilities, even with the adopted simplifying design choices.
Achieving expert-level performance through simulation-based training relies heavily on complex and adaptable scenarios, but creating these scenarios manually is often laborious and resource-intensive. Large Language Models (LLMs) offer a promising avenue to automate and enhance scenario generation. However, their reliance on purely sequential text generation in standard prompting settings can hinder consistent understanding in complex systems. We present a multi-agent reasoning framework to leverage LLMs for puzzle generation within the 2D Physics Puzzle Environment CREATE (Chain REAction Tool Environment) to overcome these limitations. This testbed is used as a simplified analogy for scenario generation to allow the development of fundamental LLM capabilities needed to collaboratively design and solve intricate challenges, with a long-term goal of application in domains such as military training. Our framework employs a multi-agent ReAct architecture, integrating reasoning and action feedback loops to dynamically interact with CREATE. By assigning distinct roles, such as solver and designer, to individual agents, our framework preserves the complex reasoning pathways required for solving and generating puzzles—enabling complex reasoning that was too difficult to achieve with basic prompting or single-agent approaches. This work represents a step towards more robust LLM-driven scenario generation by demonstrating the ability of a multi-agent system built on our framework, while interacting with CREATE simulations, to collaboratively perform multi-step reasoning and adapt to environmental constraints. While not yet achieving real-world scenario generation, our findings demonstrate the potential of LLMs to generate solvable puzzles aligned with user prompts. However, we also highlight and address persistent challenges with their reasoning about precise spatial relationships and understanding complex, multi-step chain reactions, which are crucial for generating more advanced scenarios. We conclude by discussing the future role of multi-agent LLM frameworks in creating realistic and adaptable training scenarios for various applications, building upon the foundational capabilities developed in this work. Examples and our full code are available at: https://github.com/binzeli/Puzzle_Generation .
The connections between neuroscience findings and Artificial Intelligence (AI) are very strong, but literature that analyzes how neuroscience inspired AI, and viceversa, mainly takes a machine learning point of view. However, intelligent software agents modeled after the Belief-Desire-Intention (BDI) architecture have many ties with neuroscience. Some are explicitly expressed, others are less evident and deserve to be better addressed and understood. In order to explore such ties and make their hidden potential exploitable, we introduce the BDI-inspired MEDiTATe conceptual framework encompassing theory of Mind, Emotions, Deep TAlk, and small Talk. MEDiTATe is intended as a principled means to analyze the connections between neuroscience and BDI approaches in a systematic way, and to interact with neuro-scientists by sharing a common terminological ground. The main contribution of this paper is indeed to survey the relevant scientific literature and organize the findings of this review coherently with the MEDiTATe vision. Nonetheless, most modules of MEDiTATe have been, or may be, implemented using a well known toolkit for BDI agents, Jason. In this sense, the possibility to move MEDiTATe from the conceptual level to the practical one is backed up by existing software tools. Targeting Jason and its JaCaMo extension only is a limitation of the current MEDiTATe approach, but it is the limitation that makes a shift from theory to practice feasible. MEDiTATe features small talk and deep talk that we conjecture to be related but distinct cognitive functions, each with its own purpose and possibly dedicated different brain areas. We expect that MEDiTATe – once fully developed in a practical Jason-based toolkit – may support the study of these functions and of their relations with other, better understood, cognitive processes, possibly inspiring experiments by neuro-scientists to validate the hypothesis. In fact, in our long-term vision, MEDiTATe should offer to computer scientists and neuro-scientists a shared gym for experimenting models and theories of brain functioning.
The rapid development of LLM-based language agents has led to a proliferation of architectures described using ad hoc and inconsistent methods, making it difficult to compare, reproduce, extend or even understand them. To address this, we introduce FALAA: a framework that standardizes the description of language agent architectures through a structured set of components—Planner, Executor, Evaluator, Reflector, Memory, and Environment—and a dual-level methodology combining UML diagrams and OCL specifications. FALAA offers both conceptual clarity and formal precision, enabling unambiguous definitions of agent behaviors and responsibilities. We illustrate its effectiveness through case studies of two representative agents: Reflexion and Retroformer. The formalization reveals critical ambiguities in both architectures, such as vague component definitions and underspecified memory handling. These results show how FALAA enhances clarity, supports architecture comparison, and uncovers design limitations, making it a promising tool for the development and analysis of future language agents.
In traditional BDI (Belief-Desire-Intention) Multiagent Systems (MAS), agent communication languages such as KQML (Knowledge Query and Manipulation Language) facilitate structured message exchange and are supported by widespread BDI implementations like Jason. However, KQML lacks mechanisms to account for the situatedness of agents within dynamic environments. This paper proposes an extension to KQML, KQML-S, that incorporates the notion of locality, enabling message semantics to adapt based on the agents’ shared context. Specifically, we introduce a framework where agents within the same logical communication space, or “logical room”, perceive indirect updates from interactions occurring within their locality. We present the theoretical foundations of KQML-S and its implementation within VEsNA (Virtual Environments via Natural language Agents), a framework extending Jason with chatbots for natural language interaction, and a Virtual Reality environment implemented in Godot. KQML-S bridges two foundational features of agents: social ability and situatedness. VEsNA agents, being natively situated in Virtual Reality and inheriting KQML communication from Jason, fully exploit the potential of KQML-S and showcase its usefulness in those scenarios where agents are logically or physically embodied in a discrete, “room-based” environment.
