Collective decision making by embodied neural agents (2411.18498v1)

Abstract: Collective decision making using simple social interactions has been studied in many types of multi-agent systems, including robot swarms and human social networks. However, existing multi-agent studies have rarely modeled the neural dynamics that underlie sensorimotor coordination in embodied biological agents. In this study, we investigated collective decisions that resulted from sensorimotor coordination among agents with simple neural dynamics. We equipped our agents with a model of minimal neural dynamics based on the coordination dynamics framework, and embedded them in an environment with a stimulus gradient. In our single-agent setup, the decision between two stimulus sources depends solely on the coordination of the agent's neural dynamics with its environment. In our multi-agent setup, that same decision also depends on the sensorimotor coordination between agents, via their simple social interactions. Our results show that the success of collective decisions depended on a balance of intra-agent, inter-agent, and agent-environment coupling, and we use these results to identify the influences of environmental factors on decision difficulty. More generally, our results demonstrate the impact of intra- and inter-brain coordination dynamics on collective behavior, can contribute to existing knowledge on the functional role of inter-agent synchrony, and are relevant to ongoing developments in neuro-AI and self-organized multi-agent systems.

• The paper models collective decision-making in embodied agents using biologically inspired neural oscillations, showing how a balance of internal, social, and environmental dynamics is crucial for achieving consensus in gradient environments.
• Optimal single-agent performance in decision tasks requires balancing internal neural coupling with external sensory inputs, highlighting the role of neural metastability.
• The findings provide a framework for Social NeuroAI, suggesting that integrating biologically inspired neural dynamics into agents can enhance social interaction and collective decision-making abilities.

Evaluation of Collective Decision Making by Embodied Neural Agents

The paper entitled Collective decision making by embodied neural agents explores the intricate dynamics of decision-making within multi-agent systems, particularly focusing on how neural dynamics influence sensorimotor coordination and collective behavior. This paper contributes to the intersection of collective intelligence and embodied cognitive processes, presenting a model where agents equipped with minimal neural oscillatory dynamics interact both with their environment and amongst themselves.

The authors present an embodied agent model inspired by the Haken-Kelso-Bunz (HKB) framework, utilizing neural oscillations to control agent behavior. They highlight how agents use sensorimotor coordination to make collective decisions in environments with stimulus gradients. Particularly, they show how intra-agent synchrony and inter-agent coupling facilitate or hinder successful navigation toward a consensus, depending on the relative strengths of internal, environmental, and social influences.

Key Findings

1. Single-Agent Dynamics:
   • The paper first examines single-agent scenarios, highlighting how an agent's performance in gradient ascent tasks varies with the balance of internal neural coupling and sensory inputs. Optimal decision-making arises within intermediate ranges of these parameters, where agents can maintain neural metastability and adequately process environmental stimuli.
2. Collective Dynamics:
   • In multi-agent settings, the research examines how agents achieve consensus. Success in collective decision-making is contingent on a delicate balance of social sensitivity, environmental sensing, and internal neural dynamics.
   • The authors discovered that overly strong coupling to external stimuli without sufficient internal modulation impairs consensus-building processes.
3. Coordination Metrics:
   • Metrics such as Kuramoto Order Parameter (KOP), phase-locking value (PLV), and weighted phase-lag index (wPLI) are employed to quantify intra- and inter-agent neural dynamics. These measures demonstrate varying degrees of alignment and phase coordination that correspond to different agent configurations and environmental setups.

Implications for NeuroAI and Multi-Agent Systems

The results provide insights into the theoretical foundations of embodied cognition, reinforcing the significance of integrating neural dynamics into models of artificial agents for enhanced social interaction and decision-making capabilities. By linking agent behavior to biologically inspired neural mechanisms, the paper paves the way for future research in Social NeuroAI, proposing a framework with potentially broad applicability in designing agents capable of navigating complex, social, and dynamic environments.

This paper aligns agents' decision-making complexity with factors intrinsic to both agents and their environments, analogous to real-world human and animal collective behaviors, where social and environmental interactions profoundly influence group dynamics. By doing so, it delineates a potential pathway for developing AI systems characterized by more nuanced and adaptive social interactivity.

Future Directions

Possible future research areas highlighted by the paper include the examination of more complex environments with multiple decision points and dynamically changing stimuli. Furthermore, investigating the role of noise within such models could uncover insightful dynamics around decision robustness and error correction. Additionally, extending these frameworks to simulate more realistic neural processes could further enhance the biological plausibility and utility of agent-based modeling in NeuroAI.

Conclusion

The work provides a compelling exploration of the foundational dynamics underlying collective decision-making in AI. By integrating neural dynamics within the agent model, it successfully bridges computational neuroscience and multi-agent systems research, offering promising insights for the evolution of socially adept AI. As progress in AI increasingly demands sophisticated social interaction capabilities, frameworks like the one discussed here could prove crucial in developing the next generation of AI agents well-integrated with real-world environments.