Collective decision making by embodied neural agents

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 maps minimal neural dynamics to embodied agent behaviors using the Haken-Kelso-Bunz oscillator model.
• Simulations demonstrate that balancing internal oscillator coupling with sensory and social inputs optimizes consensus performance.
• The study pioneers integrating enactive cognition into multi-agent systems, offering new pathways for sophisticated collective decision making.

Collective Decision Making by Embodied Neural Agents

This essay provides a comprehensive analysis of the paper "Collective Decision Making by Embodied Neural Agents" (2411.18498). The paper explores the neural dynamics underlying collective decision-making processes in multi-agent systems. These agents are embedded with an oscillatory model projected onto embodied agents in environments with stimulus gradients.

Neural Dynamics and Agent Architecture

The core contribution of the paper is the mapping of minimal neural dynamics to agent behaviors by leveraging the Haken-Kelso-Bunz (HKB) oscillator model. The agents are equipped with a minimal architecture featuring sensory and motor oscillators that facilitate sensorimotor coordination steering collective behavior (Figure 1). Initial simulations demonstrate dependency on the internal oscillator coupling and external sensory input for realizing adaptive agent behaviors.

Figure 1: Single-agent behavior and neural dynamics. (A) The agent architecture: two sensory oscillators (nodes 1 and 2) and two motor oscillators (nodes 3 and 4).

The model extends the traditional Braitenberg vehicle with an enactive cognition framework to simulate biological-like intraneural coordination. This integration allows the agents to demonstrate behaviors akin to reducing phase-locking value (PLV) under certain parameter regimes conducive to task performance, which in this case is moving towards a stimulus source.

Multi-agent Simulations and Collective Dynamics

Expanding from single-agent behavior, the study investigates how agents with differing initial states and parameters react in environments featuring dual stimulus sources. Consensus-building in collective decision-making scenarios is facilitated by intra-agent, inter-agent, and agent-environment couplings.

Figure 2: Agent behavior and intra-agent neural dynamics during collective decision making.

Simulation outcomes illustrate that a meticulous balance between environmental influences, social sensitivity, and internal oscillator coupling maximizes consensus performance. Disruptions in this balance can lead to convergence failure despite aligned inter-agent movements. Notably, overly strong social influences without sufficient internal coupling yield paradoxical outcomes, reducing task performance despite high movement alignment.

Implications and Future Directions

This research emphasizes the importance of integrating neural oscillator models into multi-agent systems for sophisticated behaviors driven by intra- and inter-agent dynamics. The capacity for the system dynamics to negotiate between internal and external stimuli has potential implications for enhancing AI embodiment strategies.

The findings propose a departure from simplistic modeling for multi-agent consensus strategies, projecting a biological plausibility not traditionally embraced in such computational paradigms. Future explorations can extend this work to multi-source environments and test robustness under variable initial conditions and noise influences.

Conclusion

The integration of neural dynamics into embodied agents elucidates underlying coordination mechanisms pivotal for consensual decision-making in distributed systems. This paper enriches Social NeuroAI research by bridging adaptive cognitive processing with measureable collective behaviors in multi-agent settings. The insights gleaned suggest tangible pathways for advancing agent interaction within dynamic environments, extending possibilities for both human- and AI-centric collective task executions.