Collective decision making by embodied neural agents

TL;DR

The results show that the success of collective decisions depended on a balance of intra-agent, interagent, and agent–environment coupling, and this work uses these results to identify the influences of environmental factors on decision difficulty.

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.

Figures (5)

• Figure 1: Single-agent behavior and neural dynamics. (A) The agent architecture: two sensors, each connected to a sensory oscillator, nodes 1 and 2 ($v_1$ and $v_2$), that are each in turn connected to a motor oscillator, nodes 3 and 4 ($v_3$ and $v_4$). The traveling orientation $\theta$ of the agent is determined by the angle difference $\phi_{v_3, v_4}$ between motor oscillators. (B) Gradient ascent: the agent's trajectory (red) in the environment (brighter colors indicate higher stimulus concentration). (C) Decision making: two stimulus sources are present in the environment and the agent's performance is measured by its ability to approach one of the two. (D) Internal phase locking of oscillators (contralateral sensor--motor and motor--motor) in B. (E) Internal phase locking of oscillators in C.
• Figure 2: Agent behavior and intra-agent neural dynamics during collective decision making. (A) Agents emit stimulus that can be perceived by other agents. (B) Higher social stimulation allows agents to converge onto the same stimulus source in their environment. (C) With lower social stimulation, agents do not converge on the same source. (D-E) Movement angles of agents (gray lines) and KOP of the group, indicating the degree of alignment (red line). Alignment increases when all agents are moving towards the same stimulus source and decreases when they are not. (G-H) Intra- and inter-agent neural dynamics: average intra-agent wPLI (blue line) and average inter-agent wPLI (orange line).
• Figure 3: Intra-agent neural dynamics: the mean PLV and SD(KOP) of each agent during its run ($y$ axis), according to the internal coupling degree ($a_{v_i,v_j}$ and $b_{v_i,v_j}$) in its neural controller ($x$ axis). Squares represent agents without sensory input and dots represent agents with sensory input. Lighter colors represent higher performance. For each configuration (i.e., internal coupling degree and stimulus sensitivity), 50 runs were performed with random initial phases of the oscillators. Each data point represents the average of one run.
• Figure 4: Ternary plots illustrating how collective behavior and neural dynamics depend on the agent configuration. Each point in the triangle corresponds to a certain weighting of environmental stimulus, social information, and internal motor coupling. In each simulation, the parameters fulfill the condition $stimulus \, sensitivity + social \, sensitivity + internal \, coupling = 100$. The scale $[0,50]$ for each dimension corresponds to respective parameter values of $c \in \{0, \ldots ,10\}$ for stimulus sensitivity, $S \in \{0, \ldots ,5\}$ for social sensitivity, and $a_{v_3,v_4} \in \{0, \ldots ,1\}$ for internal coupling. The top corner corresponds to maximal social sensitivity, the left corner to maximal environmental sensitivity, and the right corner to maximal internal coupling. The brightness (yellowness) in panel A indicates the performance of collective decision making. A performance of 0 indicates that agents failed to reach either of the two stimulus sources. A performance of 0.5 indicates that half of the agents reached the same stimulus source. A performance of 1 indicates that all agents reached the same stimulus source and thus that a consensus was reached. The brightness in panels B-E indicate the strength of, respectively, the movement alignment, alignment variability, inter-brain covariance, and intra-brain covariance.
• Figure 5: Dependence of the collective decision-making performance on the environment and initial orientations of agents. The leftmost extreme of the $x$-axis represents the cases with only one stimulus source present in the environment. Moving towards the right, the brightness of a second stimulus source increases until the two have equal brightness. The agents always start with equal angles between them. At the bottom of the $y$-axis, the agents are spread so that the outermost two of the ten agents are at a 180° angle. At the top of the $y$-axis, all agents start with angles of 0° between them. All agents have identical parameters; stimulus sensitivity is $c=3$, social sensitivity is $S=1$, and internal coupling is $a_{v_i,v_j}=0.5$.