CODE - Collective Decisions in Dynamic Environments

The CODE project looks at the processes leading to decision making in decentralised multi-agent systems (e.g., robot swarms), and specifically addresses complex problems that entail (i) uncertain and variable environmental conditions, and (ii) spatial heterogeneities related to the (random) mobility of agents. Decision making is a cognitive process that presents similar dynamics in systems as different as neural populations and insect colonies, and is at the basis of more complex forms of information processing and behaviour (e.g., categorisation, task allocation). Besides the theoretical relevance, the principled understanding of the decentralised mechanisms underlying decision making is fundamental for the design of large-scale artificial distributed systems capable of flexible information processing, adaptive and robust behaviour in face of changing conditions and external (possibly disruptive) events. In this way, it will be possible to provide cognitive capabilities to large-scale distributed systems ranging from multi-robot to cyber-physical systems including embedded devices, humans and robots. Such concrete application scenarios place important requirements and constraints to the decision making abilities of large-scale artificial distributed systems, and demand for a deeper theoretical understanding of the mechanisms and properties underlying the decision dynamics.

Stemming from the concept described above, this project will seek the following specific objectives. (i) Deepen the theoretical understanding of the mechanisms leading to optimal decision making in distributed systems with respect to uncertain and dynamic environmental conditions. (ii) Identify and characterise the interaction dynamics resulting from random mobility of agents under a wide range of conditions, including biases from the presence of other agents (e.g., attractions or repulsions), and study the influence of the mobility pattern on the diffusion of information within the system. (iii) Integrate decision-making and interaction dynamics to identify the mutual relationship between the random mobility of agents and the overlying decisional processes. (iv) Implement optimal collective decision making in large-scale artificial distributed systems (e.g., robot swarms) to deal with dynamic environmental conditions and spatial heterogeneities. (v) Study the influence of external (possibly malicious) users trying to steer the collective decision process, so as to devise human-swarm interaction methods that exploit the knowledge of the collective decision dynamics to maximise efficiency and account for resilience against targeted attacks.

The CODE project will employ a mix of analytical, computational and experimental studies with robot swarms, to account for both abstract problems and concrete scenarios in which spatiality and heterogeneities are present. Analytical studies will be conducted to identify the possible system dynamics and relevant parameterisations. Multi-agent simulations will be developed to implement and test the microscopic interaction rules leading to desired system dynamics, as well as to deal with conditions too complex to be tackled with an analytical approach. Links between microscopic and macroscopic descriptions will be drawn, so as to seamlessly move between the description of the individual rules governing the agents’ behaviour and the system-level dynamics. Robotics experiments provide a mean to test the ability to design artificial distributed systems based on the gathered theoretical knowledge.