Collaborative Adaptive Autonomous Agents

Research on autonomous agents and vehicles has gained momentum in the past years, which is reflected in the extensive body of literature and the investment of big players of the industry in the development of products such as self-driving cars. One open problem in the area is the modelling of interaction between different actors (software agents, humans), such that there is smooth collaboration and transfers of control between the involved parties.

The overall ambition is the achievement of human-like teamwork. One way to approach this problem employs the concept of adaptive autonomy. Agents that display adaptive autonomous behaviour are able to change their autonomy levels as a response to changing circumstances during their operation, by making decisions about whether to depend on one another or allow others to depend on them. Thus, autonomy is expressed in terms of dependencies between agents. As these dependencies change in time, so does autonomy.

This research proposes an agent architecture that models the internal operation of an agent, and uses the concept willingness to interact as the backbone of adaptive autonomous behaviour. The willingness to interact captures both aspects of interaction, through its two components, the willingness to ask for help – the likelihood that an agent will ask for assistance, and the willingness to give help – the likelihood that an agent will help upon being requested. A mathematical framework is developed which estimates the components of the willingness to interact by integrating the impact of several factors that are argued to be relevant for shaping adaptive autonomous behaviour.

This proposal is evaluated by comparing the performance of a population of agents with adaptive autonomy to agents with static autonomy or strategies. Moreover, the role of initial configurations for the willingness to interact, different schemes for the update of its components, and issues related to agent exploitation are investigated. Results indicate the potential benefit of the proposed solution with respect to agents with static strategies.