Adaptability lies at the heart of effective teams and it is through management of interdependence that teams are able to adapt. This makes interdependence a critical factor of human-machine teams. Nevertheless, engineers building human-machine systems still rely on the same tools and techniques used to build individual behaviors which were never designed to address the complexity that stems from interdependence in joint activity. Many engineering approaches lack any systematic rigor and formal method for identifying, managing and exploiting interdependence, which forces ad hoc solutions or workarounds. This gap between theories of interdependence and operable tooling leaves designers blind to the issues and consequences of failing to adequately address interdependence within human-machine teams. In this article, we propose an approach to operationalizing core concepts needed to address interdependence in support of adaptive teamwork. We describe a formalized structure, joint activity graphs, built on interdependence design principles to capture the essence of joint activity. We describe the runtime requirements needed to dynamically exploit joint activity graphs and to support intelligent coordination during execution. We demonstrate the effectiveness of such a structure at supporting adaptability using the Capture-the-Flag domain with heterogeneous teams of unmanned aerial vehicles and unmanned ground systems. In this dynamic adversarial domain, we show how agents can make use of the information provided by joint activity graphs to generally and pragmatically react and adapt to perturbations in the joint activity, the environment, or the team and explicitly manage and exploit interdependence to produce effective teamwork. In doing so, we demonstrate how flexible and adaptive teamwork can be achieved through formally guided design that supports effective management of interdependence.

Game theory offers techniques for applying autonomy in the field. In this mini-review, we define autonomy, and briefly overview game theory with a focus on Nash and Stackleberg equilibria and Social dilemma. We provide a discussion of successful projects using game theory approaches applied to several autonomous systems.

We present a unified approach to multi-agent autonomous coordination in complex and uncertain environments, using path planning as a problem context. We start by posing the problem on a probabilistic factor graph, showing how various path planning algorithms can be translated into specific message composition rules. This unified approach provides a very general framework that, in addition to including standard algorithms (such as sum-product, max-product, dynamic programming and mixed Reward/Entropy criteria-based algorithms), expands the design options for smoother or sharper distributions (resulting in a generalized sum/max-product algorithm, a smooth dynamic programming algorithm and a modified versions of the reward/entropy recursions). The main purpose of this contribution is to extend this framework to a multi-agent system, which by its nature defines a totally different context. Indeed, when there are interdependencies among the key elements of a hybrid team (such as goals, changing mission environment, assets and threats/obstacles/constraints), interactive optimization algorithms should provide the tools for producing intelligent courses of action that are congruent with and overcome bounded rationality and cognitive biases inherent in human decision-making. Our work, using path planning as a domain of application, seeks to make progress towards this aim by providing a scientifically rigorous algorithmic framework for proactive agent autonomy.