Counterfactual learning in enhancing resilience in autonomous agent systems

Resilience in autonomous agent systems is about having the capacity to anticipate, respond to, adapt and recover from adverse dynamic conditions complex environments. It associated with intelligence possessed by agents preserve functionality or minimize impact on through a transformation, reconfiguration, expansion performed across system. Enhancing resilience of could pave way toward higher autonomy allowing them tackle intricate problems. The state-of-the-art have mostly focussed improving redundancy system, adopting decentralized control architectures, utilizing distributed sensing capabilities. While machine learning approaches for efficient distribution allocation skills tasks enhanced potential these systems, they are still limited when presented To move beyond current limitations, this paper advocates incorporating counterfactual models enable ability predict possible future adjust their behavior. Counterfactual topic that has recently been gaining attention as model-agnostic post-hoc technique improve explainability models. Using causality can also help gain insights into unforeseen circumstances make inferences probability desired outcomes. We propose be used means guide prepare cope unanticipated environmental conditions. This supplementary support adaptation design more intelligent address multifaceted characteristics real-world problem domains.