In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

Abstract Machine learning and computational processing have advanced such that
automated driving systems (ADSs) are no longer a distant reality. Many automobile …

Robust optimization (RO) has been broadly utilized for decision-making under uncertainty;
however, as a key issue in RO the design of the uncertainty set could exert significant …

Time-series models typically assume untainted and legitimate streams of data. However, a
self-interested adversary may have incentive to corrupt this data, thereby altering a decision …

This paper presents a risk-averse tri-level stochastic game-theoretic model between the
defender and the attacker in application to the supply networks. A real supposition is that the …

The prevalence of security threats to organizational defense demands models that support
real-world policymaking. Security games are a potent tool in this regard; however, although …

As a variant task of time-series segmentation, trajectory segmentation is a key task in the
applications of transportation pattern recognition and traffic analysis. However, segmenting …

The public sector is characterized by hierarchical and interdependent organizations. For
defense and security applications in particular, a higher authority is generally responsible for …

Some health and safety (HSE) managers within the offshore wind industry lack effective
interorganizational collaboration strategies in emergency management (EM) for successful …

As an effective paradigm to address uncertainty in payoffs and transition probabilities, robust
stochastic games have been formulated in the literature. This paper is concerned with the …