This paper deals with the optimization of the flow of sterile instruments in hospitals which takes place between the sterilization department and the operating theatre. This topic is especially of interest in view of the current attempts of hospitals to cut cost by outsourcing sterilization tasks. Oftentimes, outsourcing implies placing the sterilization unit at a larger distance, hence introducing a longer logistic loop, which may result in lower instrument availability, and higher cost. This paper discusses the optimization problems that have to be solved when redesigning processes so as to improve material availability and reduce cost. We consider changing the logistic management principles, use of visibility information, and optimizing the composition of the nets of sterile materials.

This paper proposes a new algorithm, called best reply search (BRS), for deterministic multiplayer games with perfect information. In BRS, only the opponent with the strongest counter move is allowed to make a move. More turns of the root player can be searched resulting in long-term planning. We test BRS in the games of Chinese Checkers, Focus, and Rolit™. In all games, BRS is superior to the maxn algorithm. We show that BRS also outperforms paranoid in Chinese Checkers and Focus. In Rolit, BRS is on equal footing with paranoid. We conclude that BRS is a promising search method for deterministic multiplayer games with perfect information.

The rapid progress in artificial intelligence enables technology to more and more become a partner of humans in a team, rather than being a tool. Even more than in human teams, partners of human–agent teams have different strengths and weaknesses, and they must acknowledge and utilize their respective capabilities. Coordinated team collaboration can be accomplished by smartly designing the interactions within human–agent teams. Such designs are called Team Design Patterns (TDPs). We investigated the effects of a specific TDP on proactive task reassignment. This TDP supports team members to dynamically allocate tasks by utilizing their knowledge about the task demands and about the capabilities of team members. In a pilot study, agent–agent teams were used to study the effectiveness of proactive task reassignment. Results showed that this TDP improves a team’s performance, provided that partners have accurate knowledge representations of each member’s skill level. The main study of this paper addresses the effects of task reassignments in a human–agent team. It was hypothesized that when agents provide explanations when issuing and responding to task reassignment requests, this will enhance the quality of the human’s mental model. Results confirmed that participants developed more accurate mental models when agent-partners provide explanations. This did not result in a higher performance of the human–agent team, however. The study contributes to our understanding of designing effective collaboration in human–agent teams.

With recent technological advances, commanders request the support of artificial intelligence (AI)-enabled systems during mission planning. Future AI systems may test a wide range of courses of action (COAs) and use a simulator to test each COA’s effectiveness in a war game. The COA’s effectiveness is however dependent on the commanders’ intent. The question arises to what degree a machine can understand the commanders’ intent? Currently, the intent has to be programmed manually, costing valuable time. Therefore, we tested whether a tool can understand a freely written intent so that a commander can work with an AI system with minimal effort. The work consisted of letting a tool understand the language and grammar of the commander to find relevant information in the intent; creating a (visual) representation of the intent to the commander (back brief); and creating an intent-based computable measure of effectiveness. We proposed a novel quantitative evaluation metric for understanding the commanders’ intent and tested the results qualitatively with platoon commanders of the 11th Airmobile Brigade. They were positively surprised with the level of understanding and appreciated the validation feedback. The computable measure of effectiveness is the first step toward bridging the gap between the command intent and machine learning for military mission planning.