A Hierarchical Decision-Making Framework in Social Networks for Efficient Disaster Management

One of the major challenges faced by the current society is developing disaster management strategies to minimize the effects of catastrophic events. Disaster planning and strategy development phases of this urgency require larger amounts of cooperation among communities or individuals in society. Social networks have also been playing a crucial role in the establishment of efficient disaster management planning. This article proposes a hierarchical decision-making framework that would assist in analyzing two imperative information flow processes (innovation diffusion and opinion formation) in social networks under the consideration of community detection. The proposed framework was proven to capture the heterogeneity of individuals using cognitive behavior models and evaluate its impact on diffusion speed and opinion convergence. Moreover, the framework demonstrated the evolution of communities based on their inter-and intracommunication. The simulation results with real social network data suggest that the model can aid in establishing an efficient disaster management policy using social sensing and delivery.

Monkey Plays Pac-Man with Compositional Strategies and Hierarchical Decision-making

Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals. Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys' decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy. The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations. With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals' advanced cognition.

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchical decision-making perspective. From the view of the actual mission planning issue, the two key problems to be solved in UAV collaborative mission planning are mission allocation and route planning. In this paper, both of these problems are taken into account via a hierarchical decision-making model. Firstly, we use a target clustering algorithm to divide the original targets into target subgroups, where each target subgroup contains multiple targets. Secondly, a fuzzy ant colony algorithm is used to calculate the global path between target subgroups for a single-target group. Thirdly, a fuzzy ant colony algorithm is also used to calculate the local path between multiple targets for a single-target subgroup. After three levels of decision-making, the complete path for multiple UAVs can be obtained. In order to improve the efficiency of a collaborative task between different types of UAVs, a cooperative communication strategy is developed, which can reduce the number of UAVs performing tasks. Finally, experimental results demonstrate the effectiveness of the proposed cooperative mission planning and cooperative communication strategy for multiple UAVs.