Modeling crowd behavior is an important challenge for cognitive modelers. Models of crowd behavior facilitate analysis and prediction of human group behavior, where people are close geographically or logically, and are affected by each other's presence and actions. Existing models of crowd behavior, in a variety of fields, leave many open challenges. In particular, psychology models often offer only qualitative description, and do not easily permit algorithmic replication, while computer science models are often not tied to cognitive theory and often focus only on a specific phenomenon (e.g., flocking, bi-directional pedestrian movement), and thus must be switched depending on the goals of the simulation. We propose a novel model of crowd behavior, based on Festinger's Social Comparison Theory (SCT), a social psychology theory known and expanded since the early 1950's. We propose a concrete algorithmic framework for SCT, and evaluate its implementations in several pedestrian movement phenomena such as creation of lanes in bidirectional movement and movement in groups with and without obstacle. Compared to popular models from the literature, the SCT model was shown to provide improved results. We also evaluate the SCT model on general pedestrian movement, and validate the model against human pedestrian behavior. The results show that SCT generates behavior more intune with human crowd behavior then existing non-cognitive models.Keywords Cognitive modeling · Modeling pedestrian crowd behavior · Model of social comparison theory N. Fridman ( ) · G.A. Kaminka

There is a growing need for coverage of large maritime areas, mainly in the exclusive economic zone (EEZ). Due to the difficulty of accessing such large areas, the use of satellite based sensors is the most efficient and cost-effective way to perform this task. Vessel behavior prediction is a necessary ability for detection of moving vessels with satellite imagery. In this paper we present an algorithm for selection of the best satellite observation window to detect a moving object. First, we describe a model for vessel behavior prediction and compare its performance to two base models. We use real marine traffic data (AIS) to compare their ability to predict vessel behavior in a time frame of between 1–24 hours. Then, we present a KINGFISHER, maritime intelligence system which uses our algorithm to track suspected vessels with satellite sensor. We also present the results of the algorithm in operational scenarios of the KINGFISHER.

Models of crowd dynamics are critically important for urban planning and management. They support analysis, facilitate qualitative and quantitative predictions, and synthesize behaviors for simulations. One promising approach to crowd modeling relies on micro-level agent-based simulations, where the interactions of simulated individual agents in the crowd result in macro-level crowd dynamics which are the object of study. This article reports on an agent-based model of urban pedestrian crowds, where culture is explicitly modeled . We extend an established agent-based social agent model, inspired by social psychology, to account for individual cultural attributes discussed in social science literature. We then embed the model in a simulation of pedestrians and explore the resulting macro-level crowd behaviors, such as pedestrian flow, lane changes rate, and so on. We validate the model by quantitatively comparing the simulation results to the pedestrian dynamics in movies of human crowds in five different countries: Iraq, Israel, England, Canada, and France. We conclude that the model can faithfully replicate urban pedestrians in different cultures. Encouraged by these results, we explore simulations of mixed-culture pedestrian crowds.

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Modeling crowd behavior is an important challenge for cognitive modelers.Models of crowd behavior facilitate analysis and prediction of human group behavior, where people are close geographically or logically states, and are affected by each other's presence and actions. Existing models of crowd behavior, in a variety of fields, leave many open challenges. In particular, psychology models often offer only qualitative description, and do not easily permit algorithmic replication, while computer science models are often simplistic, treating agents as simple deterministic particles. We propose a novel model of crowd behavior, based on Festinger's Social Comparison Theory (SCT), a social psychology theory known and expanded since the early 1950's. We propose a concrete algorithmic framework for SCT, and evaluate its implementations in several crowd behavior scenarios such as pedestrian movement, gathering and imitational behavior. We show that our SCT model produces improved results compared to base models from the literature. We describe two possible implementations of SCT process in an architectural level. The first, which seems to follow directly from Festinger's Social Comparison theory, treats the SCT process as an uncertainty-resolution method. The second, takes a different approach, in which an SCT process is constantly active, in parallel to any problem solving activity. We present the implementation of these approaches in Soar cognitive architecture. Moreover, we examine these approaches in the context of crowd behavior simulations and argue that surprisingly, it is the second approach which is correct.