Navigating Through Virtual Worlds: From Single Characters to Large Crowds

With the rise and success of digital games over the past few decades, path planning algorithms have become an important aspect in modern game development for all types of genres. Indirectly-controlled playable characters as well as non-player characters have to find their way through the game's environment to reach their goal destinations. Modern gaming hardware and new algorithms enable the simulation of large crowds with thousands of individual characters. Still, the task of generating feasible and believable paths in a time- and storage-efficient way is a big challenge in this emerging and exciting research field. In this chapter, the authors describe classical algorithms and data structures, as well as recent approaches that enable the simulation of new and immersive features related to path planning and crowd simulation in modern games. The authors discuss the pros and cons of such algorithms, give an overview of current research questions and show why graph-based methods will soon be replaced by novel approaches that work on a surface-based representation of the environment

Dynamically Pruned A* for Re-planning in Navigation Meshes

Modern simulations feature crowds of AI-controlled agents moving through dynamic environments, with obstacles appearing or disappearing at run-time. A dynamic navigation mesh can represent the traversable space of such environments. The A* algorithm computes optimal paths through the dual graph of this mesh. When an obstacle is inserted or removed, the mesh changes and agents should re-plan their paths. Many existing re-planning algorithms are too memory-intensive for crowds, or they cannot easily be used on graphs that structurally change. In this paper, we present Dynamically Pruned A* (DPA*), an extension of A* for re-planning optimal paths in dynamic navigation meshes. DPA* has similarities to adaptive algorithms that make the A* heuristic more informed based on previous queries. However, DPA* prunes the search using only the previous path and its relation to the dynamic event. We describe this relation using four scenarios; DPA* uses different rules in each scenario. Our algorithm is memory-friendly and robust against structural changes, which makes it suitable for crowds in dynamic navigation meshes. Experiments show that DPA* performs particularly well in large environments and when the dynamic event is visible to the agent. We integrate the algorithm into crowd simulation software to model large crowds in dynamic environments in real-time

Recovered memories, satanic abuse, Dissociative Identity Disorder and false memories in the UK: a survey of Clinical Psychologists and Hypnotherapists

An online survey was conducted to examine psychological therapists’ experiences of, and beliefs about, cases of recovered memory, satanic / ritualistic abuse, Multiple Personality Disorder / Dissociative Identity Disorder, and false memory. Chartered Clinical Psychologists (n=183) and Hypnotherapists (n=119) responded. In terms of their experiences, Chartered Clinical Psychologists reported seeing more cases of satanic / ritualistic abuse compared to Hypnotherapists who, in turn, reported encountering more cases of childhood sexual abuse recovered for the first time in therapy, and more cases of suspected false memory. Chartered Clinical Psychologists were more likely to rate the essential accuracy of reports of satanic / ritualistic abuse as higher than Hypnotherapists. Belief in the accuracy of satanic / ritualistic abuse and Multiple Personality Disorder / Dissociative Identity Disorder reports correlated negatively with the belief that false memories were possible

On Streams and Incentives : A Synthesis of Individual and Collective Crowd Motion

We present a crowd simulation model that combines the advantages of agent-based and flow-based paradigms while only relying on local information. Our model can handle arbitrary and dynamically changing crowd densities, and it enables agents to gradually interpolate between individual and coordinated behavior. Our model can be used with any existing global path planning and local collision-avoidance method. We show that our model reduces the occurrence of deadlocks and yields visually convincing crowd behavior for high-density scenarios while maintaining individual agent behavior at lower densities

On Experimental Research in Sampling-based Motion Planning

In robotics research, it is often difficult to compare and evaluate techniques experimentally. This paper identifies these difficulties and provides solutions based on our work during the last four years in the field of sampling-based motion planning

Forgetting unwanted memories: Directed forgetting and thought suppression methods

Experimental psychopathologists have tested hypotheses regarding mechanisms that ought to be operative if victims possess skills for forgetting material related to trauma. In this article, we review research on directed forgetting and thought suppression paradigms, concentrating on laboratory studies involving attempts by individuals reporting trauma histories to forget emotionally negative material. Most studies have shown that trauma survivors, especially those with post-traumatic stress disorder, are characterized by a breakdown in the ability to forget disturbing material. Studies on individuals reporting repressed or recovered memories of trauma have not confirmed predictions regarding heightened forgetting skills for trauma-related words. However, recent research on suppressing disturbing autobiographical memories suggests that people who report spontaneously recalling childhood abuse outside of psychotherapy may, indeed, possess skills for not thinking about disturbing material

Reachability Analysis of Sampling Based Planners

The last decade, sampling based planners like the Probabilistic Roadmap Method have proved to be successful in solving complex motion planning problems. We give a reachability based analysis for these planners which leads to a better understanding of the success of the approach and enhancements of the techniques suggested. This also enables us to study the effect of using new local planners

Stealth-Based Path Planning using Corridor Maps

A relatively new area within the field of path planning deals with computing a stealthy path for a character moving in a virtual environment. Besides efficiently obtaining a path that is collision-free, short and smooth, the added difficulty is that the path must have little or no exposure to observers. We propose a new algorithm for computing such a path in the plane, and show that real-time performance can be achieved

Creating small roadmaps for solving motion planning problems

Abstract — In robot motion planning, many algorithms have been proposed that create a roadmap from which a path for a moving object can be extracted. These algorithms generally do not give guarantees on the quality of the roadmap, i.e. they do not promise that a path will always be found in the roadmap if one exists in the world. Furthermore, such roadmaps often become very large which can cause memory problems and high query times. We present a new efficient algorithm that creates small roadmaps for two- and three-dimensional problems. The algorithm ensures that a path is always found (if one exists) at a given resolution. These claims are verified on a broad range of environments. The results also give insight in the structure of covering roadmaps. Keywords—motion planning, small roadmaps, PRM I