Realistic Agent Movement in Dynamic Game Environments

Realistic and intelligent agent movement remains one of the greatest challenges for games developers. Path-finding strategies are usually employed as a means of allowing an agent to navigate from one part of the game world to another. Typically the game world is stored in a pre-processed structure called a map which contains all of the relevant geometry. In order to cut down the search space for the path-finder, this map is broken down and simplified. The path-finder then uses this simplified representation to determine the best path from the starting point to the desired destination. These simplified representations correspond to graphs, and algorithms such as Dijkstra and A* [6] can then be employed to quickly find paths between the nodes in the graph. The graph used is based on a pre-processed static representation of the game world. However, the assumption that the geometry of the game remains static during the course of play is not necessarily valid anymore. This difficulty is then compounded by the fact that the agent typically has no real-time awareness of the environment around it. This situation results in a number of problems for path-finders, each of which we now outline. The increasing use of physics engines opens up the possibility of completely dynamic game geometry, where the players and agents can physically alter the structure of the game world as play progresses, by knocking over walls for example [2]. Dynamic obstacles can therefore be introduced that block previously accessible nodes on the graph. When this happens the agent will still believe it can walk along this path due to its reliance on the preprocessed static graph. Techniques have been developed that improve the agents’ reactive abilities when dynamic objects obstruct a path. These work well in some situations, but generally the agent will not react until it has collided with an obstacle, as it has no sense of awareness until a trigger is set when a collision occurs. Another problem is the rigid and unrealistic movement that occurs when the agent walks in a straight line between nodes. This is caused by the dilemma which arises in the trade off between speed (the less number of nodes to search the better) and realistic movement (the more nodes, the more realistic the movement). This has been improved in some games by applying spline curves for smoothing out paths along nodes. A further problem is implementing tactical path-finding. This involves not just finding the shortest route but also the route that offers the most cover, or avoids unnecessary encounters with undesirable game entities. One approach is to modify the cost heuristic of A* to take line of fire from other enemy agents into account [7]. This has the benefits of adding realism to the game and also presents a less predictable opponent for the human player. The drawback is that due to the added cost, the search space becomes much larger for A* to process. This approach also assumes that the threat remains static during the paths duration, which is seldom the case. Generally game developers add in special case code to deal with these problems but typically this is only applicable to that particular game [1]. This paper examines these problems and introduces the concept of applying learning techniques to solve them in a new novel way [4]. Our solution to this problem is to provide the agent with a means of navigating its own way around the world, rather than simply relying on routes provided by the game engine. In order to accomplish this the agent requires two important abilities. Firstly it needs to be able to examine its environment in some way in order to know what is in front of it and around it, thus giving it real-time awareness. Secondly it needs some way of processing this information to accomplish tasks such as steering around obstacles that have been placed in its path. The first ability is achieved by embedding sensors in the agent. This is a concept borrowed from robotics where ultrasound or infrared sensors are common. We adapt this idea for our agents by casting rays which test for intersections with the game geometry. In this way information can be provided to the agent pertaining to the proximity of objects within its field of vision. The second ability is being able to process this information in some way. Our solution to this problem is to furnish each agent with an Artificial Neural Network (ANN) [3] which takes the sensor information as input. The ANN is a learning algorithm that we have trained to exhibit the behaviour we want – namely that the agent has the ability to steer around objects. We describe how this provides robust steering behaviour that is tolerant of noisy data. Another advantage of this approach is that the processing required is minimal and hence multiple agents can be imbued with this behaviour without causing a major strain on the CPU. This is used in conjunction with a traditional path-finding algorithm. The algorithm works out a path for the agent but the sensors and the ANN are responsible for moving the agent along that path, and are capable of adapting the path to steer around obstacles or other dynamically introduced geometric changes. Our system is implemented using the Quake 2 [5] game engine and we have extensively tested these ideas against more traditional approaches to path-finding. The game engine gives us a test bed whereby a genetic algorithm [6] is used in real-time to evolve the weights of the neural network. Since the sensors are influencing the agents movement in real-time, as it walks from node to node, it tends to gradually veer away from obstacles thus resulting in less rigid movement. Giving the agent this real-time awareness also compliments the tactical elements of pathfinding as the agent can be alerted in real-time to imminent threats. Our results indicate that this approach is extremely useful in the dynamic environments that are becoming the norm in modern computer games. References [1] Cain, Timothy, "Practical Optimizations for A*", AI Game Programming Wisdom, Charles River Media, 2002 [2] Eberly,David,H, "Game Physics", Elsevier, Inc, 2004 [3] Fausett, Laurene, "Fundamentals of Neural Networks Architectures, Algorithms, and Applications", Prentice-Hall, Inc, 1994. [4] Graham, Ross,"Neural Networks for Real-time Pathfinding in Computer Games", In proceedings of ITB journal, Issue 9 (2004) [5] www.idsoftware.com/games/quake/quake2/ [6] Russel, Stuart., Norvig, Peter., "Artificial Intelligence A Modern Approach", Prentice-Hall, Inc, 1995 [8] Van der Sterren, William,. "Tactical Path-Finding with A*", Game Programming Gems 3, Charles River Media, 200

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

The role of oblivion, memory size and spatial separation in dynamic language games

In this paper we present some multiagent simulations in which the individuals try to reach a uniform vocabulary to name spatial movements. Each agent has initially a random vocabulary that can be modified by means of interactions with the other agents. As the objective is to name movements, the topic of conversation is chosen by moving. Each agent can remember a finite number of words per movement, with certain strength. We show the importance of the forgetting process and memory size in these simulations, discuss the effect of the number of agents on the time to agree and present a few experiments where the evolution of vocabularies takes place in a divided range.This paper has been sponsored by the Spanish Interdepartmental Commission of Science and Technology (CICYT), project numbers TEL1999-0181, and TIC 2001-0685-C02-01

Agent Street: An Environment for Exploring Agent-Based Models in Second Life

Urban models can be seen on a continuum between iconic and symbolic. Generally speaking, iconic models are physical versions of the real world at some scaled down representation, while symbolic models represent the system in terms of the way they function replacing the physical or material system by some logical and/or mathematical formulae. Traditionally iconic and symbolic models were distinct classes of model but due to the rise of digital computing the distinction between the two is becoming blurred, with symbolic models being embedded into iconic models. However, such models tend to be single user. This paper demonstrates how 3D symbolic models in the form of agent-based simulations can be embedded into iconic models using the multi-user virtual world of Second Life. Furthermore, the paper demonstrates Second Life\'s potential for social science simulation. To demonstrate this, we first introduce Second Life and provide two exemplar models; Conway\'s Game of Life, and Schelling\'s Segregation Model which highlight how symbolic models can be viewed in an iconic environment. We then present a simple pedestrian evacuation model which merges the iconic and symbolic together and extends the model to directly incorporate avatars and agents in the same environment illustrating how \'real\' participants can influence simulation outcomes. Such examples demonstrate the potential for creating highly visual, immersive, interactive agent-based models for social scientists in multi-user real time virtual worlds. The paper concludes with some final comments on problems with representing models in current virtual worlds and future avenues of research.Agent-Based Modelling, Pedestrian Evacuation, Segregation, Virtual Worlds, Second Life

Agent Behaviour Simulator (ABS):a platform for urban behaviour development

Computer Graphics have become important for many applicationsand the quality of the produced images have greatly improved. Oneof the interesting remaining problems is the representation of densedynamic environments such as populated cities. Although recentlywe saw some successfulwork on the rendering such environments,the real?time simulation of virtual cities populated by thousands ofintelligent animated agents is still very challenging.In this paperwe describe a platformthat aims to accelerate the developmentof agent behaviours. The platform makes it easy to enterlocal rules and callbacks which govern the individual behaviours.It automatically performs the routine tasks such as collision detectionallowing the user to concentrate on defining the more involvedtasks. The platform is based on a 2D-grid with a four-layered structure.The two first layers are used to compute the collision detectionagainst the environment and other agents and the last two are usedfor more complex behaviours.A set of visualisation tools is incorporated that allows the testingof the real?time simulation. The choices made for the visualisationallow the user to better understand the way agents move inside theworld and how they take decisions, so that the user can evaluate ifit simulates the expected behaviour.Experimentation with the system has shown that behaviours inenvironments with thousands of agents can be developed and visualisedin effortlessly