Intelligent Path Finding for Avatars in Massively Multiplayer Online Games

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1 Intelligent Path Finding for Avatars in Massively Multiplayer Online Games Dewan Tanvir Ahmed, Shervin Shirmohammadi Distributed and Collaborative Virtual Environments Research Laboratory School of Information Technology and Engineering University of Ottawa, Ontario, Canada E mail: {dahmed, shervin}@discover.uottawa.ca Abstract Path finding is an essential feature of Massively Multiplayer Online Games (MMOG). In this paper, we propose a path finding algorithm for peer to peer online games. The proposed approach intelligently discovers a near optimal but cost effective path between any two points of a connected virtual world. The search space is reduced by the use of artificial anchor points at each zone. As a result, computational cost and memory requirement for the system could be relaxed. Moreover, active peers participate in path discovering process that reduces load on the server. In order to avoid repeated computation of a path, many sub paths are smartly reused at each zone whenever possible. Considering its advantages, it is a good solution for path finding in MMOGs. Keywords Computational intelligence, MMOG, online games, path finding, peer to peer, performance I. INTRODUCTION Entertaining applications like online games are becoming increasing popular. Massively Multiplayer Online Game (MMOG) is a kind of computer games where hundreds of thousands player interact in realtime. Interestingly, the most valuable Internet Company in China is MMOG, clicking in at US$2.0B in market cap. Once considered a small business, the business prospects of multi player gaming are increasing. It has enviable business hope for many reasons like recurring revenues, competitive moats, network effect, real competition, and time engaged. The big criticism of MMOGs is that they are "hit" businesses like Hollywood businesses as the average life of a successful MMOG is over five years. However, MMOGs are becoming actual business places, with real money spent on virtual items and virtual properties. Commercial networked virtual environments practice client server architecture which is resilient in many cases but expensive to deploy and maintain. For example, SL (Second Life) has approximately 5000 servers for its virtual world. As an alternative, peer to peer architecture has some advantages like scalability and cost effectiveness, but considering pure control for business, it is not an acceptable solution. As a consequence, many new hybrid architectures have been proposed. It is true that online games over peer to peer or hybrid peer topeer framework are effective in many ways like deployment cost, performance through reduced latencies. However, the capacity is bounded by at least two technical limitations: network bandwidth and processing power [1] [2]. In a game, an avatar moves over a virtual world either in a controlled way or in an automatic way (uncontrolled) by clicking the destination point with the help of an algorithm. For uncontrolled movements, a path discovering procedure works in the background and gives the result that will be followed by the avatar. This type of procedure is called path finding. For many type of games like real time strategy (RTS) and role playing games (RPG), real time path finding is a big problem to overcome, because path finding is an expensive task that consumes significant amount of computing resources like CPU cycle and memory. Since the work is on MMOG over hybrid peer to peer architecture, the path finding here can be improved through peer participation and computational intelligence. In this paper, we present a path finding algorithm for such a framework. The key objective here is to reduce computation cost and memory requirement with the active participation of peers. For path finding, usually a superimposed grid over a virtual world is considered. Rectangular tiles are most common as in use in Ages of Empire, Alpha Centauri, Baldur s Gate, and The Sims. For each step over a tile, there is a cost. The problem is complicated because of obstacles as non playing objects in the virtual world. The two well practiced path finding algorithms are [3] and

2 (Iterative Deepening ) [4]. avoids memory overhead of A* at the cost of a slower search. Considering peer to peer MMOG feature, we propose a path finding algorithm that can reuse already computed sub paths on its way to a destination. The search space is reduced through the use of anchor points at each zone. The paper is organized in sections each covering a particular aspect. The problem is formulated in Section II. The proposed path finding algorithm is given in Section III In section IV, we discuss it pros and cons with some simulated data. Finally, the paper is concluded in Section V. II. PROBLEM FORMULATION Path finding requires significant amount of resources especially in movement intensive games like MMOG. So, an efficient and inexpensive approach is needed, otherwise the performance of the MMOG may degrade. In MMOG, most of the times a player moves by explicit controls over the avatar. But as a feature, a player can also move to a destination usually by mouse click. For this purpose, it runs an algorithm in background that determines the path to follow. Considering the map and inscribed features inherent to a virtual world, it is an expensive operation. A near optimal path is good enough provided that it is computationally inexpensive both in terms of time and memory. be. Then within each zone grid level transitions, gate to gate, path will be solved. The objective here is to discover the automatic path for a given source and a destination point with minimum computational cost. III. THE PROPOSED SOLUTION At a first glance, the problem resembles to the shortest path algorithm. It would be nice if we could. But the information available for this problem makes it harder to form a ready problem for shortest path case. The difficulties are: 1. To formulate the problem, we consider each room as a node and each door as an edge between the adjacent rooms. The next step is to put weight on edges. But, the distance between any two rooms/nodes is not unique with respect to avatar s position within a room. 2. There could be obstacles inside a room/zone like shelf, tables, chairs, trees etc. So, it is not easy to find quick and short path. Thus to overcome the constraints while discovering the path between two points some artificial intelligence might be helpful. In this paper, we explore one possibility and figure out its pros and cons. A. Dealing with obstacles Let each room is composed of many small sized squares, i.e. grid. A square is marked if any object stays on top of it. Thus, if it is unmarked a player can run or walk over it as shown in Figure 2. Figure 2. Space decompostiopn into girds to handle obstacles Figure 1. Path finding in MMOG To illustrate the problem consider the Figure 1. A player would like to go from the source Point S to the destination Point D. So, the zone level path could As a first step to discover a path, we find the shortest path for any two points inside a zone/room. For a particular grid, a player can go to one of the four adjacent grids (most of the time) if we allow left, right, top, and bottom steps. But technically, there are eight

3 neighbor grids leading to more possibilities. Four way approach could be cheaper but less optimal whereas eight way approach is better but expensive because of higher branching factor. Considering computational overhead and memory requirement, we use four way approach to discover a path. Figure 3. Two possibilities for stepping forward (a) Fourway (b) Eight way To find a path inside a zone from a point A to another point B, we use, sounds A star, search. It finds the least cost path from a given initial node to one goal node. It is a best find graph search algorithm. It incrementally searches all routes starting from (the initial point) until it finds the shortest path to the destination (the goal). Like to all informed search algorithms, it searches first the routes that appear to be most likely leading towards the goal. And the searching process continues until it reaches the goal. The algorithm explores many paths from start to goal. For each traversed grid location, it maintains three values: : the actual shortest distance from start point A to current location n : the estimated distance from current location to goal : the sum of and At each step, the algorithm explores a new isle/branch that has minimum cost. In, the heuristic function must be admissible heuristic known as optimistic heuristic. Say is a node and is a heuristic function. Let, : Cost indicated by to reach a goal from : The actual cost to reach a goal from So, the function is admissible if, It means a heuristic is admissible if it never overestimates the cost to reach the goal. In our case, is defined by the sum of absolute difference of both grid coordinates, i.e.. So, using search we can find the optimal path inside a zone/room between any two given points. The time complexity of depends on the heuristic function. In the worst case, the number of nodes expanded in the direction of goal is exponential. But it could be polynomial provided that the admissible heuristic always gives true distance from a point to the goal. The big limitation of search is its memory requirement as in the worst case it needs remembering an exponential number of points or nodes. So, this is not an acceptable solution just to support a feature of MMOG. B. Anchor point and P2P sub path sharing The complexity of would be high for a big zone/room. The memory requirement is also a big issue. To overcome such problems, some anchor points will be chosen in each zone. This is predefined for each zone. These anchor points can work as sub goals while discovering a path. The number of anchor points is preset which is proportional to the zone size. For each anchor point and each gateway of that zone (i.e. door or inlet/outlet), a path is determined. This path will be shared among the players stayed in the room in peer to peer way. Figure 4. Anchor point for sub path lookup Consider Figure 4 which has three anchor points. Now assume that, there are known paths (explained later) from these anchor points to the gateways of a zone. So when a player needs to discover a path to get out of the zone, it needs only to find a sub path to an anchor point; usually the closer one based on Euclidean distance. Let defines the path to the anchor. So, the path to a gateway ( ) is where is the sub path

4 from that anchor point to the gateway. So, the overall path might not be the optimal one. But it largely drops the computation cost as the search space has been cut down. C. Peer Role in Path Finding We have investigated the feasibility of performing MMOG functionalities over a hybrid architecture. The system is hybrid as both server and client side resources are integrated for the application. For path finding in MMOGs, we consider peer participation to reduce server side load. When a player enters a room it becomes the member of that group. This group is called area of interest (AoI). As a game rule, they share game states among them. Significant research has been conducted in this regard [5] [6] [7] [8]. Any peer to peer MMOG framework is good enough for this purpose. Algorithm Peer Role in Path Finding(new Peer, Server, Zone ID) Input g: The total number of gateways of the zone A: The total number of anchor points of the zone begin Peer list = Discover Peer List(Server, Zone ID) if 1 then else sub path =, 2 sub path = end if Stored path = Get already computed sub paths from one of the peers if (sub path > stored path ) then end end Let is un stored [s, d] = Source Destination( ) Stored path = stored path Discover a sub path(, ) Figure 5. Sub path finding through peer participation According to the algorithm given in Figure 5, the new peer first collects peer list from the server. It then computes the number of sub paths of that zone considering the number of gateways and the number of anchor points. After collecting already computed subpaths, it then discovers another sub path if there is an un stored sub path. So, as a volunteer each peer might compute a sub path if needed. D. Path Finding In Massively multiplayer online games (MMOG) hundreds of thousands player interact in a virtual world. Generally, an MMOG may have millions of subscribers (World of Warcraft, Quest). As a consequence, the virtual world is divided into realms, which are the clones of the same virtual world, each hosting several thousand registered players. Technically, the realms are then geographically distributed across the world. Thus, players from a particular region are generally involved in the same realm. Thus, to accommodate many subscribers, companies provide many realms as needed. Realms are further divided into separate areas. Each area is considered as a zone. For path finding, we consider all connected zones/rooms as a component. Each component is thus processed independently. For example, Figure 1 (given earlier) is a connected component where a player can go anywhere within the component though the gateways. Figure 6. (a) The graph of a virtual world (b) The graph with anchor points at the source and destination zones It is true that different path finding algorithms like or can be applied to discover optimal path within the entire component. But they introduce big computational overhead and also require huge memory. Thus, we first figure out zone level path, and then within each zone we determine grid level path. Considering each zone as a vertex and each gateway as an edge, a graph, is formed. The virtual world shown in Figure 1 has converted to a graph (Figure 6a) according to above description. The anchor points of the source and destination zones are added to trim down computation cost. This is because of two distinct points at source and destination zones. For example, a player is moving from Zone to Zone and the number of

5 anchor points at and is three and two, respectively. So, the resultant graph resembles to Figure 6b. The problem here is to put weight on edges. As the layout of a virtual world is predefined, it is not hard to give rough weight on edges from a gateway to another gateway (i.e. door to door). So, we run a short path algorithm, in fact Dijkstra s algorithm, from zone to zone. It gives a path in zone wise format that needs to be followed like according to Figure 6a. It should be noted that a player does not know the exact path until it is explored. A player seeking a path starts exploration from the source zone. The anchor point closer to the next zone is its current target. From this anchor point, it goes to the gateway of the next zone and becomes the member of that zone. At all intermediate zones, a player only needs to follow the gateway to gateway segment. At each subpath, if it is already discovered by some earlier peers, it simply follows that grid level path. Otherwise, it discovers the grid level sub path and shares it with the peers of that zone for future reuse. At the destination zone, it goes from the gateway to an anchor point that is closer to the target point and finally to the target point. one gateway at 1, 45. We do not need to check doorto door performance as it is static. We compared pure against with anchor approach for the above settings. It is quite obvious that would perform better against with anchor approach as it explores more states to find the optimal path. For 52 different path finding requests, path cost is shown in Figure 7 for both approaches. But in terms of number of states explored, took many states to converge whereas with anchor approach presented here quickly finds the goal as shown in Figure 8. Figure 8. Comparision of and with anchor points in a zone in terms of states explored Just to summarize, average path cost and average states explored by both approaches are shown in Table 1. It shows that we can find quite a good path just exploring very few states compared to algorithm. Thus it could be a viable solution for peer to peer MMOGs. Figure 7. Comparision of and with anchor points in a zone in terms of path cost IV. ANALYSIS AND PERFORMANCE The performance of the proposed path finding approach was tested through a small scale simulation. For this purpose, we considered a zone with square grid. We strategically placed 4 anchor points at 12, 12, 12, 37, 37, 12 and 37, 37. We had only Table 1. Numerical comparison of A* and A* with anchor in a zone Algorithm Avg. cost Avg. states explored with anchor V. CONCLUSION Path finding is an important problem in many applications like military simulations, computer games, and robotics. In this paper, we have proposed a path finding algorithm for peer to peer architectures. The proposed approach intelligently discovers a near optimal

6 but cost effective path within a connected component of a virtual world. The fundamental problems are computational cost and memory requirement. Through the use of artificial anchor points, the search space has been reduced. As a result, computational cost and memory requirement for the system could be dropped. In addition, as the participating peers help in path discovering process, load on server drops significantly. To avoid repeated computation of a path segment, many sub paths are reused intelligently at each zone whenever possible. Considering all of its features and advantages, it is a rational solution for path finding in peer to peer MMOGs. (NOSSDAV)), 2005, pp VI. REFERENCES [1] S. Singhal and M. Zyda, Networked Virtual Environments: Design and Implementation. New York: SIGGRAPH Series. Addison-Wesley and ACM Press, [2] J. Smed, T. Kaukoranta, and H. Hakonen, "Aspects of networking in multiplayer computer games," in International Conference on Application and Development of Computer Games in the 21st Century, 2001, pp [3] P. Hart, N. Nilsson, and B. Raphael, "A Formal Basis for the Heuristic Determination of Minimum Cost Paths," IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp , July [4] R. E. Korf, "Depth-first iterative-deepening: An optimal admissible tree search," Artificial Intelligence, vol. 27, no. 1, pp , [5] D. T. Ahmed and S. Shirmohammadi, "A dynamic area of interest management and collaboration model for P2P MMOGs," in IEEE International Symposium on Distributed Simulation and Real Time Applications, [6] T. Iimura, H. Hazeyama, and Y. Kadobayashi, "Zoned federation of game servers: a peer-to-peer approach to scalable multi-player online games," in ACM SIGCOMM workshop on Network and system support for games, 2004, p [7] S.-Y. Hu, J.-F. Chen, and T.-H. Chen, "VON: a scalable peer-to-peer network for virtual environments," IEEE Network, vol. 20, no. 4, pp , [8] A. P. Yu and S. T. Vuong, "MOPAR: a mobile peerto-peer overlay architecture for interest management of massively multiplayer online games," in international workshop on Network and operating systems support for digital audio and video