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A Survival Guide during Fire Outbreak inside a Cinema


                                                    Hsin-Huei Chen...
only go around the obstacles instead of walking across     3.1 Autonomous Model
them.
                                    ...
is the next moving position of each agent at next time
                                                            step.

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Different crowd behaviors were simulated based on           Acknowledgements
different agents’ properties and some interes...
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Aritificial Life Term Project

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Transcript of "Aritificial Life Term Project"

  1. 1. A Survival Guide during Fire Outbreak inside a Cinema Hsin-Huei Cheng Instructor : Demetri Terzopoulos University of California at Los Angeles Abstract A crowd behavior simulation is performed in a situation where fire outbreak occurs inside a cinema. Each individual agent inside the simulation will decide their own escape pathways based on their local perception of the environments (i.e., agents and obstacles). It is found that agents who do not interact with their local environment (i.e. people that are stayed clam) will escape more efficiently than those who interact with other agents. This model allows for the dynamic simulation of different crowd behaviors in emergent situations, which will be useful for survival guide design. 1. Introduction “Oh My God! There’s a fire!”. Imagine when you are This paper is organized as follow. The design of a inside a cinema watching a famous movie, but virtual environment model is discussed in Section 2. suddenly the fire alarm rings, what would you do? In addition, the details of the autonomous model and Would you cry, run or wait to get rescued? Everyone the different variations will be presented in Section 3. behaves differently in facing an emergency situation. The implementation methods will be discussed in People’s reactions ultimately influence the overall Section 4, followed by results and discussions in human survival rate in the incident. In the occasion of Section 5. Finally, the conclusions and the future work fire outbreak in an enclosed area, people were found will be provided in Section 6. killed by stepping on each other, instead of being killed by the fire. Many human and environmental factors are 2. Virtual Environmental Model coupled together that will affect the final outcomes. The most critical factor is the personality of In real life, people interact with local their individuals. Other factors like the environment, the environments in deciding the escaping pathway. physical health situation, the time of making decision, Similarly, in the simulation, each agent interacts with and crowd’s behavior etc. also play important roles. their local environment in order to decide the best escaping route. Therefore, the design of the virtual In this project, I simulate the real time dynamics of environment is crucial for the correct implementation people movement inside a cinema in response to a fire of the simulation. alarm. Each person is an autonomous agent. They can interact with each others, as well as the static obstacles To begin with, the virtual cinema environment will inside the cinema. The agents make decision for their be briefly introduced. The plain view of the cinema local pathways, and this information is collected in a layout is shown in Figure 1. As shown in the figure, central decision maker at each time step to implement the grids in pink color represent the exits. Each agent the agents’ motion. In particular, each agent was selects the nearest exit at each time step, which is randomly assigned with different properties in order to determined by the Euclidean distance between the exit simulate different crowd behaviors. and the agent. The seats are illustrated in blue girds. These seats are stationary obstacles, where agents can
  2. 2. only go around the obstacles instead of walking across 3.1 Autonomous Model them. The agents were represented by circular-shaped The virtual environment model consists of a objects in the simulation. Agents with different stationary map, a mobile map, and a path map (Figure properties are represented with different colors. The 2). The stationary map records the grid locations properties of the agents are summarized as follow: where the obstacles (i.e. seats) occupy. This map is stationary with respect to time. For dynamic moving - Physical factors: different walking speeds agents, the mobile map stores the information for represent different physical conditions occupied cells in real time. Agents that are within - Mental factors: selfishness and helpfulness specific cell are recorded in the “occupied agent list”. - Group factor: belongs to a group or alone The dynamic map is updated at each time step. Both stationary and mobile maps are necessary input Each agent possesses three different states. The initial information for the central decision maker to state is indicated as “sitting” state. When the agents implement the paths. The implementation will be are alerted by the fire alarm, their states change from stored in the path map. the “sitting” state to the “running” state. After a defined time period, if the agents are trapped inside the cinema, then their states will turn into the “dead” state, which implies that the agents are killed by the fire. For those who have successfully reached the exits, their states will turn into “alive” states. A summary of state changes is illustrated in Figure 3. Figure 1. The plain view of cinema. Figure 3. State change of an agent 3.2 Autonomous Behavior Model The general behavior of agents can be classified as below (Figure 4). A. helpful agents meet helpful agents: the slower agent has the priority to move to the next grid B. selfish agents meet helpful agents: selfish Figure 2. Upper left: stationary map; upper right: ones have the priority to move to the next grid mobile map; lower: path map C. selfish agents meet selfish agents: one of them will be given the priority to move D. group members meet other individuals: group members have the higher priority if the group 3. Autonomous Agent Behavior is already in the process of moving. Group members cannot be separated. The dynamic agents are the major character in the simulation. Here, I will first introduce the properties of the agents and then describe the behavior models based on these properties.
  3. 3. is the next moving position of each agent at next time step. Since all the agents movements have to be coordinated at the same time, a path decision maker is required to coordinate the movement. This is achieved (a) (b) (c) (d) by the function “confirmPath”. For each grid on the path map, “confirmPath” checks the occupied status. Figure 4. (a) H1 has lower moving speed so it has the There are some complicated rules for the priority to move. (b) S1 has the priority to move. (c) implementation, which are outlined as follow: Either S1 or S2 has the priority. (d) G1 has the priority to move if one of the group members (e.g. G0) first A. A grid can only be occupied by one agent p passed by the grid and “confirmPath” approves p’s next moving position if - p does not belong to any group 3.3 Sensing Area - p belongs to a moving group and the previous member just walked through the grid In reality, people can only sense their local - p belongs to a group and is the leader of the environment to make a decision. In the virtual group environment, each agent is provided with the ability to sense its local neighborhood, in order to decide the B. in the situation where the grid is going to be best moving direction. For instance, if the number of occupied by more than one agent neighbors of an agent is less than that of its right hand - compare each candidate’s properties and side, the agent will choose the direction where there follow the rules described in Section 4.2 to are fewer neighbors (i.e. higher chance to escape find the “winner” who can occupy the faster). With this concept of neighborhood sensing, grid at the next time step. The agents who are each agent updates a sensing list which records the “lost” in the comparison will remain at the occupied cells within a specified area of interest at same position. each time step. An example of the sensing area is shown in Figure 5. Following the method, agents can progressively reach the nearest goal (i.e., the exit). Figure 5. The 2 tier sensing area of agent p. The light blue area represents the 1st tier, whereas the dark blue region represents the 2nd tier. Figure 6. The pseudo codes of the method. 4. Method Description An outline of the methodology used for computing a 5. Results and Discussion path for an agent is provided in Figure 6. The function “predictNext” is to find the optimal intermediate In this section, I will illustrate some agent behaviors positions (i.e. sub-goals) toward the final goal. The based on the behavior model mentioned in Section function refers to the stationary map, as well as the 2- 4.2. These demonstrations are displayed on the tier sensing list to find the next direction that has a websites at higher escape probability. The output of this function http://web.me.com/stella0107/stella/CS275_AL.html.
  4. 4. Different crowd behaviors were simulated based on Acknowledgements different agents’ properties and some interesting phenomena were observed. For instance, Demo 3 and I would like to thank Prof. Terzopoulos’s lectures that 4 demonstrate a situation where agents located on the let me understand the beauty of Artificial Life. I left hand side of the cinema do not interact with other would also like to thank the instructor Prof. agents, therefore they escape to the exit based on the Terzopoulos, as well as all the people who were shortest pathway; while agents located on the right involved in the development of the simulator for side are programmed to interact with other agents, CS174/274C. Special thanks to Professor Faloutsos therefore a longer decision making process is required and the teaching assistant Gabriele Nataneli. for individual agent to execute their pathway. This is reflected from the simulated behavior model, where some agents become hesitate to decide which way to References go. They interact with the dynamic environment in real time and cannot find a good solution immediately. [1] “Composite Agents”, Hengchin Yeh, Sean Curtis, When people meet an emergency, they often become Sachin Patil, Jur van den Berg,Dinesh Manocha, too afraid or anxious to make quick and reasonable Ming Lin, SCA2008. decision. [2] "Environmental Modeling for Autonomous In this project, I did not perform some of the Virtual Pedestrians," W. Shao, D. Terzopoulos, interesting features that would make the simulation SAE 2005 Transactions Journal of Passenger more realistic, such as collision avoidance and global Cars: Electronic and Electrical Systems, 114(7), path planning. In addition, one problem I want to February, 2006, 735–742. Compilation of the address is the grid based data structure. The grid size I most outstanding SAE technical papers of 2005. used for the stationary, the mobile, and the path maps are all the same to make the computation easier and [3] "Artificial fishes: Physics, locomotion, perception, faster. However, this leaded to a situation where some behavior," X. Tu, D. Terzopoulos, Proc. ACM agents will get stuck on the boundary of the grid SIGGRAPH'94 Conference, Orlando, FL, July, without further movement. Owing to this limitation, 1994, in Computer Graphics Proceedings, Annual the statistics of the crowd behaviors were not carried Conference Series, 1994, 43–50. out. [4] "Artificial life for computer graphics," D. 6. Conclusion and Future Work Terzopoulos, Communications of the ACM, 42(8), August, 1999, 32–42. Crowd behavior simulation is a very interesting topic, especially when the simulation is coupled with the [5] “Steering Behaviors For Autonomous complex personalities of individual agents. In this Characters”, Craig W. Reynolds, Sony Computer project, I have demonstrated a crowd behavior model Entertainment America. based on individual agents’ properties, where some interesting observations were found. Changing the behavior models will lead to different outcomes that may be outside our imagination. The results presented in this report are instructive to educate people how to escape efficiently from an emergency situation, such as fire outbreak inside a cinema. Future work includes the optimization of the data structures, as well as the behavior models. The project is a small-scale simulation model with a few agents at the moment. The ultimate goal is to design a large- scale simulation that can realistically simulate the real life situation.

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