Discuss Berger’s ‘focal points model’ in terms of its components,

Question Discuss Berger’s ‘focal points model’ in terms of its components, their relation to one another, and its usefulness.

Summary

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Question 1. IntroductionThe final goal of this project is to produce a crowd

1. IntroductionThe final goal of this project is to produce a crowd simulation software to evaluate real world crowd behaviour in library. Therefore, the project must give user ability to visualise the agent walking inside the library.Unity3D is a game production software design for games which offer a hierarchical integrated development environment, visual editing, detailed property editor and dynamic game preview. As Unity offer primary scripting API in C#, this makes JAVA developer easier to get started. Unity editor provide drag and drop function which is easy to operate and understand.Our team first intend to use the unity platform to simulate the library and make the project have board space for development.Since crowd simulation system covering a wide range of ideas. The author narrowed down several topics that might be studied, and finally decided to develop a Agent-based crowd system using the Templeman library as the library model. This dissertation will evaluate the author’s contribution to this project. Including：- Simulation Architecture- Scenes Design- AI behaviour systems- Integrating functions- Systems performance evaluation1.1 Architecture of SimulationCrowds simulation involves many components including agent behaviour, agent logic modelling, crowd movement and animation/rendering. We need a platform to apply those features, such as make real-time path calculation, support multiple path planning algorithms. Provide user good visual effect with broad space of development and free to use.After many investigation and research, we found Unity3D has meet the requirement, as the user amount of Unity platform has raised sharply these years. We intend to use this platform to develop our simulation project. 1.2 ObjectivesThe purpose of this project is to provide a direct experience with developing a goal-driven Unity3D Crowd simulation software — simulate people movements and behaviours inside a virtual building. We divide this project into 2 main stage (can be found in the work logs) with Minimum viable product as prototype and additional functionality that can extend the project content. Some of the section might have overlap with other group members, as the normal behaviour and emergency behaviour are design by different people.The author’s Main Objectives are:- Agent behave like a normal person, Including, Agent normal behaviour inside library and switch of situation, Agents behaviour before emergency happens. Agent reach their goal and complete action successfully. – Agent behaviour systems (Not including evacuation sections) meets the requirement. Including agent path planning in normal situation, logic design, Individual collision avoidance, agent exits.- Combination with other group members section. Ensure that the final systems without big bugs. Including, code integration, fix bugs, scene’s render. – The Final software meet all the run-time update requirements. 1.3 Chapter OverviewChapter 2: A methodology research and literature review about the agent behaviour design, covered all area that author has contribute to the project.Chapter 3: Describe the design and implementation of components with figure and UML diagram. Interpretation of the development and optimization of components. Chapter 4: Explain the idea and steps of project testing, with set of evidence.Chapter 5: Evaluate the degree of completion to the initial project objectives, and the performance of observe from the testing.Chapter 6: Summarize the work done and the progress achieved.Chapter 7: Suggest some method to improve the performance, for future develop. 2. Methodology2.1 Crowd simulation architecturesThe Crowd simulation is aim to summarized the uncertainty of the crowd. To produce a crowd simulation, we need to firstly understand the crowd simulation systems, there are two main type of crowd simulation systems: Macroscopic and microscopic. Macroscopic models focus on the big systems. Microscopic models study the behaviour and decisions of individual pedestrians and their interaction with other pedestrians in the crowd. Macroscopic model makes the population as a model object, regardless of interaction between individuals (Fluid mechanics model). Microscopic models describe the detailed behaviour of pedestrians. Microscopic model can be further divided into Continuous type and Discrete type.2.1.1 AI Crowd DynamicsThe behaviour of real-world crowd has two important aspects that guide the motion of real people: – Goal Seeking: Finding the destination of each action.- Least-Effort strategy: Tendency of people to reach the destination along a ‘least-effort path’.According to the two aspects, people traveling along a smooth trajectory, as this require less steps to reach the destinations. Agent simulation requires the agent’s destination, agent’s speed, and collision avoidance between agents, since those factors can affect the ‘least effort’.The concept of personal space can be also considered as an important factor when simulating crowds’ dynamics. The personal space can be thought of as an invisible circle surrounding each agent, which should not be enter by others. The size of this area is depending on serval factor, such as environment, situations, roles etc. the personal space decide the minimum distance that should be maintained among agents. 2.1.2 Agent-based modelMost of today’s crowd simulation algorithms are make crowd as one (Flow-based Approach) then use algorithms to control the individual movement. They produce a crowd logic control system then divide into agent movement section. Agent-based model simulate crowd from low level (individual agent) to high level (group of agents).Agent-based model is a kind of microscopic model that predict complex phenomena by simulating simultaneous actions and interactions of multiple agents. Pedestrian decision-making and action patterns with agent-based model are more in line with human behaviour. The key to this model is that simple behavioural rules can produce complex behavioural outcomes. Agents have been given a degree of intelligence and goal-oriented, their action are depending on their individual rule, which gives more customisability.2.1.3 Design factors review- Crowd: group of peopleRefers to the number of individuals that the system can simulate in real time.Some systems focus on simulating realistic human behaviour within a crowd. They usually work with smaller groups and study the interaction both between individuals and with the environment.- Goal: DestinationRefers to whether individuals have one main task, such as walk toward one exit, or whether they have several routes to follow. Systems that focus more on individual behaviours may also have some sub-goals within the simulation, such as going through via points, helping others to an exits or to performing some specific actions- Roles: Leader/policeSimulating realistic human behaviour within a crowd implement microscopic approaches where individuals have different decision. Those characteristics are generally given by a set of parameters whose values are assigned in a probabilistic manner or by the scenario author.- Reaction Trigger: Interaction between individuals and environment.Some systems using alarms to start the evacuation.- Behaviour method: directional movement choicesBased on few numbers of techniques: rule-based models, physical models, cellular automata and finite state machines.- Environmental types: SurroundingFor example: home, station, ship2.2 Graph Theory and Exist Path Planning Algorithms 2.2.1 Graph TheoryThe Graphs in this context refers to a collection of nodes and edges which can be considered as many nodes representing locations and edges as paths connecting them. A graph can be undirected that means the paths between nodes can be traversed in both directions or directed, in which case the paths are one way. Both nodes and edges can have associated values. These values could be the distance between nodes or the time it takes to complete an action. The individual working inside the graph cost such value to complete an action.In order to work with a graph there needs to be an efficient method of searching through the node. There are two basic high-level algorithms for searching through nodes.The first is called a breadth-first search: The Agent marks its current location as 1, then it numbers all adjacent nodes as 2 etc, until the destination node has been reached. It then counts backwards in ones to the start to find a path to the goal.Breadth first search examines all possible nodes in the graph to find the best path but due to its thoroughness it’s time consuming and not optimal The second is called a depth first search which is much simpler and quicker, but less effective. Instead of finding the best path, it finds a path beginning at the agent position an adjacent node is found a numbered and then an adjacent node to this is found and numbered. This continues until a dead end is reached at which time we return to the last node where there was another direction to try and head off in that direction. when the destination is found the nodes are then counted backwards again to give the path.2.2.2 A* algorithmsBasic pathfinding problem is to generate a continuous path that connect between a start point S and a goal point G, while avoiding collision with known obstacles. Around the proposition of path finding, some algorithms have been generated.A* is one of the most popular algorithms use to find a path. A* is a directed breadth-first search and combines the advantages of uniform-cost and greedy searches using a fitness function: f (n)=g(n) h(n);where g(n)denotes the accumulated cost from the start node to node n and h(n) is a heuristic estimation of the remaining cost to get from node n to the goal node. The section below is an example of A* Operation.Example of A* OperationFirst, all the nodes in the graph are numbered row by column. We then create two lists, an open list and a closed list. These will keep track of the nodes that we visited and persist. Each node is given full values that are used in calculating the path. An estimate value(E) which is the cost for agent get to the destination will then be calculated, this can be measure in linear or any other way. Another second is the movement cost. This value is essentially the utility in moving to the node from another node which could be based on the actual distance from one node to another. we then store a calculation that adds together the two values and store the Nodes parent which is the closest node along the path. Start by placing the starting node into the closed list, since the starting node have been already visited, all the nearby node that aren’t blocked or in the closed list will be add into the open list. Then calculate the estimated cost(E) for each of the node inside the open list, and look at the movement cost to calculate the sum. While we’re doing this calculation, all the neighbours are linked back to the current node as their starting node repeat the process, node their parent we will need to examine in the open list we now get the node in the open list with the lowest sum. This node becomes a new location to start searching the neighbours again the new location node is added to. The process has been repeated until it reaches one node away from the destination, agent will have a link of parents all the way back to the start point, with minimum cost.2.2.3 NavMeshNavMesh stand for AI Navigation Meshes, NavMesh are built in Unity feature used to divide an environment into navigable area which the A* algorithms can then be used on for path finding. NavMesh divides the model into a series of polygons and assign a cost to each, this cost is considered in the pathfinding algorithm in determining the best path for an agent to take. NavMesh has several tabs, Agent; Area; Bake; Objects. The first defines the Agent which contain four main properties, that use to define agent size and movement abilities such as step height. The area tabs are where to define different costs for different parts for NavMesh, the cost is used for A* Algorithms to calculate the best path across the mesh from one location to another. The Bake tab turns the mesh into navigation meshes. This determines where the agent should be able to travel using the baked agent’s size. The last object tab is where you can align specific area types to different parts of the mesh. This tab also allows you to specify which parts of your scene you want to create mesh.NavMesh calculate A* algorithms per frame. Agent speeds is representing the move distance of agent each frame. NavMesh give such movement a direction and destination. NavMesh agent is one of the components relate to the NavMesh, this allow the agent search for a path to a certain goal, NavMesh agent contain several factors to control individuals including speed, size, acceleration, Avoidance Priority etc.2.3 AI algorithms and Finite State Machines2.3.1 Finite State MachinesA finite state machine (FSM) is a conceptual machine that can be in exactly one of any given number of finite States at any time. FSM is used in the field about artificial intelligence to define among other things. the behaviours of agents are represented as a graph with nodes that represent their states and transitions that define the path from one node to the next, once a state has been entered. The agent stays in that state until a condition on the transition is met. For example, If the Agent is in a state, it will continue performing the associated behaviours until a condition is met on any of the transitions exiting the state, as another condition is met the agent transitions into that state.2.3.2 Goal-Oriented Action PlanningGoal-orientated action planning (GOAP) is an AI technique that has all the elements of a finite state machine but works with them very differently. FSM defined behaviour and conditions to drive the agent at fulfilling some goal. In particular, FSM could have one massive graph or divided into smaller units. However, GOAP also uses graphs for its processing but differs from FSM which actions and goals are decoupled. Actions are free elements within the system that are mixed and matched to make goals. When they are presented, instead of having a set list of actions that need to be performed to achieve a goal, GOAP allows for number of solutions to be chosen from. In FSM state are moving from one to another if one has achieved. However, in GOAP systems state are depends on agent’s current status. GOAP has a precondition and in effect the precondition is a state that must be met before the action can take place. The effect is how the action leaves the status of the agent or the world after it has occurred a set of actions forms a kind of puzzle or set of dominoes where the preconditions can be matched with effects and the effects match with preconditions to create action chains. The conceptualisation of a GOAP system contain actions, goals and world state, those factors being fed into a Planner then generate plan according to the goals and starting States to determine which plans are achievable. Then planner uses a pathfinding algorithm to find the best path. Once a plan has been generated, the agent achieving its goal using FSM that basically moves the agent to where the action needs to take place and then performs the action. The systems will keeps checked to see if the plan is still valid, if the plan is not valid, then the entire plan will be discarded and planner will try to gives another one. GOAP systems contain high customisability and flexibility. As more and more action can be added to the pool, those action can still be picked by the planner automatically, which means very little extra code to recognize graphs like the ones in complete set ups of FSM as the graphs generated on the fly by the Planner.- GOAP Agent classThe GOAP agent is the regulator for the GOAP system which takes care of the basic agent actions and make sure that it’s going to run the plan up that it’s looking for goals that it’s keeping track of the world state as it changes. The GOAP agent is a component that relate to the FSM, as it works as a Converter that convert plan into agent action and load the actions which form the agent basic behaviour structure. The GOAP Agent class should not including the physical reaction taken by the agent, this is because those physical components should depend on the types of agent, as an example, Agent has different size, therefore different obstacle avoidance distance.The GOAP agent class will initialize the queue, planner, action list and the state machine, then it creates an idle state which would get the state and the goal that planner plan for, If the planner has a plan then the FSM will be switch to perform action state then the Agent will move to the goal. However, if there is no plan found by the planner, the agent will stay at its current location, looping inside the idle state until planner gives it a new plan. There won’t be any action perform until agent reach the goal, as function ‘requiresInRange’ will keeps checking the distance between the agents and the destination. Once the agent reaches the goal range, the action will perform, the queue will keep checking if the action has finished. After the action has been done, the pervious action will be removed from queue and state machine will return to its idle state. A complete finite state machine with GOAP structure has done.- GOAP Planner class‘GOAP planner’ plans what actions can be completed to fulfil a goal state. However, it is different compare to the GOAP Agent class, as GOAP agent class converts the plan into actions state. Firstly, the planner builds a HashSet that stores all the usable actions could running based on the ‘CheckProceduralPrecondition’ which is the agent status. Then a Node class has been introducing to the systems, with ‘cost’, ‘parent node’, ‘state’ and ‘action taken’. A list of nodes can be considered as leaves in a tree, which will provide a solution to the goal. As there are leaves, list of usable actions, cost of each action, the planner can construct a graph based on those factors. The Graph is set to Boolean value, it will test through all the leaves and see if the parent state has the conditions for this leaf’s preconditions, if at least one solution was found then return true. Moreover, we can calculate the cheapest cost action plan from those factors. Once the cheapest plan has been found, those plans will be put into the queue and send to the GOAP agents.- GOAP Action classThe Design of GOAP Action is the specification of action. Each action will inherit from GOAP Action class. GOAP Action class inherit a whole bunch of actions even if they don’t get used, the GOAP Agent class which work together with GOAP planner will then push the action as a state. Hence, the agent will perform the action state. GOAP Action in the system can be inherit as many times as needs, if another newly action needs to be added, this class concise the structure of code. GOAP Action will not connected to anything at all, it’s just sort of sitting there itself and has preconditions and has a fix. When there’s a goal that comes in, the planer gathers up all these actions and tries to paste them all together to see where the preconditions and the effects join to make a chain which becomes a new plan. However, if they don’t have preconditions in a fix that will join with other actions which mean they might never get to that plan.- GOAP Listener classIn order to make the GOAP system be versatile, a ‘format’ is required in order to differentiate between different agent types. This ‘format’ contains common features in agents with different logics. Since, all types of agent will have their state of doing action. Therefore, a GOAP listener interface has been introduce to the systems. The GOAP listener is a plan-collecting interface that collect the world data for the agent which used for GOAP planning. It contains serval components. The following components are been set as the common features.- ‘Get World State’ define the starting state of this agent and the world state. Supply what states are needed for actions to run.- ‘Create Goal State’ Give the planner a new goal, so it can figure out the actions needed to fulfil it.- ‘Plan Found’; ‘Plan Failed’, ‘Plan aborted’, ‘Action finished’ Those are the action for further develop, provide a way for adding feature to the agent when some fortuitous event happens.- ‘Move Agent’ This function design to control the movement action taken by the agents, called during update, move the agent toward the target for the next action to be able to perform, also return true if the agent has reach the target then the next action can perform. 3. Design

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The post Discuss Berger’s ‘focal points model’ in terms of its components, appeared first on Smashing Essays.

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