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CO2301 - Games Development 1

Week 13 - Revision LectureAI Revision

CO2301 - Games Development 1

Week 13 - Revision LectureAI Revision

Gareth BellabyGareth Bellaby

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Exam formatExam format●2 hours. 10 minutes reading time.

●Only semester 1 material - AI + maths.

●All of the material from semester 1 is examinable.

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Exam formatExam format● Section A and Section B.

● Section A

● Answer all of the questions

● Maths (Vector maths and graph theory)

● Pathfinding definitions and terms

● Section B

● 4 questions.

● Choose 2.

● Longer questions

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Topic ListTopic List

●There's a list of words and topics I gave at the beginning of doing the AI.

● "AI - to be learnt".

●Learn all of these phrases and their meanings.

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RevisionRevision

●Read

●Read around the subject

●Write out own notes

●Condense notes

●Group study is invalulable

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Characteristics of the exam Characteristics of the exam questionsquestions

●Describe the topic, e.g. what is a game agent?, What is a FSM?

●Be prepared to give an example, e.g. give an example of a game agent, provide an example of a FSM.

●Provide an example of use. How would it be used? Why would it be used?

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Prepare to draw a diagramPrepare to draw a diagram

●Be prepared to draw a diagram for every topic.

Idle

Attacking

Dying

Attacks No hit points

Player distance &

LOS

NPC hit points

Start

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Game AgentGame Agent

sense

memory

think

act

(optional)

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Game AgentsGame Agents

●Autonomous or semi-autonomous.

●Perceives the world and acts upon the world.

●Agents are situated in the world.

●Agents interact with the world.

●Suits genres such as FPS, but also think about games such as Viva Pinata.

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Game Agents - sensingGame Agents - sensing● Perceiving the world in a similar (or analogous) way to that of

the player.

● Seeing.

An efficient vision algorithm would only consider:

● objects that the agent is interested in

● objects within the viewing distance of the agent

● objects within the viewing angle of the agent

● objects within the unobscured LOS of the agent

● Hearing.

● Similar considerations to seeing, but need to discuss sound propagation, ambient sound, etc.

● Communication with other agents.

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Finite State MachineFinite State Machine● Finite State Machine is a machine which represents

behaviour as:

● states

● transitions between states

● actions. The actions can occur within a state, on entry to a state, on exit from a state.

● Need a start state and an end state.

● You need to consider FSMs as a diagramatic technique.

● I provided a bunch of examples. Make sure you understand them and write similar examples of your own.

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PathfindingPathfinding

● What is pathfinding?

●7 algorithms:

●Crash and Turn

●Breadth-first algorithm

●Depth-first algorithm

●Hill-climbing algorithm

●Best-first algorithm

●Djikstra's algorithm

●A* algorithm

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PathfindingPathfinding●Don't need to learn the algorithms off by heart but

you do need to able to describe and discuss them.

●You must be aware of the fundamental characteristics of each of the search techniques.

●You must be able to explain and discuss the algorithms.

●Each of the search techniques can be compared to the others.

●What are the relative advantages and disadvantages of each of the searches?

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PathfindingPathfinding

● Description: The characteristics of the algorithm.

● Is the algorithm guaranteed to find a solution?

● If the algorithm guaranteed to find the optimal solution?

● Efficiency: speed, size of tree.

● Heuristic: blind or directed?

● Cost: does the algorithm employ cost?

● Application: Where would you use the algorithm? What are the advantages and disadvantages of the algorithm?

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PathfindingPathfinding

●Simplest algorithm is "Crash and Turn": move towards objective, if crash into an obstacle turn and move around its perimeter until you can move towards the objective again.

●Simple to implement.

●Can be effective in simple situations

●Guaranteed to work so long as all objects are convex.

●Think about using in combination with other methods.

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Breadth-first algorithmBreadth-first algorithm

●Expands all of the nodes, layer by layer.

●Exhaustive search.

●Guaranteed to find solution, if one exists.

●Guaranteed to find the optimal solution.

● Inefficient because every node searched. The problem of combinatorial explosion.

●Does not employ a heuristic.

●OK for small problems where size is manageable. Useful if you definitely want to search all of the nodes.

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Depth-first algorithmDepth-first algorithm● Only one node expanded. Single path followed.

● Need to include backtracking, otherwise it is pretty useless.

● Not guaranteed to find a solution. (But variants such as using a limiting wall and employing backtracking are guaranteed to do so).

● Not guaranteed to find the optimal solution.

● Overcomes combinatorial explosion because a smaller tree is generated.

● Does not employ a heuristic.

● May get lucky and, on average, faster than breadth-first.

● Used for problems in which optimality not important.

● Can be used in conjunction with other searches, e.g. for opportunistic searches.

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Hill-climbing algorithmHill-climbing algorithm● Current node examined to find the best successor. Path follows best

successor.

● Better than depth and breadth.

● Basic algorithm is not guaranteed to find a solution.

● Foothills.

● Plateaus.

● Ridges.

● However, it is guaranteed to find a solution if backtracking is used, together with the option to move to a worse successor if no other alternatives exist.

● Not guaranteed to find optimal solution.

● Uses a heuristic.

● Variants include randomness, jumps.

● Useful for climbing a hill! (Or equivalent).

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Best-first algorithmBest-first algorithm

● All unexpanded nodes recorded. Best overall node chosen.

● Uses a heuristic.

● Better than depth, breadth and hill-climbing.

● Guaranteed to find a solution.

● Not guaranteed to find the optimal solution, e.g. on a map with terrain costs.

● Useful for maps with no costs or equivalent kinds of problems.

● Can be employed in a game with moving obstacles.

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Djikstra's algorithmDjikstra's algorithm● Uses cost.

● Choose the successor node with the lowest cost. Cost is accumulated along each route.

● Guaranteed to find a solution.

● Guaranteed to find the optimal solution.

● Very efficient for maps in which all of the costs can be pre-generated.

● For a game with a large map can be inefficient because potentially a large number of alternatives may be considered.

● Inefficient with moving obstacles since cost would have to be regenerated.

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A* algorithmA* algorithm

● Uses cost and heuristic.

● Choose the successor node with the lowest summed cost + heuristic. Cost is accumulated along each route.

● Guaranteed to find a solution.

● Guaranteed to find the optimal solution.

● An efficient algorithm.

● A commonly used games algorithm. Compare with Djikstra's. If cost can be pre-generated and stored efficiently then Djikstra's will be the better choice, otherwise use A*.

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A* algorithmA* algorithm

●Admissability, , i.e. the heuristic never overestimates the distance.

●Graceful decay of admissability. Be strict about admissability at the beginning of the search but as the search is assumed to get closer to the destination relax the admissability criterion.

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SearchSearch

●Combinatorial explosion: the problem whereby rules in combination give rise to a large number of alternatives.

●Dealing with the complexity of the real world.

●Simplification. Using a less detailed representation

●Level-of-detail, e.g. different sized grids for pathfinding, rule based system can have different levels.

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SearchSearch

●Representations: grids, waypoints, regions, points-of-visibility.

●Map design.

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Game characteristicsGame characteristics

perfect information imperfect information

2-player n-player

simple physical world complex physical world

timing - precise time periods

timing - variable time periods

precise goals imprecise goals

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MathsMaths

●Maths:

●vectors:● length

● normalisation

●dot product

●cross product

●angle

cos wvwv

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TargetingTargeting●Test of "in front of", or "behind".

● x is the gun's x-axis.

● w is the vector from the gun to the target.

● x • w > 0 if the target is to the right

● x • w = 0 if the target is straight ahead.

● x • w < 0 if the target is to the left

●Also "to left of", or "to right of".

●Deriving the local axis, e.g. local x-axis.

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TargetingTargeting

●Construct a LookAt function. You need to be able to write down how the function is constructed. You need to understand:

● length of a vector

●normalise a vector

●dot product

●cross product

● the relevant axes

●Write down the procedure in full

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Interpolation and splinesInterpolation and splines

●Path smoothing is the process of smoothing out the angular route produced by the search method. So implemented after the search has been calculated.

●Be able to define and describe the terms.

●Describe and discuss what a spline is.

●Describe and discuss interpolation.

●Describe and discuss the characteristics of the Catmull-Rom and Bézier curve.

● Interpolation of the tangents.

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Interpolation and splinesInterpolation and splines

●Path smoothing.

●Smoothing is implemented through the use of interpolation - calculating points in between our known points.

●Looked at a method of generating a curve called the Catmull-Rom spline.

●Generate tangents based on the position of the sample points.