1. 1 CO2301 - Games Development 1 Week 13 - Revision Lecture AI Revision Gareth Bellaby

2. 2 Exam format ●2 hours. 10 minutes reading time. ●Only semester 1 material - AI + maths. ●All of the material from semester 1 is examinable.

3. 3 Exam 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

4. 4 Topic 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.

5. 5 Revision ●Read ●Read around the subject ●Write out own notes ●Condense notes ●Group study is invalulable

6. 6 Characteristics of the exam questions ●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?

7. 7 Prepare 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

8. 8 Game Agent sense memory think act (optional)

9. 9 Game 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.

10. 10 Game 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.

11. 11 Finite 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.

12. 12 Pathfinding ●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

13. 13 Pathfinding ●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?

14. 14 Pathfinding ●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?

15. 15 Pathfinding ●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.

16. 16 Breadth-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.

17. 17 Depth-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.

18. 18 Hill-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).

19. 19 Best-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.

20. 20 Djikstra'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.

21. 21 A* 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*.

22. 22 A* 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.

23. 23 Search ●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.

24. 24 Search ●Representations: grids, waypoints, regions, points-of-visibility. ●Map design.

25. 25 Game characteristics perfect informationimperfect information 2-playern-player simple physical worldcomplex physical world timing - precise time periods timing - variable time periods precise goalsimprecise goals

26. 26 Maths ●Maths: ●vectors: ●length ●normalisation ●dot product ●cross product ●angle

27. 27 Targeting ●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.

28. 28 Targeting ●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

29. 29 Interpolation 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.

30. 30 Interpolation 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.