游戏要好玩需发展良好的AIppt课件

1、Evolving Reactive NPCs for the Real-Time Simulation Game Advisor：Dr. HsuReporter：Wen-Hsiang Hu Author：JinHyuk Hong and Sung-Bae ChoIEEE Symposium on Computational Intelligence and GamesOutlineMotivationObjectiveIntroductionThe game: Build & BuildBasic behavior modelCo-evolutionary behavior generatio

2、nExperiment and Results DiscussionConclusionPersonal OpinionMotivationAI in computer games has been highlighted in recent, but manual works for designing the AI cost a great deal.ObjectiveDesigning NPCs behaviors without relying on human expertise.Basic behavior modelTwo different grid scales are us

3、ed for the input of the neural network such as 55 and 1111.five neural networks are used to decide whether the associating action executes or not.The game: Build & Buildrandom action probability: 0.2Co-evolutionary behavior generationWe use the genetic algorithm to generate behavior systems that are

4、 accommodated to several environments.Experiment and Results55 obtains lower winning averages for complex environment, while it performs better when the environment is rather simple.IntroductionIt is challengeable for many researchers to apply AI to control characters. (AI produce more complex and r

5、ealistic games.)Finite state machines and rule-based systems are the most popular techniques in designing the movement of characters.While neural networks, Bayesian network, and artificial life are recently adopted for flexible behaviors.Evolution generates useful strategies automatically.This paper

6、 proposes a reactive behavior system composed of neural networks is presented, and the system is optimized by co-evolution.Rule based approachAI of many computer games is designed with rules based techniques such as finite state machines (FSMs) or fuzzy logic.FSMs have a weak point of its stiffness;

7、 however, the movement of a character is apt to be unrealistic.there is a trend towards fuzzy state machine (FuSM).Adaptation and learning: NNs, EAs, and Artificial lifeThe adaptation and learning in games will be one of the most major issues making games more interesting and realistic.Neural networ

8、k, and evolutionary algorithms (e.g. genetic algorithm) are promising artificial intelligence techniques for learning in computer games.NN - is badly trainedGE - required too many computations and were too slow to produce useful results.Co-evolutionBy simultaneously evolving two or more species with

9、 coupled fitness.Superior strategies for an environment have been discovered by co-evolutionary approaches.Reactive behaviorReactive model performs effectively since it considers the current situation only.Neural networks and behavior-based approaches are recently used for the reactive behavior of N

10、PCs keeping the reality of behaviors.The game: Build & BuildBuild & Build developed in this research is a real-time strategic simulation game, in which two nations expand their own territory.Each nation has soldiers who individually build towns and fight against the enemies, while a town continually

11、 produces soldiers for a given period.The game: Build & BuildDesigning the game environmentThe game starts two competitive units in a restricted land with an initial fund.The units are able to take some actions at the normal land but not at the rock land.A unit can build a town when the nation has e

12、nough money, while towns produce units using some money.Designing the game environment(cont.)Designing NPCsNPC can move by 4 directions as well as build towns, attack units or towns, and merge with other NPCs.The attack actions are automatically executed when an opponent locates beside the NPC.Desig

13、ning NPCs (cont.)Designing NPCs (cont.) Basic behavior model (cont.)Two different grid scales are used for the input of the neural network such as 55 and 1111.Basic behavior model (cont.)In order to actively seek a dynamic situation, the model selects a random action with a probability (in this pape

14、r, a = 0.2) in advance.five neural networks are used to decide whether the associating action executes or not.Co-evolutionary behavior generationWe use the genetic algorithm to generate behavior systems that are accommodated to several environments.Two pair-wise competition patterns are adopted to e

15、ffectively calculate the fitness of an individual.Co-evolutionary behavior generation (cont.)The fitness of an individual is measured by the scores against randomly selected M opponents.Experiment and ResultsFour different battle maps = demonstrate the proposed method in generating strategies adapti

16、ve to each environment.Experiment and Results (cont.)The case with 1111 shows more diverse behaviors than that with 55, since it observes information on a more large area.55 obtains lower winning averages for complex environment, while it performs better when the environment is rather simple.Experim

17、ent and Results (cont.)Fig. 8. Winning rate between 55 behavior and 1111 behavior at each generation on map type 3.The 1111 shows the better performance than the 55, since it considers more various input conditions so as to generate diverse actions.Experiment and Results (cont.)For the plain map, 55

18、 behavior system shows a simple strategy that tries to build a town as much as possible. Building a town leads to generate many NPCs so as to slowly encroach on the battle map as showns in Fig. 9.DiscussionThe reactive system shows good performance on simple environments like the plain map, but it does not work well for complex environments.Also, the amount of input information is important for the reactive system when the environment is not simple.ConclusionA reactive behavior system was presented for the flexible and reactive behavior of the NPC.Co-evolutionary