Case Based Planner Platform For Rts Games
Download as PPTX, PDF1 like666 views
The document describes several case-based planning platforms for real-time strategy (RTS) games: 1. The CAT system uses case-based reasoning to learn to win the RTS game Wargus by training against some opponents and then defeating a novel opponent. 2. The Darmok system retrieves relevant past cases based on the current game state's shallow and deep features to determine the best plan to achieve the goal of winning. 3. These systems represent cases as goals, states, plans and performances to learn tactics and strategies from previous games. Shallow features define situations while deep features discriminate between cases.
Case Based Planner Platform For Rts Games
- 1. Case-Based Planner Platform for RTS GamesAn IntroductionAbdelrahman Al-OgailAhmed Atta
- 2. AgendaCase Representation.David W. Aha Platform.Santiago Platform.
- 3. Case RepresentationCase = ( Goal , State , Plan, Performance)
- 4. Case Representation Example of a caseGoal:ResourceGoal(Gold, MaxInfluence)State: Number of gold mines Distance between gold mines and nearest gold mine storer Number of free or useless peonsPlan: Train(3, peon) Assign(3, peons, GoldMiner) Build(Farm) Build(GoldMineCollector, 200)Performance: Number of gold collected in 1 minute
- 5. CBP – CATAbstractThe Case-Based Tactician (CAT) system, created by Aha, Molineaux, and Ponsen (2005), uses case-based reasoning to learn to win the real-time strategy game Wargus. Previous work has shown CAT’s ability to defeat a randomly selected opponent from a set against which it has trained. We now focus on the task of defeating a selected opponent while training on others. We describe CAT’s algorithm and report its cross-validation performance against a set of Wargus opponents.
- 6. CBP - CAT- Spronk and Ponsen developed a genetic algorithm and a technique called dynamic scripting to learn plans spanning the entire game which win against fixed opponent.CAT is the first case-based system designed to defeat an opponent that uses tactics and strategies that it has not trained against (novel opponent).
- 7. RTS Games concerns on: military combat, exploration, economic development, research advancement, limited diplomacy.CBP - CATSources of knowledge:
- 9. Set of tactics in every building state.
- 10. Game State.Winning (i.e., by destroying all the enemy units and buildings) requires managing three key resources: buildings, the workforce, and an army.The decision space is the set of possible actions that can be executed at a particular moment
- 11. CBP - CATWe estimate this (action space) as O(2W(A*P) +2T(D+S) + B(R+C)), Where:W is the current number of workers.A is the number of assignments workers can perform (e.g., create a building, gather gold)P is the average number of workplaces.T is the number of troops (fighters plus workers).D is the average number of directions that a unit can move.S is the choice of troop’s stance (i.e., stand, patrol, attack).B is the number of buildings.R is the average choice of research objectives at a building.C is the average choice of units to create at a building.
- 12. CBP - CATGame State Variables
- 13. CBP - CAT
- 14. CBP - CATIdea of breaking game into periods in order to current available buildings.Building state is time between the constructions of such building to the time the next is built.Building state defines the set of actions available to the player at any one time.In contrast, CAT performs no adaptation during reuse, but does perform case acquisition. Also, CAT focuses on winning a game rather than on performing a subtask.
- 15. CBP - CATCAT retrieves cases when a new state in the lattice is entered.The similarity between a stored case C and the current game state S is defined as:SimC, S = (CPerformance/dist(CDescription, S)) - dist(CDescription, S)where dist() is the (unweighted, unnormalized) Euclidean distance between two cases for the eight features.However, to gain experience with all tactics in a state, case retrieval is not performed until each available tactic at that state is selected e times, where e is CAT’s exploration parameter.During exploration, CAT randomly retrieves one of the least frequently used tactics for reuse. Exploration also takes place whenever the highest Performance among the k-nearest neighbors is below 0.5.
- 16. CBP - CATThen after applying the case we evaluate by:
- 17. CBP - CATEvaluation yields the Performance of a case’s Tactic, which is measured at both a local and global level. That is, CAT records the WARGUS game score for both the player and opponent at the start of each BuildingState and at the game’s end, which occurs when one player eliminates all of the other’s units and buildings.In retaining C’ if we found C with same <Description, Tactic> then we update it. Otherwise create new case
- 18. CBP - DarmokDarmok starts the execution with the initial goal of “WinWargus”.The system Retriever will try to return a plan to satisfy this goal by going on the following 4 steps:
- 19. CBP - Darmok1- Define the current SituationGame State best-first greedy hill-climbing algorithm shallow features aExample of extracted shallow features:lumber (number of trees in the map), food (amount of food), gold (amount of gold of the player), peasants (number of peasants) and units (number of units the player has)Game state Shallow featuresvaluesSituationbIn this example , According to the values of these features we predict that the current situation is BEGINNING
- 20. CBP - Darmok2- Return a set of cases related to the current situation Set of casesCase base SituationReturns all cases which have Situation = BEGINNING , and Goal = WINWARGUS
- 21. CBP - Darmok3- determining discriminate features Set of cases Deep featuresa feature selection algorithm Example of extracted Deep features:ispath (a boolean feature that is true when there is a path from the player base to the enemy base)
- 22. wallbarrier- width (the width of the biggest barrier between the player and the enemy)
- 23. baseproximitydistance (distance between the player’s base and the enemy base).CBP - Darmok4- define the most relevant caseDeep features case(s)normal retrieval techniquesAccording to the values of these deep features, the retriever will determine which case is the most relevant to the current game situation
- 24. CBP - DarmokShallow Features VS Deep Features Shallow FeaturesSome features used to define the currentgame situation.Deep featuresSome features used to discriminate betweensome cases.
- 25. ReferencesDefeating Novel Opponents in a Real-Time Strategy Game - 2005.pdf – David W. AhaLearning to Win - Case-Based Plan Selection in a RTS Game- 2005.pdf - David W. AhaCase-Based Planning and Execution for RTS Games - 2007.pdf - Santiago Onta˜n´onSituation Assessment for Plan Retrieval in RTS Games - 2009.pdf - Santiago Onta˜n´on