Automated Planning & Computer Games: Perspectives and Applications Hector Munoz-Avila

General-Purpose Planning: State & Goals Initial state: (on A Table) (on C A) (on B Table) (clear B) (clear C) Goals: (on C Table) (on B C) (on A B) (clear A) Initial state Goals C B A B C (Ke Xu)

General-Purpose Planning: Operators No block on top of ?x No block on top of ?y nor ?x ?x ?y transformation ?y ?x … … On table Operator: (Unstack ?x) Preconditions: (on ?x ?y) (clear ?x) Effects: Add: (on ?x table) (clear ?y) Delete: (on ?x ?y)

Planning: Search Space B C A B C B A B A C B A C B A B C C B A B A C A C A A B C B C C B A A B C (Michael Moll)

Some Examples Which of the following problems can be modeled as AI planning problems? Route search: Find a route between Lehigh University and the Naval Research Laboratory Project management: Construct a project plan for organizing an event (e.g., the Musikfest) Military operations: Develop an air campaign Information gathering: Find and reserve an airline ticket to travel from Newark to Miami Game playing: plan the behavior of a computer controlled player Resources control: Plan the stops of several of elevators in a skyscraper building. Answer: ALL!

Classes of General-Purpose Planners General purpose planners can be classified according to the space where the search is performed: We are going to discuss these forms Hierarchical Disjunctive plans state plan SAT

State- and Plan-Space Planning State-space planners transform the state of the world. These planners search for a sequence of transformations linking the starting state and a final state State of the world (total order) Plan-space planners transform the plans. These planners search for a a plan satisfying certain conditions (partial-order, least-commitment)

Why Plan-Space Planning? 1. Motivation: “Sussman Anomaly” Two subgoals to achieve: (on A B) (on B C) A C B A B C

Why Plan-Space Planning? Problem of state-space search: Try (on A B) first: put C on the Table, then put A on B Accidentally wind up with A on B when B is still on the Table We can not get B on C without taking A off B Try to solve the first subgoal first appears to be mistaken A A B C A B C B C

But… Does Automated Planning has any use in Computer Games? F.E.A.R. not! Intro: http://www.youtube.com/watch?v=iNiss2ccDjA Combat sequence

Implementation Points in FEAR Goal Three categories of gaming goals Relaxed Investigative Aggressive Planner Search: state-space planning World Representation: compiled in a data structure Planner Optimization: compiled operators, heuristics (factoring the gaming goals)

Automated Planning: Uses in Games AI paradigm used to implement opponent’s behavior in FEAR (see previous class) Automated Planning is used to gain flexibility in designing Game AI behavior An action: Attack positioned Weapon ready Enemy attacked effects preconditions

Example positioned Enemy attacked Attack Weapon is ready positioned FEAR defines 30+ such actions. So could generate many plans. How else could we use to create an opponent AI? Attack positioned Weapon is ready Enemy attacked Stay-put positioned Take cover Prepare Weapon Ammo loaded Holding weapon Weapon is ready How many plans can we generate to achieve Enemy attacked? FSMs (Project 1)

Optimization: Representation Declared as a C++ procedure: Action Attack() { if (positioned && WeaponISReady) EnemyAttacked = true; } Attack positioned Weapon is ready Enemy attacked Declared as a variable in standard C++: Boolean EnemyAttacked; Discussion points: Complexity versus an standard AI planner So why AI planners do not adopt this?

Optimization: Search C A B C A B C B A B A C B A C B A B C C B A B A C (Michael Moll)

Implementation Continued

Implementation Continued Operators are represented as code Allows fast running time evaluation

FSM vs AI Planning (for details see Stanislav’s presentation) FSM: Planning Operators Patrol Preconditions: No Monster Effects: patrolled Fight Monster in sight (for details see Stanislav’s presentation) Patrol Fight Monster In Sight No Monster FSM: A resulting plan: Patrol patrolled Fight No Monster Monster in sight Neither is more powerful than the other one

Planning Gives More Flexibility But Developer May Loose Control “Separates implementation from data” --- Orkin reasoning knowledge Many potential plans: Patrol Fight … Planning Operators Patrol Preconditions: No Monster Effects: patrolled Fight Monster in sight … If conditions in the state change making the current plan unfeasible: replan!

Hierarchical (HTN) Planning Principle: Complex tasks are decomposed into simpler tasks. The goal is to decompose all the tasks into primitive tasks, which define actions that change the world. Travel(UMD, Lehigh) Travel from UMD to Lehigh University Fly(National, L.V. International) Travel(L.V. Int’nal,Lehigh) Travel(UMD,National) Seats available Travel by plane Enough money for air fare available Travel by car Enough money for gasoline Roads are passable Taxi(UMD,UMD-Metro) Metro(UMD-Metro,National) alternative methods Taxi(L.V. Int’nal,Lehigh)

Application to Computer Bridge Chess: better than all but the best humans Bridge: worse than many good players Why bridge is difficult for computers It is an imperfect information game Don’t know what cards the others have (except the dummy) Many possible card distributions, so many possible moves If we encode the additional moves as additional branches in the game tree, this increases the number of nodes exponentially worst case: about 6x1044 leaf nodes average case: about 1024 leaf nodes Not enough time to search the game tree (Dana S. Nau)

How to Reduce the Size of the Game Tree? Bridge is a game of planning Declarer plans how to play the hand by combining various strategies (ruffing, finessing, etc.) If a move doesn’t fit into a sensible strategy, then it probably doesn’t need to be considered HTN approach for declarer play Use HTN planning to generate a game tree in which each move corresponds to a different strategy, not a different card Reduces average game-tree size to about 26,000 leaf nodes Bridge Baron: implements HTN planning Won the 1997 World Bridge Computer Challenge All commercial versions of Bridge Baron since 1997 have include an HTN planner (has sold many thousands of copies) (Dana S. Nau)

Why “Classical”? Classical planning makes a number of assumptions: Symbolic information (i.e., non numerical) Actions always succeed The “Strips” assumption: only changes that takes place are those indicated by the operators Despite these (admittedly unrealistic) assumptions some work-around can be made (and have been made!) to apply the principles of classical planning to games Neoclassical planning removes some of these assumptions