Team Member AI in an FPS and Goal Oriented Action Planning

Overview ► Correct Positioning ► Correct Movement ► Correct Behavior ► Supporting the Player ► Implementation ► GOAP ► AIISC

Correct Positioning ► Should not be in the players line of sight ► Should not be in the players line of fire ► Should be visible to the player ► Should give player first opportunity to act ► Positioning depends on body stance ► Should move out of sight if possible

Correct Positioning Continued

Correct Movement ► Find smallest angle to a clear view ► Move away from the players current viewing direction ► Proximity ► Level of awareness and readiness to fire ► Should use tactical strategies ► Respect current context of the situation ► Move at same speed as player

Movement Continued

Correct Behavior ► Use cover if available ► Selective firing ► Reloading

Supporting the Player ► Capture the flag ► Support player in every movement ► Reporting ► In face of danger ► Taking orders ► Protecting the player

Player is the Most Important ► Complement game play ► Use similar weapons ► Get choice of items after the player

Implementation of Squad Tactics ► Implementation Hierarchy ► 3: Environmental ► 1: Player ► 2: Threat ► 4: Team Manager

Implementation Continued ► O = # of enemies within range ► P = # of enemies threatening team ► Q = Supporting team member (T/F) ► Fire Staggering ► Should not target same enemy ► Team manager should change cover area ► Finding an Available NPC ► Availability = (1+N)(1+O)(1+P)+(Q∞) ► N = # of enemies in covering area

GOAP (Goal Oriented Action Planning) ► A decision making architecture that allows characters to decide not only what to do, but how to do it. ► Not a commonly used technique in today’s games ► Superior to more common techniques such as Finite State Machines (FSM) and Rule- based Systems (RBS).(AIISC)

Why GOAP ► Less repetitive than a FSM ► Can adapt action to fit current situation ► Less predictable than FSM ► A better way to simulate intelligence

Definitions ► Goal: any condition that an agent wants to satisfy ► Agent can have many goals A C BC B A Initial stateGoals

Definitions ► Goal… ► Only one goal active at any one time

Sussman Anomaly

Definitions ► Goal ► Three categories of gaming goals ► Relaxed ► Investigative ► Aggressive

Definitions Cont ► Goals are not hard coded and do not contain a plan ► Plan: a sequence of actions that takes an agent from a starting state to a state that satisfies a goal ► Action: a single step within a plan that make an agent do something ► Actions have effect(s) and may have precondition(s)

Benefits of GOAP ► Characters can find alternate solutions to problems ► Characters can handle dependencies that may not have been thought of at development time ► Difficult to manage every possible situation ► Characters do not have to use all possible actions (creates different behaviors)

Implementing GOAP ► Planner Search ► World Representation ► Planner Optimization

Implementation Continued

AIISC ► AI Interface Standards Committee  Their benefits ► Modularity ► Workflow ► Gameplay ► Would like to define an API that is appealing to game developers ► Determine how real-time dynamic planning can be used in practice

References ► AI Game Programming Wisdom 2 ► AI Game Programming Wisdom ► The 2004 AIISC Report ► Symbolic Representation of Game World State: Toward Real-Time Planning in Games