Tactical & Strategic AI

Tactical & Strategic AI

Tactical and Strategic Reasoning • Covers… • Deducing tactical situations from sketchy (limited) information • Using tactical situations to make decisions • Coordinating between multiple characters • …and more….

Waypoint Tactics • A waypoint – a single position in the game level (“nodes”, “representative points” used for pathfinding) • To use waypoints tactically need to add more data to the nodes (not just location info) • Waypoints can be used to represent positions in the level with unusual tactical features (so that characters are better positioned tactically) • Normally the level designer has some say in this…

Tactical Locations • Waypoints used for tactical purposes are sometimes called – “rally points” e.g. • To mark a fixed safe location for character to retreat if losing fight (defensive) • To mark a pre-determined hiding spot that can ambush or snipe incoming enemy (offensive) • To move secretly in shadow areas without being detected (stealth) • Many more…

Tactical Locations

Tactical Locations • A game level consists of a large set of waypoints, each labeled with tactical qualities • If waypoints used for pathfinding, they will also inherit other data such as connections etc. • Practically, tactical locations are not very useful as part of a pathfinding graph… • More efficient to have separate pathfinding graph and tactical location set

Tactical Locations • Although common to combine two sets of waypoints (one for tactical, one for pathfinding), not efficient nor flexible • E.g. Cover and sniping waypoint nodes are not useful for pathfinding! Result in unrealistic movements within level

Primitive and Compound Tactics • Most games have a set of pre-defined tactical qualities (e.g. sniping, shadow, cover, etc.). These are primitive defined tactics • Combination of these primitive tactics result in locations with compound tactical qualities. • E.g. Sniper locations – Points that have combination of both cover points and high-visibility points. • A point can have both defensive and offensive tactical features.

Primitive and Compound Tactics • For this e.g. how is an ambush point constructed from primitive tactical locations?

More Compound Tactics – Waypoint Graphs • Waypoints can be CONNECTED to form waypoint graphs (similar to pathfinding graphs) when the waypoints defined are not isolated/separated • Where is the best spot for a hit-and-run move? • Topological analysis

Continuous Tactics • Marking locations with numerical values (able to use fuzzy logic and probabilities) instead of Boolean values • E.g. A waypoint will have a value for cover feature (0.7) and visibility feature (0.9) • In choosing between a few cover points to go, choose one that has better/higher value • Using fuzzy logic rules can allow us to combine these values, E.g. • Sniper (value) = cover (value) AND visibility (value) • Sniper = MIN(0.7, 0.9) = 0.7

Using Tactical Locations • How do we build a tactical mechanism within the character AI? • Three approaches: • Controlling tactical movement (simple method) • Incorporate tactical information into decision-making • Use tactical information during pathfinding to produce character motion that is always tactically aware

1. Tactical Movement • Tactical waypoints are queried during game when the character AI needs to make a tactical move • E.g. Character needs to reload bullets, it queries the tactical waypoints in the immediate area to look for “nearest suitable location” to stop and reload, before continuing • Action decision is carried out first, then apply tactical information to achieve its decision • Limitation: Some realism, not able to use tactical information to influence decision making

2. Tactical Information in Decision-Making • Give the “decision-maker” access to tactical information, just like any other game world information • DT example: • SM: Trigger transitions only when certain waypoints are available and/or fulfill required numeric value (if used)

3. Tactical Information during Pathfinding • Relatively simple extension of basic pathfinding. • Rather than finding shortest/quickest path, it takes into consideration tactical situation of game • Simplest way is to manipulate graph connection costs (by adding “tactical cost” to locations that are dangerous or reducing “tactical cost” at locations that are easy)

Finding nearby waypoints • To use any of these approaches, a fast method is needed to generate nearby waypoints • Given location of character, generate a list of suitable waypoints in order of distance • Methods to work out what objects are nearby  Binary space partitions (BSP), quad-trees, multi-resolution maps

Creating waypoints • So far we assumed the waypoints are pre-created (by developer or level designer), and the properties have been decided • This is the most common practice – 1) Actual properties are decided, 2) context-sensitive information placed at locations to be interpreted later • It is also possible to CALCULATE the tactical properties of each location automatically – cover, visibility, shadow points, etc.

Tactical Analyses • Sometimes known as influence maps – a technique pioneered and widely used in RTS games where the AI keeps track of areas of military influence in game • Can also be used in simulation/evolution games, FPSs or MMOs • Overwhelming majority of current implementations are based on tile-based grid worlds. Even for non-tile-based worlds, a grid can be imposed over the geometry for tactical analyses. • Already discussed In Pathfinding lesson

Revising Influence Maps • Keeps track of “military” influence at each location level • Calculations – Linear/non-linear drop-off • Limited radius of effect – to constrain calculations to a finite circular area • Allows game AI to analyze the game level, for strategic decision making • “Fog-of-war”

Combining Tactical Analyses • Multi-layer analyses involved combining a few influence maps into a composite influence map. • Example: To find best location to build tower, consider: Wide range of visibility, secured location, far from other towers to avoid redundancy (3 maps) • To get a single influence value, the 3 base tactical analyses can be combined by multiplication (or addition, etc.) Quality = Security x Visibility x Distance (or if tower influence is used instead of distance) Quality = Security x Visibility Tower Influence

Combining Tactical Analyses

Structure for Tactical Analyses • Different types of tactical analyses can be distinguished by its properties and frequency of updating needed

Tactical Pathfinding • Similar to regular pathfinding (same techniques/ algos), only modification is the cost function used – extended to tactical info • Cost function influenced by two criteria: • Distance/time • Tactical Information • Cost of a connection given by a formula where D is the distance/time of connection, wi is the weighing factor for each tactic Ti and i is the number of tactics supported.

Tactic Weights and Blending • The value for each tactic is multiplied by a weighting factor before summing into the final cost value. • Locations with hightacticsweight will be avoided • Locations with lowtacticsweight will be favored • Weights can be negative, BUT careful not to have negative overall weight, which may result in negative overall cost! • Tactical costs can be pre-calculated if they are static (terrain, visibility). If they are dynamic (military power, number of units), they should be updated from time-to-time

Customizing Weights • In certain games, different units can have different sets of tactical weights (w) based on their characteristic. • Example: Reconnaissance units, light infantry, heavy artillery. Tactical info: terrain difficulty, visibility, proximity of enemy units

Customizing Weights • Weights can also be customized according to a unit’s aggression • E.g. Healthy units finds paths in normal way. When it is injured, the weight for proximity to enemy can be increased to make the unit choose a more conservative route back to base.

Implications on heuristic-based pathfinding • When modifying pathfinding heuristics (especially for A*), make sure heuristic measure is not reduced too much due to subtraction of tactical costs, or increased too much due to addition of tactical costs.  May result in underestimating or overestimating heuristic

Coordinated Action • To coordinate multiple characters to cooperate together to get their job done, some structure need to be in place. Two categories: • Team/Group AI (a group of AI NPCs, fully AI) • Cooperative AI (AI cooperates with a human player in a team) • Common Qs: • Should individual AIs “speak” to each other, and make collective decisions? • Should a central “command center/brain” give orders and instructions to each individual AI? • Can we have a bit of both?

Multi-Tier AI – Top-Down • Highest level AI makes a decision, passes it down to next level, which uses its instruction to make its decision, and pass again down to the lowest level • Example: Military Hierarchy

Multi-Tier AI – Bottom-Up • Lowest level AI algorithms take their own initiative to make decisions, then use higher level algorithms to provide information on which they can base their actions • Example: Autonomous decision making by individual characters that can influence the overall game: Squad-based Strategy games, Evolution-based games • “Emergent cooperation”

Multi-Tier AI – Example • Multiple levels – Each character ha s its own AI, squads of characters together will have a different set of AI algorithms

Multi-Tier AI – Example • Player involvement in group AI: Recognition of player actions and passing down to next layers • Intermediate layers between player and individual AIs should rely on player action • Decision making can act to complement player

Structuring Multi-Tier AI • 2 infrastructure components: • A communication mechanism to transfer orders from higher layers in the hierarchy downward: Overall strategy, assigned targets, areas of avoid, etc. • A hierarchical scheduling system to execute the correct behaviors at the right time, in the right order, and only when required

Emergent Cooperation • Less centralized, more free-flowing • Characters take into account what other characters are doing (e.g. moving together)

Emergent Cooperation • E.g. FSM for four characters in a team – provide mutual cover and working coherently • If any member is removed, team operates as usual

Tactical & Strategic AI