Introduction

Tactical pathfinding has a reputation for being more intricate or complex compared to other pathfinding algorithms. Despite that, tactical pathfinding is quite similar to those algorithms; the only difference is the cost incorporates tactical information in addition to the typical distance and time cost (Millington 2019). Along with a weight multiplied by the tactical information, the product is added to the distance and time cost to create the new cost for the connection; This can cause intricacies as tactical information is commonly stored on a per-location basis.

Application In Games

You can utilize tactical pathfinding in virtually any game involving complex strategic gameplay. For instance, stealth games and military combat would incorporate this technique to allow AI-controlled enemies to take additional actions such as fleeing, calling in reinforcements when outnumbered, taking cover, hiding, reloading, etc. (Cifelli 2016). All those actions deliver a slightly more immersive experience to the player than the standard shortest path pathfinder. 

At first thought, Real-Time Strategy (RTS) games' AI may seem to benefit from incorporating elements of tactics; however, since the player is manipulating their pawns' actions, this method is used. RTS games that involve non-player-controlled squads could possibly work; It would just require intelligent game level design; to prevent the player's team from poor pathfinding, such as accidentally subverting from the player's preferred path. The squad can then evaluate the game situation correctly to perform actions: seeking cover if the enemy is shooting, squatting if the player is crouching, jumping over a ledge after the player has jumped, covering the player while they are flanking, etc. (Millington 2019).

Influence Maps for Pathfinding

Influence maps are great for handling tactical pathfinding, particularly for large outdoor environments. These maps are evoked from spatial logic through propagators, which are constructed by scalar fields of influence, carrying positive or negative influence. Influence maps provide these components to make the AI make better decisions based on the influence value at the given AI's location (Adaixo 2014):

Situation Summary - Summarize the active details in the world, allowing data retrieve easier (quick response time)

Historical Statistics -  store the current world's information for a limited amount of time

Future Predictions - Influence maps anticipate upcoming events.

If a red unit were to enter a blue area, the influence map would predict that the blue units would attack, so the red units would stand guard and prepare to call for reinforcements.

Basic Influence Map Demo

Multi-Tier AI

With Multi-Tier AI, each unit has its own AI while partaking in a group and there are tiers for how decisions are made collectively and individually; There are two common types of decision trees in this technique top-down approach and bottom-up (Millington 2019). .

The top-down approach is where the highest level AI makes a decision for the group and passes the information down each tier to the lowest level. There is usually a strategic reasoning system like state-machines, tactical analysis, or influence maps. The highest decision is the player in this approach. The characters will respond to the player's presence and input. For instance, if the player decided to enter a nearby river to "quickly" get across the other side, the AI would follow the player in this approach to match the player's preferred path. 

The bottom-up approach is where the lowest level AI accesses the game situation and acts on them. Systems on lower tiers would allow units to stay together while avoiding local obstacles and storing new information from their surroundings (from influence maps as an example (Millington 2019). In the river example, in a game where group members have different attributes like swim speed or walk speed, a group member with a faster swim speed would follow the player's preferred path. If there was a nearby bridge, other group members may opt to walk across the bridge and wait for the player to reach the other side (with influence-map-based future predictions).

An example of a Multi-Tier AI hierarchy

References

Adaixo, Michael Carlos Gonçalves. “Influence Map-Based Pathfinding Algorithms in Video Games.” uBibliorum, June 20, 2014. https://ubibliorum.ubi.pt/handle/10400.6/5517?locale=en. 

Cifelli, Tyler. “Tactical Waypoints and Tactical Pathfinding.” Tyler Cifelli, April 25, 2016. https://tylercifelli.com/2013/12/07/tactical-waypoints-and-tactical-pathfinding/.