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Unreal Engine Experiments: Borderlands 2 Damage Display System

I recently started working on some side projects during free time to experiment with & learn about interesting gameplay systems from my favorite games. And Borderlands 2, being high up on the list, I decided to create a working model of its Damage Display system using blueprints. It's finally done & here is a video demonstration of the system:



The system uses widget components attached to dynamically spawned actors, to draw the text on screen space. Just as in the game, the floating damage texts move along a parabolic path, while the critical hit status text moves along a normal linear path in the upward direction. Both of them keep scaling up till a certain point along the path, & then start scaling down for the remainder of its journey, thus creating a sort of pop-out effect. Another factor that controls the scaling of the text display is the distance from the player character, thus increasing/decreasing its size [the text becomes less clear over long distances in the…
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Tower Defense Starter Kit Tutorial: How to create a new level

1. First ensure that the default Game Mode & Game Instance class parameters in the Project Settings are set to 'BP_GameMode' & 'BP_GameInstance' classes respectively.

2. Now create a new map, open it, & lay down the navigable area for AI bots by placing instances of 'BP_AIPath' across the level. Add a Navmesh Bounds Volume & extend it to encapsulate all the AIPath actors.

3. Add a Lightmass Importance Volume around the core game space.

4. The 'BP_AIPath' actors that we added earlier has a secondary function apart from providing a traversal space for the AI bots. And that is to allow the deployment of Global Abilities like Airstrike. Everytime a player selects a global ability, a targeting reticule is activated to point the precise location for deployment. However if we have empty spaces in the level, this can be an issue as there will be no surfaces to block the GlobalAbilityTargeting trace channel. In order to rectify this issue, the toolk…

Top Down Stealth Toolkit Basics: Global Alert Level System

The following information is based on the v2.1 edition of Top Down Stealth Toolkit & hence may not remain entirely relevant in later versions. For more information about the toolkit, check out the official support thread in the Unreal Engine forums:   https://forums.unrealengine.com/unreal-engine/marketplace/68942-top-down-stealth-toolkit


The v2.1 update for Top Down Stealth Toolkit introduced a Global Alert Level Controller (GALC) tasked with controlling high-level AI responses to the player's actions. While all AI agents still retain their individual alert level systems, the GALC acts as an autonomous outside listener that keeps track of stimulus perception events, with minimal coupling to existing systems within the toolkit. With every new instance of a stimulus being perceived by an AI agent, the Global Alert Meter keeps rising based on the perceived threat rating of the stimulus. As the meter crosses certain threshold values, the Global Alert Level system kicks into action…

Thoughts on Game Design: XP Management Systems for Tower Defense Games

Two years ago, before I first started working on the Tower Defense Starter Kit, I had done some research on various games from the genre to understand the design process behind the underlying gameplay mechanics. Among the lot were popular games like Kingdom Rush & Defense Grid, as well as their lesser-known counterparts like Anomaly Defenders & Sentinel 4. Each of those games had some interesting unique feature that made them stand out & Sentinel 4 actually had quite a few of them going for it. Of special note among these, was the fact that towers leveled up on their own over time. This single design choice added a whole new layer of tactics when it came to tower placement, right from the early stages of a mission.

A couple of months ago, I finally got around to adding an experience based auto-leveling system to the toolkit. While I thoroughly enjoyed Origin8's (Sentinel 4 Developer) take on tower defense, one aspect that concerned me was the decision to use a last-hit …

Top Down Stealth Toolkit Tutorial: How to control the AI Perception model for AI agents

The following information is based on the v2.0 edition of Top Down Stealth Toolkit & hence may not remain entirely relevant in later versions. For more information about the toolkit, check out the official support thread in the Unreal Engine forums: https://forums.unrealengine.com/showthread.php?97156-Top-Down-Stealth-Toolkit


The Top Down Stealth Toolkit uses a custom Perception system to evaluate potential threats for the AI agents. It's user defined properties can all be edited through the component details panel from the owning blueprint, thus facilitating creation of different threat perception models for different types of AI agents. This tutorial will go over the basic parameters that control the working of this system.



Visual Perception Data:

1. IsVisualPerceptionEnabled?: Determines if the agent can use Visual Perception to perceive stimuli.
2. Vision Range: Determines the direct line of sight range.
3. HalfVisionAngle: Determines the half angle for the agent's cone of …

Top Down Stealth Toolkit Tutorial: How to create a new custom Interest Stimulus

The following information is based on the v2.0 edition of Top Down Stealth Toolkit & hence may not remain entirely relevant in later versions. For more information about the toolkit, check out the official support thread in the Unreal Engine forums: https://forums.unrealengine.com/showthread.php?97156-Top-Down-Stealth-Toolkit]

The v2.0 update for Top Down Stealth Toolkit introduced a dedicated stimulus driven AI perception system. This tutorial will go over the process of using the aforementioned system to create a new Interest stimulus from scratch.


The base attributes for Interest stimuli are controlled through the 'InterestStimulusDataArray' in BP_AISensoryManager class. Each element of the struct array contains details about a specific type of Interest & hence provide an avenue for easily customizing it's properties. Before going into the actual process of creating a new Interest, I'll provide a brief description of the various parameters that control that co…

Top Down Stealth Toolkit Basics: AI Perception

The v2.0 update for Top Down Stealth Toolkit introduced a custom component driven AI Perception system tailored specifically for implementation in Stealth games. This system uses a combination of four different perception models to evaluate threats based on the new Stimulus model introduced in the update.

Perception Models:

1. Visual Perception: Using a combination of distance, angular, & line trace checks, the Visual Perception enables an AI agent to detect stimuli within it's direct line of sight range. It is capable for sensing Target stimuli as well as certain types of Interest stimuli like Incapacitated & Defunct allies.

2. Aural Perception: The Aural Perception enables an AI agent to perceive & track noises created in the vicinity. It factors in the Loudness of a noise while performing the distance check operation, in order to gauge the relevance of the stimulus. This ensures that louder noises can potentially be heard by an agent, even if it was created outside the…