Performance-Based Barking: A Dynamic AI System for Enhanced Gaming Experience

Introduction: 

In this article, we explore the basic concept behind Performance-Based Barking, a powerful AI system that enriches the gaming experience through dynamic in-game reactions based on the player's performance. At its core, this system employs "barks," which are short, expressive lines of dialogue delivered by AI agents or non-player characters (NPCs). These barks serve as quick responses to the player's actions and achievements, adding a layer of transparency and immersion to the gameplay. By tracking the player's success rates and implementing the Memory Table and Look Out Table, game developers can create more personalized and engaging gaming scenarios. We will explore the intricacies of Performance-Based Barking, demonstrate its application in FPS and carnival games, and propose improvements to further unleash its potential for game developers.

Understanding Performance-Based Barking: 

Performance-Based Barking is a versatile AI system that can easily integrate into different game genres, enriching the narrative and gameplay dynamics. At its core, the system monitors the player's success rates on specific tasks, calculates performance scores, and triggers tailored in-game reactions based on these scores. With careful design and creative implementation, game developers can provide players with a more personalized and interactive gaming experience.

Defining the FPS Scenario: 

In the FPS scenario, we'll focus on a game where the player assumes the role of a skilled sniper tasked with taking down enemy targets in various missions.

Memory Table: 

The Memory Table is a vital component for tracking the player's shooting performance in different sections of the game. Each section represents a specific mission or level, and the table stores the total number of shots fired and the normalized shooting performance for that section.

Section	Total Shots	Performance
1	30	0.73
2	42	0.82
3	55	0.95
...	...	...

The normalized shooting performance is calculated by dividing the number of successful shots by the total shots fired in that section. This measurement gives us valuable insights into the player's improvement over time.

Look Out Table: 

AI Agent Barks in FPS: The Look-out Table holds the rules and corresponding barks for AI agents, enabling them to react to the player's shooting performance.

Rules	Barks
PerformanceSection1 < PerformanceInSection && PerformanceInSection >= 0.8	"I can't believe how much you've grown! When we first met, you could barely hit a target."
PerformanceInSection >= 0.6	"Your shooting skills have improved, but there's still room for growth."
PerformanceInEarlierSection - PerformanceInSection >= 0.2	"You seem to be getting better with each encounter."
...	...

The rules are combinations of parameters that need to be true for the AI agent to deliver a particular bark. For example, the agent can say the first bark when the player's shooting performance in the current section is 0.8 or higher, indicating significant improvement, while the second bark is used for performances between 0.6 and 0.8.

Implementing AI Agent Reactions: 

Let's implement the AI agents' reactions based on the Look-out Table  to handle the AI agent's dialogue:

typedef std::tuple<float, float, float, std::string> LookOutTableRow;

typedef std::vector<LookOutTableRow> LookOutTable;

std::string GetValidBark(const LookOutTable& lookOutTable, float performance) {

    for (const auto& row : lookOutTable) {

        if (CheckRuleValidity(row, performance)) {

            return std::get<3>(row);

        }

    }

    return "No matching condition found.";

}

Defining the Carnival Shooting Gallery Mini-Game:

In the carnival shooting gallery mini-game, the player's objective is to shoot down moving ducks on a chain.

Memory Table: 

Similar to the FPS scenario, we will create a Memory Table to track the player's shooting performance in different attempts of the carnival shooting gallery mini-game.

Attempt	Performance
1	0.2
2	0.5
3	0.7
...	...

The performance is calculated by dividing the successful shots by the total shots.

Look Out Table: AI Agent Barks in the Carnival Game:

The Look Out Table for the carnival game holds the rules and corresponding barks for AI agents, enabling them to react to the player's shooting performance.

Rules	Barks
Attempt1 and performance >= 0.9	"Impossible! You must be a natural."
PerformanceInEarlierAttempt - PerformanceInAttempt >= 0.2	"You're making progress, maybe next try will be the one."
PerformanceInAttempt <= 0.2	"I've seen kids shoot better than you."
...	...

Once again, the AI agent's response is tailored to the player's current performance, providing an encouraging and context-aware gaming experience.

Example dialogue:

Suppose the player initially performs poorly on their first try, prompting the AI to taunt the player and encourage them to try again. On the second attempt, the player shows some improvement. At this point, the AI refers to our table and responds with a bark that acknowledges the progress made, saying something like, "That was a bit better, but I know you can do even better!" However, if the player's performance declines again on the next attempt, the AI reacts accordingly and playfully says, "I thought you were getting better! I guess I was wrong." This approach gives the NPC a sense of awareness towards the player's progress, coaxing them to keep playing and making the gaming experience feel more dynamic and alive.

Improvements for the Performance-Based Barking System:

To further enhance the Performance-Based Barking system, developers can consider the following improvements:

Dynamic Dialogue Selection: Implement a dialogue management system that allows the AI agents to use multiple barks based on specific performance levels, ensuring a more diverse and engaging interaction.

Adaptive AI Behaviors: Integrate Performance-Based Barking with an adaptive AI system, enabling AI agents to adjust their tactics based on the player's shooting performance.

Player Skill Assessment: Incorporate advanced algorithms to assess the player's shooting skill accurately, considering factors such as weapon type, distance, and movement.

Narrative Impact: Extend the system's reach beyond barks and integrate player performance into the game's narrative, influencing character relationships, story outcomes, and mission objectives.

Conclusion:

Performance-Based Barking is a powerful AI system that empowers game developers to create more immersive and interactive gaming experiences. By tracking player performance, designing the Memory Table and Look Out Table, and implementing AI agent reactions, developers can provide players with personalized and engaging gameplay scenarios in a wide range of games. As the player's skills evolve, the AI agents provide tailored feedback, encouragement, or subtle challenges, enriching the player's journey through the virtual worlds. The combination of these components not only enhances the gaming experience but also establishes a deeper connection between players and the game world, making each playthrough an unforgettable and interactive adventure