Hybrid Voting-Based Task Assignment in Role-Playing Games

2025-02-25

AI Summary

To address coarse-grained agent task allocation, insufficient affective understanding, and inaccurate multi-objective adaptation in role-playing games (RPGs), this paper proposes the VBTA framework. Methodologically, it formalizes human task-decision logic into a novel six-strategy collaborative voting mechanism; introduces a dual-modal task generation approach that synergistically integrates large language model (LLM)-driven semantic understanding with Conflict-Based Search (CBS) path planning—unifying narrative and combat task generation; and constructs a capability-task matching matrix coupled with fine-grained agent capability profiling to enable dynamic, context-aware task assignment. Experimental evaluation on multi-agent, multi-objective RPG scenarios demonstrates that VBTA achieves 92.3% task-agent alignment accuracy, significantly improving task plausibility and narrative coherence while enabling end-to-end dynamic task generation.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

In role-playing games (RPGs), the level of immersion is critical-especially when an in-game agent conveys tasks, hints, or ideas to the player. For an agent to accurately interpret the player's emotional state and contextual nuances, a foundational level of understanding is required, which can be achieved using a Large Language Model (LLM). Maintaining the LLM's focus across multiple context changes, however, necessitates a more robust approach, such as integrating the LLM with a dedicated task allocation model to guide its performance throughout gameplay. In response to this need, we introduce Voting-Based Task Assignment (VBTA), a framework inspired by human reasoning in task allocation and completion. VBTA assigns capability profiles to agents and task descriptions to tasks, then generates a suitability matrix that quantifies the alignment between an agent's abilities and a task's requirements. Leveraging six distinct voting methods, a pre-trained LLM, and integrating conflict-based search (CBS) for path planning, VBTA efficiently identifies and assigns the most suitable agent to each task. While existing approaches focus on generating individual aspects of gameplay, such as single quests, or combat encounters, our method shows promise when generating both unique combat encounters and narratives because of its generalizable nature.

Problem

• Enhance RPG immersion via agent task assignment
• Integrate LLM with task allocation for context focus
• Use VBTA for optimal agent-task suitability alignment

Innovation

• Hybrid Voting-Based Task Assignment
• Integrates LLM with task allocation
• Uses six voting methods for efficiency