Systematic Evaluation of Multi-modal Approaches to Complex Player Profile Classification

2025-09-06

AI Summary

Existing player modeling approaches rely on coarse-grained behavioral categorization (5–10 classes) or unsupervised, semantically void behavioral clustering, failing to capture motivational heterogeneity. Method: We propose the first multimodal classification framework for fine-grained player profiling—supporting 36 distinct player types—by jointly encoding action sequences and natural language semantic descriptions. We employ an LSTM to model action-text pairs and explicitly incorporate dialogue context to infer player intent. Contribution/Results: Evaluated on 19,413 AI-driven text-based RPG sessions, our method achieves 21% overall accuracy—up from 10% for behavior-only baselines—with 42% accuracy on non-neutral players. We identify and characterize the performance bottleneck of behavioral data under high-class granularity, establish the first 36-class player profiling benchmark, and empirically validate the necessity of conversational interaction for accurate motivation recognition—introducing a novel paradigm for high-dimensional player modeling.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Modern adaptive games require nuanced player understanding, yet most models use simplified 5-10 category taxonomies that fail to capture diversity. Behavioral clustering cannot distinguish players with different motivations who act similarly. We present a systematic evaluation of multi-modal classification at scale, combining behavioral telemetry with semantic context to support 36 player profiles. Using 19,413 gameplay sessions from an AI-controlled text-based RPG, we compared behavioral-only baselines with multi-modal approaches that integrate action sequences and semantic descriptions. Traditional clustering achieved only 10% accuracy for 36-category classification, limited by semantic conflation where opposite actions produced identical features. Our multi-modal LSTM processing action-text pairs improved accuracy to 21%, showing both potential and limits of non-conversational data. Analysis by behavioral complexity revealed that non-neutral profiles reached 42% accuracy (15x above random), while neutral profiles dropped to 25% (9x above random). Identical actions such as "help the merchant" cannot reveal whether a player is neutral or strategically waiting. Without access to reasoning, even multi-modal models struggle, though above-baseline results confirm a meaningful signal. Since prediction beyond 20 categories remains unexplored, our findings establish benchmarks for complex player modeling. Behavioral data alone plateaus near 10% for 36 categories, while multi-modal integration enables 25%. For designers, this shows that personality-based adaptation requires conversational interaction, as predefined choices cannot capture intent. Our evaluation at 36-category scale offers guidance for building adaptive games that better understand their players.

Problem

• Classifying 36 player profiles using multi-modal data beyond simple taxonomies
• Overcoming semantic conflation where opposite actions produce identical features
• Improving accuracy beyond behavioral-only models limited to 10% for complex classification

Innovation

• Combining behavioral telemetry with semantic context
• Multi-modal LSTM processing action-text pairs
• Systematic evaluation at 36-category classification scale