ajithksenthil/personalitymediatednarrativegen

Using Markov Chains and Computational Psychodynamics, a process-based approach to modeling event driven personality and cognition, in generative story worlds

Personality Mediated Generative Story Worlds

Introduction

Drawing inspiration from computational linguistics and computational psychodynamics, this project dives into the realm of generative story worlds. At its core is the modeling of event-driven personality and cognition to create dynamic narratives.

Computational Psychodynamics

This methodology bridges the gap between computational linguistics and behavior modeling. Just as computational linguistics combines linguistic schemas with statistical models, computational psychodynamics offers a structured schema for understanding behavior and cognition, using tools such as Markov chains and Transformer models. Key principles include:

Behavioral schema rooted in Jungian cognitive functions

The Free Energy Principle-Active Inference (FEP-ActInf) framework

Linearly separable binary classifications for behavioral states

Transition modeling between behavioral states over time

Features

Dynamic Modeling: Using the principles of Computational Psychodynamics, this project captures the nuances of personality and behavior in generative story worlds.

AI Integration: The methodologies emphasize potential AI applications, especially in portraying behavior and personality through multi-level binary classifications.

Ecological View of Personality: Observations of behaviors form the main source of data, ensuring a more grounded and holistic understanding of personality dynamics.

How It Works

Behavioral Schema: At the foundation is a behavioral schema based on Jungian cognitive functions, categorizing judgment/observer functions with perception/decider functions.

FEP-ActInf Integration: The schema is connected with the FEP-ActInf framework, emphasizing the brain's role in reducing differences between predicted and actual sensory inputs.

Transition Modeling: Behavioral transitions over time are modeled using Markov chains, Recurrent Neural Networks, and Transformer models.

Generative Narratives: The behavioral models are then utilized to create dynamic narratives, reflecting realistic personality-driven scenarios. Applications

Brain-computer interfaces: Refining cognitive models for improved interaction and understanding.

AI personalities: Create more human-like AI entities with distinct personalities. Generative narrative worlds: Craft intricate story worlds driven by dynamic characters. Behavior prediction & analysis: Enhance the prediction and analysis of behavior over time.

Related Manuscripts in Preparation

Computational Psychodynamics: Process-Based Framework for Modeling Cognition and Personality Using Active Inference: Delve deeper into the theory of Computational Psychodynamics and its applications in modeling event-driven personality and cognition in generative story worlds.