Search Results for author: Arnav Jhala

Found 6 papers, 0 papers with code

Emergent social NPC interactions in the Social NPCs Skyrim mod and beyond

no code implementations27 Jul 2022 Manuel Guimarães, Pedro A. Santos, Arnav Jhala

This work presents an implementation of a social architecture model for authoring Non-Player Character (NPC) in open world games inspired in academic research on agentbased modeling.

Modeling Risk in Reinforcement Learning: A Literature Mapping

no code implementations8 Dec 2023 Leonardo Villalobos-Arias, Derek Martin, Abhijeet Krishnan, Madeleine Gagné, Colin M. Potts, Arnav Jhala

Our literature mapping covers literature from the last 5 years (2017-2022), from a variety of knowledge areas (AI, finance, engineering, medicine) where RL approaches emphasize risk representation and management.

Management reinforcement-learning +2

Panel Transitions for Genre Analysis in Visual Narratives

no code implementations14 Dec 2023 Yi-Chun Chen, Arnav Jhala

Our contributions to the community are: a dataset of annotated manga books, a multi-modal analysis of visual panels and text in a constrained and popular medium through high-level features, and a systematic process for incorporating subjective narrative patterns in computational models.

Genre classification

CPST: Comprehension-Preserving Style Transfer for Multi-Modal Narratives

no code implementations14 Dec 2023 Yi-Chun Chen, Arnav Jhala

Among static visual narratives such as comics and manga, there are distinct visual styles in terms of presentation.

Style Transfer

A Customizable Generator for Comic-Style Visual Narrative

no code implementations14 Dec 2023 Yi-Chun Chen, Arnav Jhala

We present a theory-inspired visual narrative generator that incorporates comic-authoring idioms, which transfers the conceptual principles of comics into system layers that integrate the theories to create comic content.

Decision Making

Potential-Based Reward Shaping For Intrinsic Motivation

no code implementations12 Feb 2024 Grant C. Forbes, Nitish Gupta, Leonardo Villalobos-Arias, Colin M. Potts, Arnav Jhala, David L. Roberts

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments.

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