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Found 20 papers, 4 papers with code
Adaptive patch foraging in deep reinforcement learning agents
Patch foraging is one of the most heavily studied behavioral optimization challenges in biology.
Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals
We illustrate Dramatron's usefulness as an interactive co-creative system with a user study of 15 theatre and film industry professionals.
A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning
Two major open research questions in the field of IML are: "How should we design systems that can learn to make better decisions over time with human interaction?"
Learning Robust Real-Time Cultural Transmission without Human Data
We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents.
Generating Diverse Realistic Laughter for Interactive Art
We propose an interactive art project to make those rendered invisible by the COVID-19 crisis and its concomitant solitude reappear through the welcome melody of laughter, and connections created and explored through advanced laughter synthesis approaches.
Automatic Embedding of Stories Into Collections of Independent Media
We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections.
Collaborative Storytelling with Human Actors and AI Narrators
We introduced novel constraints on our language model to produce longer narrative text and tested the model in rehearsals with a team of professional improvisers.
Conceptualization and Framework of Hybrid Intelligence Systems
As artificial intelligence (AI) systems are getting ubiquitous within our society, issues related to its fairness, accountability, and transparency are increasing rapidly.
Women, politics and Twitter: Using machine learning to change the discourse
In this paper, we present ParityBOT: a Twitter bot which counters abusive tweets aimed at women in politics by sending supportive tweets about influential female leaders and facts about women in public life.
A Human-Centered Approach to Interactive Machine Learning
The interactive machine learning (IML) community aims to augment humans' ability to learn and make decisions over time through the development of automated decision-making systems.
Evaluating Coherence in Dialogue Systems using Entailment
Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers.
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives.
Augmenting Neural Response Generation with Context-Aware Topical Attention
Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation.
dAIrector: Automatic Story Beat Generation through Knowledge Synthesis
dAIrector is an automated director which collaborates with humans storytellers for live improvisational performances and writing assistance.
Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre
In this work, we have employed it to control what a subset of human actors say during an improv performance.
Reactive Reinforcement Learning in Asynchronous Environments
The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored.
Communicative Capital for Prosthetic Agents
This work presents an overarching perspective on the role that machine intelligence can play in enhancing human abilities, especially those that have been diminished due to injury or illness.
Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback
In many human-machine interaction settings, there is a growing gap between the degrees-of-freedom of complex semi-autonomous systems and the number of human control channels.
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception
We illustrate the impact of varying human feedback parameters on task performance by investigating the probability of giving feedback on each time step and the likelihood of given feedback being correct.
Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning
This paper contributes a preliminary report on the advantages and disadvantages of incorporating simultaneous human control and feedback signals in the training of a reinforcement learning robotic agent.
