244,791 research outputs found
VirtualHome: Simulating Household Activities via Programs
In this paper, we are interested in modeling complex activities that occur in
a typical household. We propose to use programs, i.e., sequences of atomic
actions and interactions, as a high level representation of complex tasks.
Programs are interesting because they provide a non-ambiguous representation of
a task, and allow agents to execute them. However, nowadays, there is no
database providing this type of information. Towards this goal, we first
crowd-source programs for a variety of activities that happen in people's
homes, via a game-like interface used for teaching kids how to code. Using the
collected dataset, we show how we can learn to extract programs directly from
natural language descriptions or from videos. We then implement the most common
atomic (inter)actions in the Unity3D game engine, and use our programs to
"drive" an artificial agent to execute tasks in a simulated household
environment. Our VirtualHome simulator allows us to create a large activity
video dataset with rich ground-truth, enabling training and testing of video
understanding models. We further showcase examples of our agent performing
tasks in our VirtualHome based on language descriptions.Comment: CVPR 2018 (Oral
Learning to Speak and Act in a Fantasy Text Adventure Game
We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully
Feedback, Learning Outcomes and Mathematics Anxiety in a Digital Game Based Learning Approach in Mathematics Education
Feedback is a crucial part of learning, and an essential element in digital game-based learning approaches, in which digital games - known as \u27serious games\u27 - are used to deliver educational content. Feedback features respond to players\u27 actions within the game, providing them with information and guidance, as well as potentially impacting their learning, motivation and engagement. However, these features may be designed differently, since they include various distinct characteristics and dimensions. This work proposes a new taxonomy for feedback features in serious games, with an emphasis in game design aspects, in order to provide clearer descriptions and distinctions of different feedback characteristics.https://arrow.tudublin.ie/cddpos/1005/thumbnail.jp
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different
tasks in an autonomous agent, such as a robot or a virtual entity in a computer
game. BTs are a very efficient way of creating complex systems that are both
modular and reactive. These properties are crucial in many applications, which
has led to the spread of BT from computer game programming to many branches of
AI and Robotics. In this book, we will first give an introduction to BTs, then
we describe how BTs relate to, and in many cases generalize, earlier switching
structures. These ideas are then used as a foundation for a set of efficient
and easy to use design principles. Properties such as safety, robustness, and
efficiency are important for an autonomous system, and we describe a set of
tools for formally analyzing these using a state space description of BTs. With
the new analysis tools, we can formalize the descriptions of how BTs generalize
earlier approaches. We also show the use of BTs in automated planning and
machine learning. Finally, we describe an extended set of tools to capture the
behavior of Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success probabilities
and time to completion
Discovering User-Interpretable Capabilities of Black-Box Planning Agents
Several approaches have been developed for answering users' specific
questions about AI behavior and for assessing their core functionality in terms
of primitive executable actions. However, the problem of summarizing an AI
agent's broad capabilities for a user is comparatively new. This paper presents
an algorithm for discovering from scratch the suite of high-level
"capabilities" that an AI system with arbitrary internal planning
algorithms/policies can perform. It computes conditions describing the
applicability and effects of these capabilities in user-interpretable terms.
Starting from a set of user-interpretable state properties, an AI agent, and a
simulator that the agent can interact with, our algorithm returns a set of
high-level capabilities with their parameterized descriptions. Empirical
evaluation on several game-based scenarios shows that this approach efficiently
learns descriptions of various types of AI agents in deterministic, fully
observable settings. User studies show that such descriptions are easier to
understand and reason with than the agent's primitive actions.Comment: KR 202
The Plame Game: framing a political scandal
The media play an important role in society. They interpret political events, actions, policies, and scandals in a manner that citizens can understand. The media use frames to assist in interpretations and descriptions. They may create their own frames or use frames supplied by the political elites. Frames can also lead to biased coverage when used to omit details or present someone in a favorable or unfavorable manner. This study examines the frames the media used during the coverage of President George W. Bush’s first political scandal, the “Plame Game.” On July 14, 2003, Robert Novak exposed the identity of CIA agent Valerie Plame in his syndicated editorial column. Over the next five years the media followed the “Plame Game” scandal using frames to describe the actors and their actions. A content analysis of three national newspapers shows that the media did use frames in their coverage of this political scandal. The media used frames they created and some that political elites gave them through interviews and press releases. Over the five years, the frames associated with each actor in the “Plame Game” did change. Even though some individual articles are biased in their coverage of the actors in the scandal, statistical results prove that the cumulative coverage of the “Plame Game” was balanced. This means that an equal number of positive and negative frames were used to describe each actor and their actions over the course of five years. Little research deals with media framing of political scandals. The results of this study can aid in future research of political scandal framing, and can extend the already existing wealth of framing research
Language Understanding for Text-based Games Using Deep Reinforcement Learning
In this paper, we consider the task of learning control policies for
text-based games. In these games, all interactions in the virtual world are
through text and the underlying state is not observed. The resulting language
barrier makes such environments challenging for automatic game players. We
employ a deep reinforcement learning framework to jointly learn state
representations and action policies using game rewards as feedback. This
framework enables us to map text descriptions into vector representations that
capture the semantics of the game states. We evaluate our approach on two game
worlds, comparing against baselines using bag-of-words and bag-of-bigrams for
state representations. Our algorithm outperforms the baselines on both worlds
demonstrating the importance of learning expressive representations.Comment: 11 pages, Appearing at EMNLP, 201
Ludii -- The Ludemic General Game System
While current General Game Playing (GGP) systems facilitate useful research
in Artificial Intelligence (AI) for game-playing, they are often somewhat
specialised and computationally inefficient. In this paper, we describe the
"ludemic" general game system Ludii, which has the potential to provide an
efficient tool for AI researchers as well as game designers, historians,
educators and practitioners in related fields. Ludii defines games as
structures of ludemes -- high-level, easily understandable game concepts --
which allows for concise and human-understandable game descriptions. We
formally describe Ludii and outline its main benefits: generality,
extensibility, understandability and efficiency. Experimentally, Ludii
outperforms one of the most efficient Game Description Language (GDL)
reasoners, based on a propositional network, in all games available in the
Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of
performance with the more recently proposed Regular Boardgames (RBG) system,
and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202
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