15,520 research outputs found
Affect and believability in game characters:a review of the use of affective computing in games
Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions
Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact usefully
with real-world environments, we need to communicate complex goals to these
systems. In this work, we explore goals defined in terms of (non-expert) human
preferences between pairs of trajectory segments. We show that this approach
can effectively solve complex RL tasks without access to the reward function,
including Atari games and simulated robot locomotion, while providing feedback
on less than one percent of our agent's interactions with the environment. This
reduces the cost of human oversight far enough that it can be practically
applied to state-of-the-art RL systems. To demonstrate the flexibility of our
approach, we show that we can successfully train complex novel behaviors with
about an hour of human time. These behaviors and environments are considerably
more complex than any that have been previously learned from human feedback
Learning Manipulation under Physics Constraints with Visual Perception
Understanding physical phenomena is a key competence that enables humans and
animals to act and interact under uncertain perception in previously unseen
environments containing novel objects and their configurations. In this work,
we consider the problem of autonomous block stacking and explore solutions to
learning manipulation under physics constraints with visual perception inherent
to the task. Inspired by the intuitive physics in humans, we first present an
end-to-end learning-based approach to predict stability directly from
appearance, contrasting a more traditional model-based approach with explicit
3D representations and physical simulation. We study the model's behavior
together with an accompanied human subject test. It is then integrated into a
real-world robotic system to guide the placement of a single wood block into
the scene without collapsing existing tower structure. To further automate the
process of consecutive blocks stacking, we present an alternative approach
where the model learns the physics constraint through the interaction with the
environment, bypassing the dedicated physics learning as in the former part of
this work. In particular, we are interested in the type of tasks that require
the agent to reach a given goal state that may be different for every new
trial. Thereby we propose a deep reinforcement learning framework that learns
policies for stacking tasks which are parametrized by a target structure.Comment: arXiv admin note: substantial text overlap with arXiv:1609.04861,
arXiv:1711.00267, arXiv:1604.0006
Learning Manipulation under Physics Constraints with Visual Perception
Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure
Player agency in interactive narrative: audience, actor & author
The question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, âhow can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?â This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brechtâs Epic Theatre and Boalâs Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds
We describe a method to use discrete human feedback to enhance the
performance of deep learning agents in virtual three-dimensional environments
by extending deep-reinforcement learning to model the confidence and
consistency of human feedback. This enables deep reinforcement learning
algorithms to determine the most appropriate time to listen to the human
feedback, exploit the current policy model, or explore the agent's environment.
Managing the trade-off between these three strategies allows DRL agents to be
robust to inconsistent or intermittent human feedback. Through experimentation
using a synthetic oracle, we show that our technique improves the training
speed and overall performance of deep reinforcement learning in navigating
three-dimensional environments using Minecraft. We further show that our
technique is robust to highly innacurate human feedback and can also operate
when no human feedback is given
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