1,223 research outputs found
Representing and Parameterizing Agent Behaviors
The last few years have seen great maturation in understanding how to use computer graphics technology to portray 3D embodied characters or virtual humans. Unlike the off-line, animator-intensive methods used in the special effects industry, real-time embodied agents are expected to exist and interact with us live . They can be represent other people or function as autonomous helpers, teammates, or tutors enabling novel interactive educational and training applications. We should be able to interact and communicate with them through modalities we already use, such as language, facial expressions, and gesture. Various aspects and issues in real-time virtual humans will be discussed, including consistent parameterizations for gesture and facial actions using movement observation principles, and the representational basis for character believability, personality, and affect. We also describe a Parameterized Action Representation (PAR) that allows an agent to act, plan, and reason about its actions or actions of others. Besides embodying the semantics of human action, the PAR is designed for building future behaviors into autonomous agents and controlling the animation parameters that portray personality, mood, and affect in an embodied agent
Hiding variables when decomposing specifications into GR(1) contracts
We propose a method for eliminating variables from component specifications during the decomposition of GR(1) properties into contracts. The variables that can be eliminated are identified by parameterizing the communication architecture to investigate the dependence of realizability on the availability of information. We prove that the selected variables can be hidden from other components, while still expressing the resulting specification as a game with full information with respect to the remaining variables. The values of other variables need not be known all the time, so we hide them for part of the time, thus reducing the amount of information that needs to be communicated between components. We improve on our previous results on algorithmic decomposition of GR(1) properties, and prove existence of decompositions in the full information case. We use semantic methods of computation based on binary decision diagrams. To recover the constructed specifications so that humans can read them, we implement exact symbolic minimal covering over the lattice of integer orthotopes, thus deriving minimal formulae in disjunctive normal form over integer variable intervals
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is a convolutional neural network, trained with a variant
of Q-learning, whose input is raw pixels and whose output is a value function
estimating future rewards. We apply our method to seven Atari 2600 games from
the Arcade Learning Environment, with no adjustment of the architecture or
learning algorithm. We find that it outperforms all previous approaches on six
of the games and surpasses a human expert on three of them.Comment: NIPS Deep Learning Workshop 201
Missing Links: Referrer Behavior and Job Segregation
The importance of networks in labor markets is well-known, and their job segregating effects in organizations taken as granted. Conventional wisdom attributes this segregation to the homophilous nature of contact networks, and leaves little role for organizational influences. But employee referrals are necessarily initiated within a firm by employee referrers subject to organizational policies. We build theory regarding the role of referrers in the segregating effects of network recruitment. Using mathematical and computational models, we investigate how empirically-documented referrer behaviors affect job segregation. We show that referrer behaviors can segregate jobs beyond the effects of homophilous network recruitment. Further, and contrary to past understandings, we show that referrer behaviors can also mitigate most if not all of the segregating effects of network recruitment. Although largely neglected in previous labor market network scholarship, referrers are the missing links revealing opportunities for organizations to influence the effects of network recruitment
Programmable Agents
We build deep RL agents that execute declarative programs expressed in formal
language. The agents learn to ground the terms in this language in their
environment, and can generalize their behavior at test time to execute new
programs that refer to objects that were not referenced during training. The
agents develop disentangled interpretable representations that allow them to
generalize to a wide variety of zero-shot semantic tasks
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
Shaping in humans and animals has been shown to be a powerful tool for
learning complex tasks as compared to learning in a randomized fashion. This
makes the problem less complex and enables one to solve the easier sub task at
hand first. Generating a curriculum for such guided learning involves
subjecting the agent to easier goals first, and then gradually increasing their
difficulty. This paper takes a similar direction and proposes a dual curriculum
scheme for solving robotic manipulation tasks with sparse rewards, called
MaMiC. It includes a macro curriculum scheme which divides the task into
multiple sub-tasks followed by a micro curriculum scheme which enables the
agent to learn between such discovered sub-tasks. We show how combining macro
and micro curriculum strategies help in overcoming major exploratory
constraints considered in robot manipulation tasks without having to engineer
any complex rewards. We also illustrate the meaning of the individual curricula
and how they can be used independently based on the task. The performance of
such a dual curriculum scheme is analyzed on the Fetch environments.Comment: To appear in the Proceedings of the 18th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2019). (Extended Abstract
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