9,438 research outputs found
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
We seek to create agents that both act and communicate with other agents in
pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)---a
large-scale crowd-sourced fantasy text-game---with a dataset of quests. These
contain natural language motivations paired with in-game goals and human
demonstrations; completing a quest might require dialogue or actions (or both).
We introduce a reinforcement learning system that (1) incorporates large-scale
language modeling-based and commonsense reasoning-based pre-training to imbue
the agent with relevant priors; and (2) leverages a factorized action space of
action commands and dialogue, balancing between the two. We conduct zero-shot
evaluations using held-out human expert demonstrations, showing that our agents
are able to act consistently and talk naturally with respect to their
motivations
Tax morale and tax evasion: Social preferences and bounded rationality
We study a family of models of tax evasion, where a flat-rate tax finances only the provision of public goods, neglecting
audits and wage differences. We focus on the comparison of two modeling approaches. The first is based on optimizing
agents, who are endowed with social preferences, their utility being the sum of private consumption and moral utility. The second approach involves agents acting according to simple heuristics. We find that while we encounter the
traditionally shaped Laffer-curve in the optimizing model, the heuristics models exhibit (linearly) increasing Laffercurves. This difference is related to a peculiar type of behavior emerging within the heuristics based approach: a
number of agents lurk in a moral state of limbo, alternating between altruism and selfishness
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds.
We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations
Challenges in Bridging Social Semantics and Formal Semantics on the Web
This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources
Trust-Based Social Behaviour in Role-Playing Games
Although previous artificial intelligence research has done much to advance video games, not as much has been done to integrate that research into commercially viable titles. In this study, the game Barrel Smasher was developed, demonstrating a socially aware model of Non-Player Characters based on internal trust scores of other characters. The game allows the player to interact with these characters through dialogue, and it provides elements of Role-Playing Games like quests, items, and combat. The result is a game that combines social interactions and other forms of gameplay into a single, connected system. In doing so, the game creates interactive quest progression with more variation than is found in traditional quest systems
Trust-aware Safe Control for Autonomous Navigation: Estimation of System-to-human Trust for Trust-adaptive Control Barrier Functions
A trust-aware safe control system for autonomous navigation in the presence
of humans, specifically pedestrians, is presented. The system combines model
predictive control (MPC) with control barrier functions (CBFs) and trust
estimation to ensure safe and reliable navigation in complex environments.
Pedestrian trust values are computed based on features, extracted from camera
sensor images, such as mutual eye contact and smartphone usage. These trust
values are integrated into the MPC controller's CBF constraints, allowing the
autonomous vehicle to make informed decisions considering pedestrian behavior.
Simulations conducted in the CARLA driving simulator demonstrate the
feasibility and effectiveness of the proposed system, showcasing more
conservative behaviour around inattentive pedestrians and vice versa. The
results highlight the practicality of the system in real-world applications,
providing a promising approach to enhance the safety and reliability of
autonomous navigation systems, especially self-driving vehicles
Expressive movement generation with machine learning
Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
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