137 research outputs found
Stabilizing Estimates of Shapley Values with Control Variates
Shapley values are among the most popular tools for explaining predictions of
blackbox machine learning models. However, their high computational cost
motivates the use of sampling approximations, inducing a considerable degree of
uncertainty. To stabilize these model explanations, we propose ControlSHAP, an
approach based on the Monte Carlo technique of control variates. Our
methodology is applicable to any machine learning model and requires virtually
no extra computation or modeling effort. On several high-dimensional datasets,
we find it can produce dramatic reductions in the Monte Carlo variability of
Shapley estimates
Iteratively Learn Diverse Strategies with State Distance Information
In complex reinforcement learning (RL) problems, policies with similar
rewards may have substantially different behaviors. It remains a fundamental
challenge to optimize rewards while also discovering as many diverse strategies
as possible, which can be crucial in many practical applications. Our study
examines two design choices for tackling this challenge, i.e., diversity
measure and computation framework. First, we find that with existing diversity
measures, visually indistinguishable policies can still yield high diversity
scores. To accurately capture the behavioral difference, we propose to
incorporate the state-space distance information into the diversity measure. In
addition, we examine two common computation frameworks for this problem, i.e.,
population-based training (PBT) and iterative learning (ITR). We show that
although PBT is the precise problem formulation, ITR can achieve comparable
diversity scores with higher computation efficiency, leading to improved
solution quality in practice. Based on our analysis, we further combine ITR
with two tractable realizations of the state-distance-based diversity measures
and develop a novel diversity-driven RL algorithm, State-based Intrinsic-reward
Policy Optimization (SIPO), with provable convergence properties. We
empirically examine SIPO across three domains from robot locomotion to
multi-agent games. In all of our testing environments, SIPO consistently
produces strategically diverse and human-interpretable policies that cannot be
discovered by existing baselines
Towards an architectural framework for intelligent virtual agents using probabilistic programming
We present a new framework called KorraAI for conceiving and building
embodied conversational agents (ECAs). Our framework models ECAs' behavior
considering contextual information, for example, about environment and
interaction time, and uncertain information provided by the human interaction
partner. Moreover, agents built with KorraAI can show proactive behavior, as
they can initiate interactions with human partners. For these purposes, KorraAI
exploits probabilistic programming. Probabilistic models in KorraAI are used to
model its behavior and interactions with the user. They enable adaptation to
the user's preferences and a certain degree of indeterminism in the ECAs to
achieve more natural behavior. Human-like internal states, such as moods,
preferences, and emotions (e.g., surprise), can be modeled in KorraAI with
distributions and Bayesian networks. These models can evolve over time, even
without interaction with the user. ECA models are implemented as plugins and
share a common interface. This enables ECA designers to focus more on the
character they are modeling and less on the technical details, as well as to
store and exchange ECA models. Several applications of KorraAI ECAs are
possible, such as virtual sales agents, customer service agents, virtual
companions, entertainers, or tutors
Resilience, reliability, and coordination in autonomous multi-agent systems
Acknowledgements The research reported in this paper was funded and supported by various grants over the years: Robotics and AI in Nuclear (RAIN) Hub (EP/R026084/1); Future AI and Robotics for Space (FAIR-SPACE) Hub (EP/R026092/1); Offshore Robotics for Certification of Assets (ORCA) Hub (EP/R026173/1); the Royal Academy of Engineering under the Chair in Emerging Technologies scheme; Trustworthy Autonomous Systems “Verifiability Node” (EP/V026801); Scrutable Autonomous Systems (EP/J012084/1); Supporting Security Policy with Effective Digital Intervention (EP/P011829/1); The International Technology Alliance in Network and Information Sciences.Peer reviewedPostprin
Modelling the relationship between gesture motion and meaning
There are many ways to say “Hello,” be it a wave, a nod, or a bow. We greet others not only with words, but also with our bodies. Embodied communication permeates our interactions. A fist bump, thumbs-up, or pat on the back can be even more meaningful than hearing “good job!” A friend crossing their arms with a scowl, turning away from you, or stiffening up can feel like a harsh rejection. Social communication is not exclusively linguistic, but is a multi-sensory affair. It’s not that communication without these bodily cues is impossible, but it is impoverished. Embodiment is a fundamental human experience.
Expressing ourselves through our bodies provides a powerful channel through which we express a plethora of meta-social information. And integral to communication, expression, and social engagement is our utilization of conversational gesture. We use gestures to express extra-linguistic information, to emphasize our point, and to embody mental and linguistic metaphors that add depth and color to social interaction.
The gesture behaviour of virtual humans when compared to human-human conversation is limited, depending on the approach taken to automate performances of these characters. The generation of nonverbal behaviour for virtual humans can be approximately classified as either: 1) data-driven approaches that learn a mapping from aspects of the verbal channel, such as prosody, to gestures; or 2) rule bases approaches that are often tailored by designers for specific applications.
This thesis is an interdisciplinary exploration that bridges these two approaches, and brings data-driven analyses to observational gesture research. By marrying a rich history of gesture research in behavioral psychology with data-driven techniques, this body of work brings rigorous computational methods to gesture classification, analysis, and generation. It addresses how researchers can exploit computational methods to make virtual humans gesture with the same richness, complexity, and apparent effortlessness as you and I. Throughout this work the central focus is on metaphoric gestures. These gestures are capable of conveying rich, nuanced, multi-dimensional meaning, and raise several challenges in their generation, including establishing and interpreting a gesture’s communicative meaning, and selecting a performance to convey it. As such, effectively utilizing these gestures remains an open challenge in virtual agent research. This thesis explores how metaphoric gestures are interpreted by an observer, how one can generate such rich gestures using a mapping between utterance meaning and gesture, as well as how one can use data driven techniques to explore the mapping between utterance and metaphoric gestures.
The thesis begins in Chapter 1 by outlining the interdisciplinary space of gesture research in psychology and generation in virtual agents. It then presents several studies that address presupposed assumptions raised about the need for rich, metaphoric gestures and the risk of false implicature when gestural meaning is ignored in gesture generation. In Chapter 2, two studies on metaphoric gestures that embody multiple metaphors argue three critical points that inform the rest of the thesis: that people form rich inferences from metaphoric gestures, these inferences are informed by cultural context and, more importantly, that any approach to analyzing the relation between utterance and metaphoric gesture needs to take into account that multiple metaphors may be conveyed by a single gesture. A third study presented in Chapter 3 highlights the risk of false implicature and discusses this in the context of current subjective evaluations of the qualitative influence of gesture on viewers.
Chapters 4 and 5 then present a data-driven analysis approach to recovering an interpretable explicit mapping from utterance to metaphor. The approach described in detail in Chapter 4 clusters gestural motion and relates those clusters to the semantic analysis of associated utterance. Then, Chapter 5 demonstrates how this approach can be used both as a framework for data-driven techniques in the study of gesture as well as form the basis of a gesture generation approach for virtual humans.
The framework used in the last two chapters ties together the main themes of this thesis: how we can use observational behavioral gesture research to inform data-driven analysis methods, how embodied metaphor relates to fine-grained gestural motion, and how to exploit this relationship to generate rich, communicatively nuanced gestures on virtual agents. While gestures show huge variation, the goal of this thesis is to start to characterize and codify that variation using modern data-driven techniques.
The final chapter of this thesis reflects on the many challenges and obstacles the field of gesture generation continues to face. The potential for applications of Virtual Agents to have broad impacts on our daily lives increases with the growing pervasiveness of digital interfaces, technical breakthroughs, and collaborative interdisciplinary research efforts. It concludes with an optimistic vision of applications for virtual agents with deep models of non-verbal social behaviour and their potential to encourage multi-disciplinary collaboration
Sketching for Real-time Control of Crowd Simulations
Controlling the behaviour of a crowd simulation typically involves tuning of a system's parameters through trial and error, a time-consuming process relying on knowledge of a potentially complex parameter set. Numerous graphical control approaches have been proposed to allow the user to interact with a simulation intuitively. This research investigates the use of a real-time sketch-based approach for crowd simulation control. This is done by modifying the environment of the simulation. Users can create entrances/exits, barriers and flow lines in real-time on top of an environment. This process requires a data structure to represent the environment and navigate the crowd through it. Two alternatives are presented: grid and navigation mesh. A detailed comparison shows that the navigation mesh is a more scalable approach since it uses less memory, has a similar pathfinding time, and is a better structure to represent the environment than the grid.
The thesis also presents extensions to the sketch-based approach in the form of novel control tools, including storyboards to define the journey of the crowd, a timeline interface to simulate events through the day, and a sketch-based group storyboard to link behaviours and paths to be followed by a group. These tools are used to create two complex scenarios to exemplify possible applications of the sketch-based approach. The work on timelines also raises a new problem for an approach that dynamically modifies an environment in real-time which is 'when does the crowd know about the change?' Some initial solutions to how this should be handled are presented.
The sketch-based system is evaluated by comparing it to a validated commercial system called MassMotion. The comparison takes into account the plausibility of the simulation and usability of the user interface. A user study is carried out to evaluate the graphical user interface of both systems. Formal evaluation methods are used to make the comparison: the benchmark suite 'steersuite', an adapted version of the Keystroke-Level Model (KLM) and the System Usability Scale (SUS). The results show that the sketch-based approach is faster and easier to use than MassMotion, but with fewer control options. An implementation of the sketching interface in a Virtual Reality environment is also considered. However, when compared to the desktop interface using a proposed adaptation to KLM for VR, the results show that sketching in a VR environment is slower and less accurate than the desktop version
Goal reasoning for autonomous agents using automated planning
Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely
a plan, which achieves a goal from a given initial state. Most planning research
consider goals are provided by a external user, and agents just have to find a
plan to achieve them. However, there exist many real world domains where
agents should not only reason about their actions but also about their goals,
generating new ones or changing them according to the perceived environment.
In this thesis we aim at broadening the goal reasoning capabilities of planningbased
agents, both when acting in isolation and when operating in the same
environment as other agents.
In single-agent settings, we firstly explore a special type of planning tasks
where we aim at discovering states that fulfill certain cost-based requirements
with respect to a given set of goals. By computing these states, agents are able
to solve interesting tasks such as find escape plans that move agents in to safe
places, hide their true goal to a potential observer, or anticipate dynamically arriving
goals. We also show how learning the environment’s dynamics may help
agents to solve some of these tasks. Experimental results show that these states
can be quickly found in practice, making agents able to solve new planning
tasks and helping them in solving some existing ones.
In multi-agent settings, we study the automated generation of goals based on
other agents’ behavior. We focus on competitive scenarios, where we are interested
in computing counterplans that prevent opponents from achieving their
goals. We frame these tasks as counterplanning, providing theoretical properties
of the counterplans that solve them. We also show how agents can benefit
from computing some of the states we propose in the single-agent setting to
anticipate their opponent’s movements, thus increasing the odds of blocking
them. Experimental results show how counterplans can be found in different
environments ranging from competitive planning domains to real-time strategy
games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert
Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review
Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years.
On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance.
Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones
GROVE: A computationally grounded model for rational intention revision in BDI agents
A fundamental aspect of Belief-Desire-Intention (BDI) agents is intention revision. Agents revise their intentions in order to maintain consistency between their intentions and beliefs, and consistency between intentions. A rational agent must also account for the optimality of their intentions in the case of revision. To that end I present GROVE, a model of rational intention revision for BDI agents. The semantics of a GROVE agent is defined in terms of constraints and preferences on possible future executions of an agent’s plans. I show that GROVE is weakly rational in the sense of Grant et al. and imposes more constraints on executions than the operational semantics for goal lifecycles proposed by Harland et al. As it may not be computationally feasible to consider all possible future executions, I propose a bounded version of GROVE that samples the set of future executions, and state conditions under which bounded GROVE commits to a rational execution
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