358 research outputs found
Learning object behaviour models
The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models.
The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy.
This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences.
The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine
Cybernetics of the mind:learning individual's perceptions autonomously
In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments
Cybernetics of the mind:learning individual's perceptions autonomously
In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments
Predictability, complexity and learning
We define {\em predictive information} as the mutual
information between the past and the future of a time series. Three
qualitatively different behaviors are found in the limit of large observation
times : can remain finite, grow logarithmically, or grow
as a fractional power law. If the time series allows us to learn a model with a
finite number of parameters, then grows logarithmically with
a coefficient that counts the dimensionality of the model space. In contrast,
power--law growth is associated, for example, with the learning of infinite
parameter (or nonparametric) models such as continuous functions with
smoothness constraints. There are connections between the predictive
information and measures of complexity that have been defined both in learning
theory and in the analysis of physical systems through statistical mechanics
and dynamical systems theory. Further, in the same way that entropy provides
the unique measure of available information consistent with some simple and
plausible conditions, we argue that the divergent part of
provides the unique measure for the complexity of dynamics underlying a time
series. Finally, we discuss how these ideas may be useful in different problems
in physics, statistics, and biology.Comment: 53 pages, 3 figures, 98 references, LaTeX2
Forking Uncertainties:Reliable Prediction and Model Predictive Control With Sequence Models via Conformal Risk Control
In many real-world problems, predictions are leveraged to monitor and control cyber-physical systems, demanding guarantees on the satisfaction of reliability and safety requirements. However, predictions are inherently uncertain, and managing prediction uncertainty presents significant challenges in environments characterized by complex dynamics and forking trajectories. In this work, we assume access to a pre-designed probabilistic implicit or explicit sequence model, which may have been obtained using model-based or model-free methods. We introduce probabilistic time series-conformal risk prediction (PTS-CRC), a novel post-hoc calibration procedure that operates on the predictions produced by any pre-designed probabilistic forecaster to yield reliable error bars. In contrast to existing art, PTS-CRC produces predictive sets based on an ensemble of multiple prototype trajectories sampled from the sequence model, supporting the efficient representation of forking uncertainties. Furthermore, unlike the state of the art, PTS-CRC can satisfy reliability definitions beyond coverage. This property is leveraged to devise a novel model predictive control (MPC) framework that addresses open-loop and closed-loop control problems under general average constraints on the quality or safety of the control policy. We experimentally validate the performance of PTS-CRC prediction and control by studying a number of use cases in the context of wireless networking. Across all the considered tasks, PTS-CRC predictors are shown to provide more informative predictive sets, as well as safe control policies with larger returns
Bayesian models of category acquisition and meaning development
The ability to organize concepts (e.g., dog, chair) into efficient mental representations,
i.e., categories (e.g., animal, furniture) is a fundamental mechanism which allows humans
to perceive, organize, and adapt to their world. Much research has been dedicated
to the questions of how categories emerge and how they are represented. Experimental
evidence suggests that (i) concepts and categories are represented through sets of
features (e.g., dogs bark, chairs are made of wood) which are structured into different
types (e.g, behavior, material); (ii) categories and their featural representations are
learnt jointly and incrementally; and (iii) categories are dynamic and their representations
adapt to changing environments.
This thesis investigates the mechanisms underlying the incremental and dynamic formation
of categories and their featural representations through cognitively motivated
Bayesian computational models. Models of category acquisition have been extensively
studied in cognitive science and primarily tested on perceptual abstractions or artificial
stimuli. In this thesis, we focus on categories acquired from natural language stimuli,
using nouns as a stand-in for their reference concepts, and their linguistic contexts as
a representation of the concepts’ features. The use of text corpora allows us to (i) develop
large-scale unsupervised models thus simulating human learning, and (ii) model
child category acquisition, leveraging the linguistic input available to children in the
form of transcribed child-directed language.
In the first part of this thesis we investigate the incremental process of category acquisition.
We present a Bayesian model and an incremental learning algorithm which
sequentially integrates newly observed data. We evaluate our model output against
gold standard categories (elicited experimentally from human participants), and show
that high-quality categories are learnt both from child-directed data and from large,
thematically unrestricted text corpora. We find that the model performs well even under
constrained memory resources, resembling human cognitive limitations. While
lists of representative features for categories emerge from this model, they are neither
structured nor jointly optimized with the categories.
We address these shortcomings in the second part of the thesis, and present a Bayesian
model which jointly learns categories and structured featural representations. We
present both batch and incremental learning algorithms, and demonstrate the model’s
effectiveness on both encyclopedic and child-directed data. We show that high-quality
categories and features emerge in the joint learning process, and that the structured
features are intuitively interpretable through human plausibility judgment evaluation.
In the third part of the thesis we turn to the dynamic nature of meaning: categories and
their featural representations change over time, e.g., children distinguish some types
of features (such as size and shade) less clearly than adults, and word meanings adapt
to our ever changing environment and its structure. We present a dynamic Bayesian
model of meaning change, which infers time-specific concept representations as a set
of feature types and their prevalence, and captures their development as a smooth process.
We analyze the development of concept representations in their complexity over
time from child-directed data, and show that our model captures established patterns of
child concept learning. We also apply our model to diachronic change of word meaning,
modeling how word senses change internally and in prevalence over centuries.
The contributions of this thesis are threefold. Firstly, we show that a variety of experimental
results on the acquisition and representation of categories can be captured
with computational models within the framework of Bayesian modeling. Secondly,
we show that natural language text is an appropriate source of information for modeling
categorization-related phenomena suggesting that the environmental structure that
drives category formation is encoded in this data. Thirdly, we show that the experimental
findings hold on a larger scale. Our models are trained and tested on a larger
set of concepts and categories than is common in behavioral experiments and the categories
and featural representations they can learn from linguistic text are in principle
unrestricted
Learning plan networks in conversational video games
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 121-123).We look forward to a future where robots collaborate with humans in the home and workplace, and virtual agents collaborate with humans in games and training simulations. A representation of common ground for everyday scenarios is essential for these agents if they are to be effective collaborators and communicators. Effective collaborators can infer a partner's goals and predict future actions. Effective communicators can infer the meaning of utterances based on semantic context. This thesis introduces a computational cognitive model of common ground called a Plan Network. A Plan Network is a statistical model that provides representations of social roles, object affordances, and expected patterns of behavior and language. I describe a methodology for unsupervised learning of a Plan Network using a multiplayer video game, visualization of this network, and evaluation of the learned model with respect to human judgment of typical behavior. Specifically, I describe learning the Restaurant Plan Network from data collected from over 5,000 players of an online game called The Restaurant Game.by Jeffrey David Orkin.S.M
- …