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Elisheva Bonchek-Dokow. Cognitive Modeling of Human Intention Recognition. Ph.D. Thesis, Bar Ilan University, 2012.
Human beings, from the very young age of 18 months, have been shown to be able to extrapolate intentions from actions. That is, upon viewing another human executing a series of actions, an observing child can guess the underlying intention, even before the goal has been achieved, and even when the performer failed at achieving the goal. In this work, we propose a cognitive model of this human ability, namely, that of intention recognition. The proposed model deals with the challenge of recognizing the intention of an observed sequence of actions, performed by some acting agent. Intention recognition is apparently one of the core components of social cognition. Such a model is therefore important both from a cognitive science point of view and from an engineering perspective. It could provide a deeper understanding of normal and pathological development of human social cognition processes, as well as allowing for artificial implementation of this ability in software agents and physical robots, towards the end of creating more socially intelligent artificial beings. Much work has already been done in all the many areas touching upon this topic, from psychology through neuroscience to artificial intelligence and engineering. In this work we aim to address those aspects of intention recognition which have not yet been treated satisfactorily. We provide a high-level overview of the process as a whole, and detail this model in a way which can explain how failed sequences of actions can be dealt with and their underlying intention extracted, and how novel objects can be dealt with, and goals regarding them predicted, although there is---seemingly---no prior knowledge about them. We elaborate on two components of our proposed model, which we believe to be at its core, namely, those of intention detection and intention prediction. By intention detection we mean the ability to discern whether or not a sequence of actions has any underlying intention at all, or whether it was performed in an arbitrary manner with no goal in mind. By intention prediction we mean the ability to extend an incomplete sequence of actions to its most likely intended goal. The overall structure of the model, i.e. its components and the connections between them, is justified by psychological theories and supported by a plethora of empirical results reviewed in the relevant literature. These theories and experiments are referred to appropriately throughout this work. As for the two core modules on which we elaborate---Intention Detection and Intention Prediction---we present results from several experiments which we have designed and implemented for this purpose. The Intention Detection module is based on a measure of intention, which captures a notion of efficiency, in keeping with the Principle of Rational Action, which states that intentions are brought about by the most rational means available to the actor. This module is validated by two experiments. The first is an artificial emulation of the original intention re-enactment procedure by Meltzoff (1995). The results show that the proposed measure of intention indeed succeeds at categorizing streams of action according to the extent to which they convey an underlying intention. The second experiment validating the Intention Detection module is closer to real life. It uses surveillance videos taken from an online database, and analyzes them according to the proposed measure of intention. This analysis is then compared to human judgment of intention on the same videos. The resulting correlation between the output of our module and that of the human subjects is high, showing once again that our measure of intention indeed captures the notion of intention present in action. Like the Intention Detection module, the Intention Prediction module is based on a measure of intention as well. This measure is also designed to be in line with the Principle of Rational Action, however, it is formalized differently, for reasons which will be discussed. The Intention Prediction module also makes use of the psychological notion of affordances, for extracting goal states from objects in the environment, which the observed actions might possibly be intending to realize. In order to test this second measure of intention as far as its usefulness for predicting intention, we designed an online experiment in which human subjects were presented with abstract objects (various geometric shapes), and were asked to predict the end-configuration of the objects, which observed sequences of movements were aiming to achieve. The predictions arrived at by our measure of intention were compared to the human results, and proved to be highly reliable. Other possible measures, such as proximity of the terminal state arrived at by the actions to the various goals, were also considered. However the success of these measures at predicting the intended goal was inferior to that of our measure of intention, and they at most play a secondary role in the process. To conclude our work, we summarize our findings and propose several directions for future research on intention modeling. We hope this work will be of interest and of use to researchers in the multidisciplinary communities dealing with intention recognition, and look forward to seeing the ideas proposed here implemented in socially cognitive artificial systems.
@PhdThesis{elisheva-phd,
author = {Elisheva Bonchek-Dokow},
title = {Cognitive Modeling of Human Intention Recognition},
school = {{B}ar {I}lan {U}niversity},
year = {2012},
wwwnote = {},
OPTannote = {} ,
abstract = {
Human beings, from the very young age of 18 months, have been shown to be able to extrapolate intentions from
actions. That is, upon viewing another human executing a series of actions, an observing
child can guess the underlying intention, even before the goal has been achieved, and even when the performer
failed at achieving the goal. In this work, we propose a cognitive model of this human ability, namely, that
of intention recognition.
The proposed model deals with the challenge of recognizing the intention of an observed sequence of actions,
performed by some acting agent. Intention recognition is apparently one of the core components of social
cognition. Such a model is therefore important both from a cognitive science point of view and from an
engineering perspective. It could provide a deeper understanding of normal and pathological development
of human social cognition processes, as well as allowing for artificial implementation of this ability
in software agents and physical robots, towards the end of creating more socially intelligent artificial
beings.
Much work has already been done in all the many areas touching upon this topic, from psychology through
neuroscience to artificial intelligence and engineering. In this work we aim to address those aspects
of intention recognition which have not yet been treated satisfactorily. We provide a high-level
overview of the process as a whole, and detail this model in a way which can explain how \emph{failed}
sequences of actions can be dealt with and their underlying intention extracted, and how \emph{novel}
objects can be dealt with, and goals regarding them predicted, although there is---seemingly---no
prior knowledge about them.
We elaborate on two components of our proposed model, which we believe to be at its core, namely,
those of intention detection and intention prediction. By \emph{intention detection} we mean the
ability to discern whether or not a sequence of actions has any underlying intention at all, or
whether it was performed in an arbitrary manner with no goal in mind. By \emph{intention prediction}
we mean the ability to extend an incomplete sequence of actions to its most likely intended goal.
The overall structure of the model, i.e. its components and the connections between them, is justified
by psychological theories and supported by a plethora of empirical results reviewed in the relevant
literature. These theories and experiments are referred to appropriately throughout this work. As
for the two core modules on which we elaborate---Intention Detection and Intention Prediction---we
present results from several experiments which we have designed and implemented for this purpose.
The Intention Detection module is based on a measure of intention, which captures a notion of efficiency,
in keeping with the Principle of Rational Action, which states that intentions
are brought about by the most rational means available to the actor. This module is validated by two
experiments. The first is an artificial emulation of the original intention re-enactment procedure
by Meltzoff (1995). The results show that the proposed measure of intention indeed succeeds at
categorizing streams of action according to the extent to which they convey an underlying intention.
The second experiment validating the Intention Detection module is closer to real life. It uses
surveillance videos taken from an online database, and analyzes them according to the proposed measure
of intention. This analysis is then compared to human judgment of intention on the same videos. The
resulting correlation between the output of our module and that of the human subjects is high, showing
once again that our measure of intention indeed captures the notion of intention present in action.
Like the Intention Detection module, the Intention Prediction module is based on a measure of intention
as well. This measure is also designed to be in line with the Principle of Rational Action, however,
it is formalized differently, for reasons which will be discussed. The Intention Prediction module also
makes use of the psychological notion of \emph{affordances}, for extracting goal states from objects in
the environment, which the observed actions might possibly be intending to realize.
In order to test this second measure of intention as far as its usefulness for predicting intention, we
designed an online experiment in which human subjects were presented with abstract objects (various
geometric shapes), and were asked to predict the end-configuration of the objects, which observed
sequences of movements were aiming to achieve. The predictions arrived at by our measure of intention
were compared to the human results, and proved to be highly reliable. Other possible measures, such as
proximity of the terminal state arrived at by the actions to the various goals, were also considered.
However the success of these measures at predicting the intended goal was inferior to that of our measure
of intention, and they at most play a secondary role in the process.
To conclude our work, we summarize our findings and propose several directions for future research on
intention modeling. We hope this work will be of interest and of use to researchers in the multidisciplinary
communities dealing with intention recognition, and look forward to seeing the ideas proposed here
implemented in socially cognitive artificial systems.
},
}
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