My research on interactive AI methods seeks to give users extensive control over reasoning
and learning processes. In many critical applications, especially in military and medical
domains, users will reject traditional AI automation without the ability for each result to
be checked and altered by a human operator. Interactive AI methods incorporate such levels
of user control to facilitate the transition of AI into these types of applications. This
interactive AI paradigm combines user-driven control with the complementary system-driven
approach of active learning.
A Manifold-Based Approach to Interactive Learning
At Lockheed Martin, I led the development of an interactive AI method based on manifold
learning (left figure) that trains a regression function in collaboration with the user
(Eaton et al., 2010).
This approach was applied to a naval system used to ensure the safety of shipping ports —
a critical application in which watchstanders require the ability to rapidly adjust the
model in response to changing mission requirements. This method generalizes user feedback on
individual vessels to alter the model in an intuitive manner that monotonically improves
performance with any correction, providing the first such guarantee of any interactive
learning method. This technique could also be applied to other systems used by network
security analysts, stock traders, and crisis monitoring centers.
Semi-Supervised Community Detection
My group also developed an interactive method (right figure) for incorporating user guidance
and background knowledge into the community detection process using a semi-supervised
spin-glass model (Eaton & Mansbach, 2012).
We focused on scenarios in which there was noise in the relational network, and showed that
popular modularity-based community detection algorithms perform poorly as the network
becomes increasingly noisy. We showed that semi-supervision could be integrated into a
spin-glass model for community detection, providing robust performance in noisy networks.
We also showed that this semi-supervised spin-glass model yields an alternate form of
Newman-Girvan graph modularity that incorporates background information, enabling existing
modularity-based community detection algorithms to be easily modified to incorporate
semi-supervision.
Modeling User Preferences over Sets of Objects
In collaboration with Marie desJardins (UMBC) and Kiri Wagstaff (NASA Jet Propulsion Laboratory), I developed the DDPref framework for learning and reasoning about a user’s preferences for selecting sets of objects where items in the set interact (desJardins et al., 2006); (Wagstaff et al., 2010). The DDPref representation captures interactions between items within a set, modeling the user’s desired levels of quality and diversity of items in the set. Our approach allows a user to either manually specify a preference representation, or select example sets that represent their desired information, from which we can learn a representation of their preferences.
We applied the DDPref method to identify sets of images taken by a remote Mars rover for transmission back to the user. Due to the limited communications bandwidth, it is important to send back a set of images which together captures the user’s desired information. This research is also applicable to search result set creation, automatic content presentation, and targeted information acquisition.
Current modularity-based community detection methods
show decreased performance as relational networks
become increasingly noisy. These methods also yield
a large number of diverse community structures as
solutions, which is problematic for applications
that impose constraints on the acceptable solutions
or in cases where the user is focused on specific
communities of interest. To address both of these
problems, we develop a semi-supervised spin-glass
model that enables current community detection
methods to incorporate background knowledge in the
forms of individual labels and pairwise constraints.
Unlike current methods, our approach shows robust
performance in the presence of noise in the
relational network, and the ability to guide the
discovery process toward specific community
structures. We evaluate our algorithm on several
benchmark networks and a new political sentiment
network representing cooperative events between
nations that was mined from news articles over
six years.
@inproceedings{Eaton2012SpinGlass,author={Eaton, Eric and Mansbach, Rachael},title={A spin-glass model for semi-supervised community detection},booktitle={Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-12)},month=jul,location={Toronto, Canada},publisher={AAAI Press},pages={900--906},year={2012},bib2html_dl_poster={papers/poster-Eaton2012SpinGlass.pdf},bib2html_dl_code={software/ICD-2011v0.5.zip}}
We present an interactive learning method that enables
a user to iteratively refine a regression model. The
user examines the output of the model, visualized as
the vertical axis of a 2D scatterplot, and provides
corrections by repositioning individual data instances
to the correct output level. Each repositioned data
instance acts as a control point for altering the
learned model, using the geometry underlying the data.
We capture the underlying structure of the data as a
manifold, on which we compute a set of basis functions
as the foundation for learning. Our results show that
manifold-based interactive learning improves performance
monotonically with each correction, outperforming
alternative approaches.
@inproceedings{Eaton2010Interactive,author={Eaton, Eric and Holness, Gary and McFarlane, Daniel},title={Interactive Learning using Manifold Geometry},booktitle={Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-10)},month=jul,location={Atlanta, GA},publisher={AAAI Press},pages={437--443},year={2010},bib2html_dl_slides={papers/Eaton2010Interactive-Slides.ppt}}
Although there has been significant research on modeling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional object- and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises.
@article{Wagstaff2010Modeling,author={Wagstaff, Kiri and desJardins, Marie and Eaton, Eric},year={2010},title={Modeling and learning user preferences over sets},journal={Journal of Experimental & Theoretical Artificial Intelligence},volume={22},number={3},pages={237--268},month=sep,}
Most work on preference learning has focused
on pairwise preferences or rankings over individual
items. In this paper, we present a
method for learning preferences over sets of
items. Our learning method takes as input a
collection of positive examples–that is, one
or more sets that have been identified by a
user as desirable. Kernel density estimation
is used to estimate the value function for individual
items, and the desired set diversity is
estimated from the average set diversity observed
in the collection. Since this is a new
learning problem, we introduce a new evaluation
methodology and evaluate the learning
method on two data collections: synthetic
blocks-world data and a new real-world music
data collection that we have gathered.
@inproceedings{desJardins2006Learning,author={desJardins, Marie and Eaton, Eric and Wagstaff, Kiri},title={Learning user preferences for sets of objects},booktitle={Proceedings of the 23rd International Conference on Machine Learning (ICML)},year={2006},month=jun,address={Pittsburgh, PA},bib2html_dl_code={software/DDPref.zip}}
