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Research Statement
Pradeep Varakantham
School of Information Systems
Email: [email protected] Tel: (+65) 6828 0519
Ubiquitous computing devices have not only changed the way people interact
with the world, but have also helped capture an elaborate footprint of people’s
actions. My long-term research goal is to analyze how people make decisions
(by processing data about their actions) and using that analysis to build agents
that provide intelligent decision support for an improved and efficient living
in real world environments. More specifically, I am interested in real world
environments that are uncertain, dynamic and on a large scale (many intelligent
decision
makers
or
complex
decision
making
process).
A
few
example
environments that I have focused in my recent research contributions are:
•
Decision support for taxi drivers to increase revenue and reduce starvation
for taxis in a modern city
•
Reducing congestion at overcrowded theme parks by offering decision
support and incentives to patrons
•
Developing analysis agents that interact with computer malware and bot
networks for understanding their behavior
•
Advising users on flexibility in meeting schedules to conserve energy
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The figure above provides visualization for the settings of interest, where each
individual human or an agent acting on behalf of human takes decisions based
on observations about the environment and on the decision model constructed
from analysis of decisions of other agents/humans.
Two major obstacles in accomplishing the above mentioned goals are: (a)
Analyzing
large
data
sets
for
understanding
human
decisions;
and
(b)
Constructing decision models based on the analysis and solving them in tune
with human objectives and requirements. The complexity of addressing (a) and
(b) varies significantly based on:
•
Uncertainty: This arises due to many factors including but not limited to
incompleteness of data, non-deterministic outcomes of actions, partial
observability of the world, durational uncertainty, etc. All these variants of
uncertainty add significant computational complexity to the process of
computing strategic decisions.
•
Multiple agents: Understanding the impact of one agent or human’s actions
on another’s decision is hard to gauge from data and so is strategizing in the
presence of other agents.
This is due to differing attitudes (competitive or
cooperative) of other agents/people.
•
Scale: The number of agents and the complexity of the decision problem
faced by an agent have a significant bearing on the approaches to be
employed for efficient and ultimately useful decision support.
Thus far, my contributions can be summarized as addressing a combination of
the above three factors in various settings. Here I outline four of the very recent
research threads (categorized on the problem settings) that further highlight the
theme of decision analytics (understanding decisions) and subsequent intelligent
decision support:
(a) Resource constrained and congested environments:
Imbalance in the usage of public and private resources (such as roads, buses,
trains, theaters, malls, etc.) is considered to have a major impact on quality of
life in cities of today. Coordinating the allocation and access of resources for
balanced usage through mobile devices (such as smart phones) is thus an
important problem. More specifically, we have addressed problems where plans
for multiple agents interact due to common resources. Here are the key results
in this thread:
(i)
Developed a behavioral model [1] that showed that the average value
for levels of reasoning for a taxi driver (about other taxi drivers
movement) for taxi drivers is between 1 and 2. This a model had less
than 5% of error for the fleet over a 2-year data set.
(ii)
Provided decision support mechanisms for both competitive [1,4] and
cooperative scenarios [6,9] that scaled to problems with thousands
of agents and showed an increase of over 40% in revenue for taxi
drivers in the context of taxi fleet.
(iii)
Decision support mechanisms in (ii) for competitive settings [10]
guarantee strategy equilibrium and furthermore demonstrate a
price of anarchy bound of ½.
(iv)
In the context of resource constrained cooperative settings [11], our
approaches provide 63% of optimal guarantee on solution quality in
problem domains from literature (including social welfare for
competitive settings such as taxi fleets).
(b) Active Malware Analysis using Strategic Planning
Cyber security is increasingly important for defending computer systems from
loss of privacy or unauthorized use. One important aspect is threat analysis —
how does an attacker infiltrate a system and what do they want once they are
inside. In this thread, we consider the problem of Active Malware Analysis [5],
where we learn about the human or software intruder by actively interacting
with it with the goal of learning about its behaviors and intentions, whilst at the
same time that intruder may be trying to avoid detection or showing those
behaviors and intentions. Game-theoretic active learning was used to obtain a
behavioral clustering of real malware (downloaded from various well known
databases on information stealing malware). We were able to provide a
80% accuracy in clustering of these malware. Furthermore, we have also
provided a case study for employing the same mechanism for actively
analyzing a real BotNet (a network of compromised computers taken over by
an attacker) called Zeus.
(c) Risk aware planning and scheduling in uncertain domains
In high stakes domains such as -- financial markets, disaster rescue scenarios,
digital watermarking (for critical information sharing), many chance based
games (where money is involved) and project scheduling problems with
durational uncertainty -- decisions need to be made while considering the risk
attitudes (risk averse, risk seeking, risk neutral) of decision maker and also the
uncertainty [2,3,7]. We have provided scalable algorithms with quality
guarantees in specific instances that provide decision support while accounting
for risk attitudes of the user.
(d) Delayed Observation Planning
Traditional models for planning under uncertainty such as Markov Decision
Processes (MDPs) or Partially Observable MDPs (POMDPs) assume that the
observations about the results of agent actions are instantly available to the
agent. In so doing, they are no longer applicable to domains where
observations are received with delays caused by temporary unavailability of
information (e.g. delayed response of the market to a new product). To that
end, we provide scalable methods for delayed observation planning in the
context of POMDPs [8].
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Evolution of My Research
Towards achieving improved and efficient living using intelligent decision support
systems, my research was initially focused on single agent environments,
where a single agent provides decision support based on the observations about
the environment and by considering the impact its decisions have on the
environment. This was primarily in the context of software personal assistants.
Subsequently, I expanded my focus to systems, where each agent has to
consider the impact of other agents/decision makers in addition to the
challenges of the environment. Now, to integrate such systems into a real world
environment where there are also human decision makers, I have expanded my
focus to analyze human decisions so that we can have a better understanding of
where intelligent decision support strategies outperform human decisions and in
the future account for limited adoption.
Future Research
For intelligent decision support in devices to finally break out in the real world, in
a very fundamental sense, they must conquer uncertainty, reason with human
models and also think about other intelligent devices. In the future, I would like
to build upon my research towards understanding the reasoning process in ever
more realistic environments and specifically across the following three
directions:
•
Game Changing Incentives: In many environments, where there is
resource congestion (ex: theme parks) due to similarity in preferences,
we hope to study how positive reinforcements (ex: free rides or shows,
discounts at restaurants in theme parks) can be employed for modifying
people’s utilities thereby changing the equilibrium strategies in a way
that reduces congestion.
•
Limited Adoption: Typically decision support mechanisms in multiple
agent/person systems require that everyone follow advice for the desired
performance to be achieved. Here, we wish to come up with approaches
based on decision analytics and prediction that can perform well even in
the context of limited adoption.
•
Behavioral Models for Multi-Agent Decision Analytics: In this
thread, we hope to generalize and construct behavioral models for people
that can accurately reflect people’s decision making strategies in the
presence of other agents/people given data sets of environments.
I believe that understanding the process of decision making in these critical
settings and utilizing this knowledge towards building practical intelligent
decision support will result in significantly improving user experience in many
environments.
References
[1] "Uncertain Congestion Games with Assorted Human Agent Populations", by Asrar Ahmed, Pradeep
Varakantham and Shih-Fen Cheng. Proceedings of Twenty Eighth International Conference on
Uncertainty in Artificial Intelligence (UAI-2012).
[2] "Dynamic Stochastic Orienteering Problems for Risk-Aware Applications", by Lau Hoong Chuin, William
Yeoh, Pradeep Varakantham, Duc Thien Nguyen and Huaxing Chen. Proceedings of Twenty Eighth
International Conference on Uncertainty in Artificial Intelligence (UAI-2012).
[3] "Robust Local Search for Solving RCPSP/max with Durational Uncertainty", by Na Fu, Lau Hoong Chuin,
Pradeep Varakantham, and Xiao Fei. Journal of Artificial Intelligence Research (JAIR).
[4] "Decision Support for Agent Populations in Uncertain and Congested Environments", by Pradeep
Varakantham, Shih-Fen Cheng, Geoff Gordon and Asrar Ahmed. Proceedings of Twenty Sixth Conference
on Artificial Intelligence (AAAI-2012).
[5] "Active Malware Analysis using Stochastic Games", by Simon Williamson, Pradeep Varakantham, Debin
Gao and Chen Hui Ong. Proceedings of Eleventh International Conference on Autonomous Agents and
Multi-Agent Systems (AAMAS-2012).
[6] "Distributed Model Shaping for Scaling to Decentralized POMDPs with hundreds of agents", by Prasanna
Velagapudi, Pradeep Varakantham, Paul Scerri and Katia Sycara. Proceedings of the Tenth International
Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS).
[7] "Risk-Sensitive Planning in Partially Observable Domains", by Janusz Marecki and Pradeep Varakantham,
2010, Proceedings of the Ninth International Joint Conference on Autonomous Agents and MultiAgent
Systems (AAMAS), Toronto, Canada.
[8] "Delayed Observation Planning in Partially Observable Domains", by Pradeep Varakantham and Janusz
Marecki. Proceedings of Eleventh International Conference on Autonomous Agents and Multi-Agent
Systems (AAMAS-2012).
[9] "Lagrangian Relaxation for Large Scale Multi-Agent Planning", by Geoff Gordon, Pradeep Varakantham,
William Yeoh, Lau Hoong Chuin, Ajay Srinivasan and Cheng Shih-Fen. Proceedings of Ninth International
Conference on Intelligent Agent Technology (IAT-2012).
[10] “Guarantees on Equilibrium Welfare in Uncertain Congestion Games”, by Pradeep Varakantham, Asrar
Ahmed. Submitted to Twelveth Inernational Conference on Autonomous Agents and Multi-Agent
Systems (AAMAS-2013).
[11] “Greedy Approaches for Resource Constrained Multi-Agent Planning”, by Pradeep Varakantham, William
Yeoh. Submitted to Twelveth Inernational Conference on Autonomous Agents and Multi-Agent Systems
(AAMAS-2013).