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Research Interests

My research interests lie at the intersection of various areas including:
  • Artificial Intelligence Planning
       Artificial intelligence is a very broad focus with many specialties. Planning tries to develop autonomous problem solvers that can find sequences of actions that accomplish a goal. That is, can a computer solve a problem on its own? The Resource Bounded Reasoning (RBR) Lab's work often involves planning under uncertainty, which can be viewed in many ways including:
  • Partial Information: you do not know every detail about the world. For example, where is the item you need? (it may not be where you expect unless you already observed it and can guarantee that it was not moved)
  • Stochasticity: "random" events may happen with some probability. For example, will the grocery store have an ingredient in stock? (predicting other shoppers' purchases and the arrival of new shipments can be approximated by probability)
  • Nondeterminism: actions can have one of several outcomes. For example, what could happen when printing a document? (you get a hard copy of the document if everything works, there may be nothing there if the paper jams, the document may be difficult to read if the ink or toner is low, etc.)
  • Unspecified Problem Aspects: the goal may be qualitatively described or generalized. For example, deliver "enough" pizzas, but how many is enough? (small numbers may be easy, but there could be a need for more pizzas than ovens or than a single delivery vehicle can hold)
  • Thus we want to develop methods that are robust enough for machines to solve problems while accounting for various unknown circumstances.
  • Plan Recognition
       Inverting the planning problem makes the solution the input and the problem the output. Specifically, given an observed sequence of an agent's executed actions and/or a sequence of changes in the world, what was the problem? For example, you walk into a room and see your friend walking around doing various things. So you ask yourself, "What is he/she doing?"
  • Activity Recognition
       Given a collection of raw sensor readings, can we identify higher-level actions? For example, if an accelerometer senses an up-and-down motion (which could be caused by jumping), then how does the machine explain this in words that a human can interpret?
  • Intent Recognition
       Given an observed sequence of an agent's executed actions, what is the agent's goal and/or next action(s)? This task is more predictive than plan recognition, but the two areas have a lot in common with respect to formulations.
  • Human-Computer/Robot Interaction
       The development and study of algorithms and systems intended to facilitate user experiences with digital entities, both virtual and physical. This can range from easing usability of an interface to making more pleasant/comfortable engagements between the system and user.
  • Topic Modeling
       The development of models to infer the underlying themes in sets of data, usually written documents. The interpretation of these underlying themes are also studied. That is, what are a collection of books about, and how does one identify this?
  • Knowledge Representation
       Identifying features of information that make it practical for artificial intelligence algorithms. That is, what does a piece of information look like in the machine's brain, and how can it be used?
  • Statistical-Relational Methods
       Combining nondeterministic/probabilistic and relational approaches in artificial intelligence. This provides the best of both worlds between handling some forms of statistical uncertainty and relational structure for a given domain.
  • The broad focus of my research is artificial intelligence for human-computer/robot interaction, aiming to develop systems that adaptively interact with human users through understanding their actions in the environment. My approaches to this are very interdisciplinary based on my wide range of interests.
    My ultimate research goal is to find methods that will better bridge the computer-human gap. In partiular, machines are computational entities while humans are relational entities (a difference depicted by Moravec's Paradox). The fact that Turing Recognizable problems exist shows that there are some problems a human can figure out that a computer cannot solve. Thus trying to emulate humans through computers will not necessarily make machines more human. However, I believe that we can find ways to bijectively map between features of computational entities and human beings; think of it like a cultural translator. Then we should be able to facilitate our interaction and cooperation with machines.


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