Phillip Odom

AAAI Conference 2020 Conference Paper

A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains

• Harsha Kokel
• Phillip Odom
• Shuo Yang
• Sriraam Natarajan

Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. We develop a unified framework for both classification and regression settings that can both effectively and efficiently incorporate such constraints to accelerate learning to a better model. Our results in a large number of standard domains and two particularly novel realworld domains demonstrate the superiority of using domain knowledge rather than treating the human as a mere labeler.

AAMAS Conference 2018 Conference Paper

Preference-Guided Planning: An Active Elicitation Approach

• Mayukh Das
• Phillip Odom
• Md. Rakibul Islam
• Janardhan Rao Doppa
• Dan Roth
• Sriraam Natarajan

AAMAS Conference 2016 Conference Paper

Active Advice Seeking for Inverse Reinforcement Learning

• Phillip Odom
• Sriraam Natarajan

Intelligent systems that interact with humans typically require input in the form of demonstrations and/or advice for optimal decision making. In more traditional systems, such interactions require detailed and tedious effort on the part of the human expert. Alternatively, active learning systems allow for incremental acquisition of the demonstrations from the human expert where the learning system generates the queries. However, active learning allows for only labeled examples as input, significantly restricting the interaction between expert and learning algorithm. Advice-based learning systems increase the expressiveness of the interaction, but typically require all the advice about the domain in advance. By combining active learning and advice-based learning, we consider the problem of actively soliciting human advice. We present the algorithm in an inverse reinforcement learning setting where the utilities are learned from demonstrations. We show empirically the contribution of a more expressive advice over traditional active learning approaches.

AAAI Conference 2015 Conference Paper

Active Advice Seeking for Inverse Reinforcement Learning

• Phillip Odom
• Sriraam Natarajan

AAAI Conference 2015 Conference Paper

Knowledge-Based Probabilistic Logic Learning

• Phillip Odom
• Tushar Khot
• Reid Porter
• Sriraam Natarajan

Advice giving has been long explored in artificial intelligence to build robust learning algorithms. We consider advice giving in relational domains where the noise is systematic. The advice is provided as logical statements that are then explicitly considered by the learning algorithm at every update. Our empirical evidence proves that human advice can effectively accelerate learning in noisy structured domains where so far humans have been merely used as labelers or as designers of initial structure of the model.