5 citations · 10 across the 8 of their papers we have counts for
9 papers
Learning Heterogeneous Preferences
Shiwali Mohan, Matt Hong, Dule Shu +3
Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in policy learning. Existing meth…
Deconstructing Taste: Toward a Human-Centered AI Framework for Modeling Consumer Aesthetic Perceptions
Matthew K. Hong, Joey Li, Alexandre Filipowicz +6
Understanding and modeling consumers' stylistic taste such as "sporty" is crucial for creating designs that truly connect with target audiences. However, capturing taste during the…
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
Casey Kennington, Malihe Alikhani, Heather Pon-Barry +20
The ability to interact with machines using natural human language is becoming not just commonplace, but expected. The next step is not just text interfaces, but speech interfaces…
Can Generative AI Support Patients' & Caregivers' Informational Needs? Towards Task-Centric Evaluation Of AI Systems
Shreya Rajagopal, Jae Ho Sohn, Hari Subramonyam +1
Generative AI systems such as ChatGPT and Claude are built upon language models that are typically evaluated for accuracy on curated benchmark datasets. Such evaluation paradigms m…
Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World
James Chao, Wiktor Piotrowski, Roni Stern +4
Autonomous agents operating within real-world environments often rely on automated planners to ascertain optimal actions towards desired goals or the optimization of a specified ob…
A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds
Shiwali Mohan, Wiktor Piotrowski, Roni Stern +4
Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA - a f…
Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
Wiktor Piotrowski, Yoni Sher, Sachin Grover +2
This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem. To model the game, we use…
Learning to Operate in Open Worlds by Adapting Planning Models
Wiktor Piotrowski, Roni Stern, Yoni Sher +4
Planning agents are ill-equipped to act in novel situations in which their domain model no longer accurately represents the world. We introduce an approach for such agents operatin…
Playing Angry Birds with a Domain-Independent PDDL+ Planner
Wiktor Piotrowski, Roni Stern, Matthew Klenk +4
This demo paper presents the first system for playing the popular Angry Birds game using a domain-independent planner. Our system models Angry Birds levels using PDDL+, a planning…