Abstract:
Plan and goal recognition is the task of inferring the plan and goal of an agent through the observation of its actions and its environment and has a number of applications on computer-human interaction, assistive technologies and surveillance.
Although such techniques using planning domain theories have developed a number of very accurate and effective techniques, they often rely on assumptions of full observability and noise-free observations.
These assumptions are not necessarily true in the real world, regardless of the technique used to translate sensor data into symbolic logic-based observations.
In this work, we develop plan recognition techniques, based on classical planning domain theories, that can cope with observations that are both incomplete and noisy and show how they can be applied to sensor data processed through deep learning techniques.
We evaluate such techniques on a kitchen video dataset, bridging the gap between symbolic goal recognition and real-world data.

Speaker Bio:
Dr. Felipe Meneguzzi is a researcher on multiagent systems, normative reasoning and automated planning. He is currently an associate professor at Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS). Prior to that appointment he was a Project Scientist at the Robotics Institute at Carnegie Mellon University in the US. Felipe got his PhD at King’s College London in the UK and an undergraduate and masters degree at PUCRS in Brazil. He received the 2016 Google Research Awards for Latin America, and was one of four runners up to 2013 Microsoft Research Awards. His current research interests include plan recognition, hybrid planning and norm reasoning.

Slides from the talk