Sridhar Mahadevan (Adobe Research and University of Massachusetts Amherst)
Date: Tuesday 20 October 6 PM – 9 PM UTC
The history of AI is inextricably linked with advances in the theory of planning. Beginning with
the earliest work in AI on logical formulations of planning, such as STRIPS, to the more recent
work on stochastic planning under uncertainty and reinforcement learning, formulations of
planning have progressively become more sophisticated to meet the demands of real-world
applications. Recent advances in machine learning, particularly deep reinforcement learning,
have once again cast planning in a new light, enabling the development of agents that can plan
in complex video games without the need for a priori models. In this tutorial, we explore new
frontiers of planning that may emerge as a result of advances in other areas of AI, particularly
deep learning models of imagination, such as generative adversarial networks, causal graphical
models, and intrinsic motivation formulations of creativity.
Humans exhibit a strong predisposition to imagine — to mentally transcend time, place, and
circumstance — from an early age, an ability that is at the heart of all creative human activities,
from art, literature, poetry, science, and technology. The ability to imagine is strongly
connected to planning, as it is related to prediction of future states, and yet, imagination in
humans is considerably more sophisticated than existing formulations of planning, such as
those based on Markov decision processes. Imagination involves the construction of
counterfactuals (e.g., what if Hilary Clinton had been elected President of the United States), as
well as the elucidation of explanations (e.g., what is causing climate change?). At the core of
human intelligence lies the notion of creativity, a capability that is prized among artists,
scientists, and technological entrepreneurs. What is the relationship between creativity and
planning?
The tutorial will outline the key challenges in developing new formulations of planning that
introduce causality, creativity, and imagination into the objectives of a deliberative agent. The
tutorial will also elaborate novel connections between imagination-based planning and ongoing
research in various areas, such as causality, deep learning, and transfer learning, and show why
reformulating planning to include these additional capacities may transform AI in the coming
decades.