Georgios’s Visions ( interactive learning representations )

Georgios’s Visions(interactive learning representations)

MIT CSAIL

HHMM…

Ed Wood(Characterized as the worst film maker ever)

"Home? I have no home Hunted,despised, Living like an animal! The jungle is my home. But I will show the world that I can be its master! I will perfect my own race of people. A race of atomic supermen which will conquer the world!"

Why?

Learning from delayed reward is hopeless (in my opinion)

Supervised learning is impractical

Humans and animals live in societies

Need something above RL and below supervised learning

Possible Titles

Social learning

Interactive learning

Learning to communicate

Classroom learning

Competitive learning

Do what I mean not what I say

What do you mean?

Let’s talk

Robot apprentices

Searching for the right representations

Final Product

Observations, Actions,Rewards,State modification

Erik’s representation

Pavlov’s representation

Georgios’srepresentation

PHYSICAL ENVIRONMENT

Obstacles

A mathematical framework for interactive learning (reward shaping?)

What are objects (sensory, motor sequences ?)

How do they relate to each other. What are the representations (atomic, propositional, first-order?)

Example Systems

A robot that learns to navigate by interaction with a human trainer

A personalized web agent(active information extraction)

Personal assistants (office)

Tools & Concepts

H-POMDPS?

What is missing? Dynamic abstractions (structure learning)

Teleological abstractions

Relational structure

Factorization (hierarchical reuse)

Multiagency /concurrency

Grounded Projects

Other H-POMDP applications

Model reduction in POMDPs with macros

Structure learning of H-POMDPs

Theoretical localization results in grid-worlds with structure

Mathematical framework for interactive learning

Efficient algorithms for learning stochastic models

Other H-POMDP Applications Passive “hierarchical” HMM applications

Policy recognition (AMM) (Hung Bui) Video Structure discovery (HHMM) (Lexing Xie) Human activity recognition (Nuria Oliver) Emotion Recognition (multi –level HMM) (Ira Cohen) Natural English text & cursive hand-writing (HHMM) (Fine) Information extraction (HHMM) (skounakis)

Active recognition/learning Active object detection/recognition (RL) (Lucas paletta) Selective perception policies for guiding sensing (layered HMM ) (Nuria Oliver, Eric

Horvitz) Active learning of HMMs (Tobias Scheffer)

What can we do (active learning?) (active recognition==POMDP planning?) Recognition of office activity / Active recognition of office activity / Active learning

of model parameters

POMDPs & Macro-Actions

A model based RL over a dynamic grid abstraction in belief space with macro-actions (NIPS 2003) Consider only needed part of belief space Learn faster than just using primitive actions Ability to do information gathering

What’s next? A new minimized POMDP other than than the belief

state representation (PSRs? Non-linear dimensionality reductions? Smaller HMMs?)

Other domains

Structure Learning

Natural Language approaches Sequitor (Nevill-Manning) Unsupervised Language acquisition (Carl G. de

Marcken)

Structure learning in graphical models Discovering hidden state (X. Boyen)

From Data Mining Bursty and Hierarchical structure in streams (Jon

Kleinberg)

Localizing in Flat Grid Worlds is NP-hard

In flat POMDPs finding localization plans that are within a log factor of optimal is NP-Hard (Sven Koenig)

Does the same hold for H-POMDPs?

Mathematical Framework for Interactive learning

T

R

O Policy

Action a

State s

Reward r

zAGENT

ENVIRONMENT

State s

Reward r

Supervisor

Interactive Learning Literature

Programmable RL agents (David Andre)

Principle methods for advising RL agents (Garrison Cottrell)

Machine discovery of effective admissible heuristics (Armand E. Prieditis)

Supervised learning combined with an actor-critic architecture (Michaels Rosenstein)

Shaping in RL by changing the physics of the problem (Jette Randolv)

What if the teacher needs to learn too?

Efficient Learning Algorithms for Models of Stochastic Processes

Parameter learning in graphical models is inefficient (structure learning impractical)

Can we do better? Train model where it needs to be trained Do informed searching when learning

structure

Conclusions

Big results require big ambitions

To make progress towards AI,We need to make learning and planning more interactive

This will keep me busy for a while