Date post: | 02-Nov-2014 |
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Interactive Artificial Learning in Multi-agent Systems Yomna M. Hassan, Salman Ahmed, and Jacob W. CrandallComputing and Information Science Program at the Masdar Institute of Science and Technology, Abu Dhabi, UAE.
Email: {yhassan, sahmed, jcrandall}@masdar.ac.ae
On-going Research
Multi-agent learning algorithms for coordination in smart power grids
In power systems with renewable energy resources, demand response programs can be used
to are encourage more efficient use of energy resources. Intelligent devices can be developed
to help users respond effectively. One method we are considering for these devices is
interactive evolutionary learning, wherein human input is provided to a genetic algorithm. We
are developing interactive evolutionary algorithms that learn successfully in multi-agent systems
with minimal human input. Basic structure of the algorithm is shown in the figure to the left.
Learning By Demonstration in Repeated stochastic games
We have performed preliminary investigating the usefulness of LbD in MAS. The simulation have been done on a repeated stochastic
games based which models the iterative prisoner’s dilemma..Results show that LbD helps learning agents learn non-myopic equilibrium in
repeated stochastic games when human demonstrations are
well-informed. On the other hand, when human demonstrations
are less informed, these agents sometimes learn behavior that
produces (less-successful) myopic behavior.
Task Scheduling in Multi-Vehicle Transportation Systems
In general, machine learning algorithms rely heavily on the configuration stage,
wherein the programmer selects relevant features and a distance metric. We are
investigating the possibility of deriving the distance metric from interactions between
the agent and the user. In particular, we are adapting and extending the CBA
algorithm (Chernova and Veloso, 2009) for an online taxi problem.
initialization Evaluate fittnesCheck termination conditiion criteria
selection
no
crossover
mutation
New population
terminateyes
Human input
Human input
Introduction
Many real-world problems, in which intelligent machines
can be useful, require interactions between multiple
intelligent agents. To overcome the challenges of
previously used methods, we are adapting Interactive
artificial learning (IAL) as a learning methodology in
multi-agent systems (MAS). Learning by demonstration
(LbD) and reward reinforcement have been studied
previously in single agent environments. We are
focusing on MAS.
Interactive artificial learning process
( Chernova and Veloso, 2009)Configure
Needs Demonstration
ActNO
Train
Yes
Needs Help to Update Policy
Update Policy
NO
Update Policy
Yes
Modification Planned
Programmer Input Required
User Input Required
Work Autonomsly
Configure Plan ActObserve
and Reward
Update
End User
Step 1 Step 2 Step 3 Step 4 Step 5
Learning By Demonstration
Reward Reinforcement