Maana Researcher Dr. Fangkai Yang to Present Research Paper at the 27th International Joint Conference on Artificial Intelligence

Maana, the pioneer of digital knowledge technology, today announced its
researcher Dr. Fangkai Yang will present research paper “PEORL:
Integrating Symbolic Planning and Hierarchical Reinforcement Learning
for Robust Decision-Making”, co-authored with Maana chief scientist Dr.
Steven Gustafson, Prof. Bo Liu and Daoming Lyu from Auburn University,
in a technical sessions at the 27th International Joint Conference on
Artificial Intelligence (IJCAI) and the 23rd European Conference on
Artificial Intelligence, the premier international gathering of
researchers in Artificial Intelligence, to be held from July 13–19 in
Stockholm, Sweden.

What: Research paper “PEORL: Integrating Symbolic Planning and
Hierarchical Reinforcement Learning for Robust Decision-Making”Who:
Dr. Fangkai Yang, Researcher at MaanaWhen: July 17,
2018, 14:55Where: 27th International Joint Conference on
Artificial Intelligence (IJCAI) and the 23rd European Conference on
Artificial Intelligence in Stockholm, Sweden, Room C7

IJCAI is the most established premiere academic conference on artificial
intelligence, starting from 1969. IJCAI-18 is part of the Federated AI
Meeting that takes place at Stockholmsmässan
in Stockholm July 9-19. Other conferences include AAMAS, ICML, ICCBR
and SoCS.
The World
Computer Chess Championships will also take place in parallel. More
than 5,000 researchers, technologists and experts are expected to attend
IJCAI and its partner AI conferences. Keynote speakers include Prof.
Yann LeCun (Facebook), Prof. Max Tegmark (MIT), Prof. Joshua Tenenbaum
(MIT), and many more. The main conference will hold 800 separate AI
seminars, industry days, demonstration sessions, robotics showcases and
exhibition.

During the conference, Dr. Yang will present the latest advancement of
its theoretical study underpinning the Maana knowledge platform. The
research paper describes a unified framework that integrates
knowledge-based artificial intelligence, in particular, knowledge
representation and automated planning, with reinforcement learning. This
framework allows the agent to generate its optimal behavior by
reinforcement learning from the interaction with the environment, guided
through reasoning and planning with explicitly represented domain
knowledge. This process enables planning and learning to mutually
benefit each other so that the behavior rapidly converges to the optimal
for complex and dynamic domains.

This research supports the user-guided, machine-assisted decision-making
methodology of the Maana platform, where human knowledge helps the
intelligent agent to automatically generate its solutions for business
problems and proposes recommendations to the user. The agent can further
improve its recommendation by observing the effectiveness of its
solution or directly receiving user feedback. Eventually, the agent can
reach optimal decision-making, utilizing both explicitly formulated
human knowledge and personalized feedback, for different users and
different problems.

The theoretical fruit of this paper has already led to the development
of the AutoML service. AutoML service automatically recommends top
performing machine learning pipeline and hyper-parameters given a
particular dataset. Different from other AutoML system that mainly
focuses on automating ML tasks using a black box, AutoML service allows
for extracting knowledge learned from the dataset, a step towards
“interpretable AI”. The paper, entitled “Program Search for Machine
Learning Pipelines Leveraging Symbolic Planning and Reinforcement
Learning” was presented by Dr. Gustafson in 16th Genetic
Programming Theory & Practice (GTPT XVI), in May 2018.

For more information about IJCAI, visit: https://www.ijcai-18.org/

About Maana

Maana’s patented Computational Knowledge Graph™ is a unique technology
that represents and analyzes industrial knowledge mathematically. This
innovation enables industrial companies to encode human expertise and
data from across silos into a digital knowledge layer to help employees
make better and faster decisions. Using the Maana Knowledge Platform,
Fortune 500 industrial companies can quickly develop AI-driven Knowledge
Applications, that accelerate digitizing decision flows and operations.
In 2017 Maana was recognized by the World Economic Forum as a Technology
Pioneer for enabling the 4th industrial revolution and also by IHS
Markit as a CERAWeek 2017 Energy Innovation Pioneer.

Customers include Global Fortune 500 industrial companies such as
Airbus, BHP, Chevron, GE, Maersk, and Shell. Maana is privately held
with offices in Palo Alto, California, Bellevue, Washington, Houston,
Texas, and international presence in Dhahran, Saudi Arabia; London, UK;
Copenhagen, Denmark and the Netherlands. Visit us at https://www.maana.io

Twitter: @MaanaKnowledge

LinkedIn: https://www.linkedin.com/company/maana

View source version on businesswire.com: https://www.businesswire.com/news/home/20180710005010/en/

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