H L Data Storage Store Banner 970x90
Breaking News

DJI Air 2S Melds Incredible Image Quality With Unmatched Flight Performance Fujifilm launches FUJINON Lens XF18mmF1.4 R LM WR ASRock Launches the Radeon RX 6900 XT OC Formula 16GB Graphics Cards BIOSTAR ANNOUNCES THE NEW B550T-SILVER MOTHERBOARD Unity will add native NVIDIA DLSS support to its game engine

logo

  • Share Us
    • Facebook
    • Twitter
  • Home
  • Home
  • News
  • Reviews
  • Forum
  • Legacy
  • About
    • Submit News

    • Contact Us
    • Privacy

    • Promotion
    • Advertise

    • RSS Feed
    • Site Map

Search form

Microsoft AI Masters Pac-Man

Microsoft AI Masters Pac-Man

Enterprise & IT Jun 14,2017 0

To master the game Ms. Pac-Man, Microsoft researchers have created an artificial intelligence-based system that learned how to get the maximum score on the legendary video game Ms. Pac-Man, using a divide-and-conquer method that could have broad implications for teaching AI agents to do complex tasks that augment human capabilities.

The team from Maluuba, a Canadian deep learning startup acquired by Microsoft earlier this year, used a branch of AI called reinforcement learning to play the Atari 2600 version of Ms. Pac-Man perfectly. Using that method, the team achieved the maximum score possible of 999,990.

To get the high score, the team divided the large problem of mastering Ms. Pac-Man into small pieces, which they then distributed among AI agents. That's similar to some theories of how the brain works, and it could have broad implications for teaching AIs to do complex tasks with limited information.

The method, which the Maluuba team calls Hybrid Reward Architecture, used more than 150 agents, each of which worked in parallel with the other agents to master Ms. Pac-Man. For example, some agents got rewarded for successfully finding one specific pellet, while others were tasked with staying out of the way of ghosts.

Then, the researchers created a top agent who took suggestions from all the agents and used them to decide where to move Ms. Pac-Man. The top agent took into account how many agents advocated for going in a certain direction, but it also looked at the intensity with which they wanted to make that move. For example, if 100 agents wanted to go right because that was the best path to their pellet, but three wanted to go left because there was a deadly ghost to the right, it would give more weight to the ones who had noticed the ghost and go left.

Harm Van Seijen, a research manager with Maluuba who is the lead author of a new paper about the achievement, said the best results were achieved when each agent acted very egotistically - for example, focused only on the best way to get to its pellet - while the top agent decided how to use the information from each agent to make the best move for everyone.

Rahul Mehrotra, a program manager at Maluuba, said figuring out how to win these types of videogames is actually quite complex, because of the huge variety of situations you can encounter while playing the game.

With reinforcement learning, an agent gets positive or negative responses for each action it tries, and learns through trial and error to maximize the positive responses, or rewards.

An AI-based system that uses supervised learning would learn how to come up with a proper response in a conversation by feeding it examples of good and bad responses. A reinforcement learning system, on the other hand, would be expected to learn appropriate responses from only high-level feedback, such as a person saying she enjoyed the conversation - a much more difficult task.

AI experts believe reinforcement learning could be used to create AI agents that can make more decisions on their own, allowing them to do more complex work and freeing up people for even more high-value work.

Tags: MicrosoftArtificial Intelligence
Previous Post
Pioneer's Flagship Se-Monitor5 Hi-Res Headphones Released
Next Post
Nokia Unveils the World's Fastest Routers

Related Posts

  • Microsoft announces Surface Laptop 4

  • Whats best for you MAC or PC?

  • Microsoft Introduces Surface Pro 7+

  • Minecraft with RTX Now Officially Available For All Windows 10 Players

  • Microsoft announces Surface Laptop Go, new updates to Surface Pro X

  • Microsoft releases Windows File Recovery tool

  • Removing “Annoying” Windows 10 Features is a DMCA Violation, Microsoft Says

  • Fujitsu AI-Video Recognition Technology Promotes Hand Washing Etiquette and Hygiene in the Workplace

H L Data Storage Store Banner 300x600

 

Latest News

DJI Air 2S Melds Incredible Image Quality With Unmatched Flight Performance
Consumer Electronics

DJI Air 2S Melds Incredible Image Quality With Unmatched Flight Performance

Fujifilm launches FUJINON Lens XF18mmF1.4 R LM WR
Cameras

Fujifilm launches FUJINON Lens XF18mmF1.4 R LM WR

ASRock Launches the Radeon RX 6900 XT OC Formula 16GB Graphics Cards
GPUs

ASRock Launches the Radeon RX 6900 XT OC Formula 16GB Graphics Cards

BIOSTAR ANNOUNCES THE NEW B550T-SILVER MOTHERBOARD
PC components

BIOSTAR ANNOUNCES THE NEW B550T-SILVER MOTHERBOARD

Unity will add native NVIDIA DLSS support to its game engine
Gaming

Unity will add native NVIDIA DLSS support to its game engine

Popular Reviews

CeBIT 2005

CeBIT 2005

Zidoo Z9S 4K Media Player review

Zidoo Z9S 4K Media Player review

CeBIT 2006

CeBIT 2006

LiteOn iHBS112 review

LiteOn iHBS112 review

Club3D HD3850

Club3D HD3850

Crucial P1 NVMe 1TB SSD review

Crucial P1 NVMe 1TB SSD review

Toshiba Exceria M303 64GB and M501 Exceria Pro 64GB MicroSDXC review

Toshiba Exceria M303 64GB and M501 Exceria Pro 64GB MicroSDXC review

Hitachi DZ-MV100A DVD Camcorder

Hitachi DZ-MV100A DVD Camcorder

  • Home
  • News
  • Reviews
  • Forum
  • Legacy
  • About
    • Submit News

    • Contact Us
    • Privacy

    • Promotion
    • Advertise

    • RSS Feed
    • Site Map
  • About
  • Privacy
  • Contact Us
  • Promotional Opportunities @ CdrInfo.com
  • Advertise on out site
  • Submit your News to our site
  • RSS Feed