Post on 09-Mar-2018
AI and Its Applications in Manufacturing
Dr. Biplav Srivastava IBM Research – India
Presentation to MEL 423 (Computers in Manufacturing Class) IIT Delhi, November 12, 2014
Outline
l Artificial Intelligence l Sample Applications l Manufacturing Domain and Planning l Improving Manufacturing with Data and
Extracted Knowledge
Resources
l AI – Summary of the sub-areas:
http://aitopics.org/topic/ai-overview – Russell and Norvig, AI – A Modern Approach – http://en.wikipedia.org/wiki/
Artificial_intelligence
Artificial Intelligence (AI)
l Intelligent Agents - Build useful systems – Symbolic: logic, reasoning, search – Statistical: machine learning, Bayes rules
l Cognition – Understand working of brain
Sample Systems
l ASIMO Copies Dance Moves (video) l Chat with Ramona (link) l IBM Watson (video)
Sample AI Applications · Agriculture & Natural Resources · Archaeology · Architecture & Design · Art · Artificial Noses · Assistive Technologies · Astronomy & Space Exploration · Automatic Programming · Automotive Industry · Autonomous Vehicles · Aviation · Banking & Finance · Bioinformatics · Biometrics · Business & Manufacturing · Chatbots · Chemistry · Decision Support Systems · Earth & Atmospheric Science · Engineering Design · Fraud Detection · Hazards & Disasters · Knowledge Management
· Law · Law Enforcement & Public Safety · Machine Storytelling · Marketing · Medicine · Military · Music · Networks · Oil & Gas · Politics & Foreign Relations · Recommender Systems · Robots in the Home · Robots in the Workplace · Science & Mathematics · Smart Houses & Appliances · Social Science · Software Engineering · Spam Filtering · Surveillance · Telecommunications · Transportation & Shipping
© 2014 IBM Corporation
Software System
7
Environment
Reading Input
Produce Output
Business Logic Processing
Referenced Data
Input Output
Move More Business Logic To Declarative Data (policy)
© 2014 IBM Corporation
Example: Taking Care of a Baby Individual’s Extension
8
Agent
Expected behavior: • Inform
• Alert when crying • Alert when awake • Alert when idle
• Do • Raise temperature of room • Play music • …
© 2014 IBM Corporation
Example: Taking Care of a Senior Assisted Cognition
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Agent
Expected behavior: • Inform
• Alert when idle • Alert when away from known locations • Alert when checkup/ medicines due
• Do • Send body parameters periodically • …
© 2014 IBM Corporation
Example: Taking Care of Oneself Personal Digital Assistants
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Expected behavior: • Inform
• When missing meetings • When missing social commitments • Reminding of priorities • …
• Do • Make all cancellations / re-bookings when
schedule changes • Find alternatives to current decisions and
give choices (e.g., traffic) • …
Agent
01/23/12 IBM ABLE Agents Overview 11
Agents are active, persistent software components that perceive, reason, Agents are active, persistent software components that perceive, reason, act, and communicate. (Huhns and Singh)act, and communicate. (Huhns and Singh)
Software that Software that assistsassists people and acts on their behalfpeople and acts on their behalf
Agents can helpAgents can help peoplepeople and and processesprocessesAgents are used for automation and control Agents are used for automation and control
What Are Intelligent Agents?
finding and filtering informationpersonalizing your environmentnegotiating for servicesautomating tedious taskstaking actions you delegatelearning about you over timecollaborating with other agentscapturing individual and organizational knowledgesharing knowledge
finding and fixing problemsautomating complex proceduresfinding "best fit" procedurespattern recognition and classificationpredictions and recommendationsnegotiate and cooperate with other organizations' agents
Planning & (Classical Planning)
Environment
action perception Goals
(Static) (Observable)
(perfect) (deterministic)
What action next?
I = initial state G = goal state Oi (prec) (effects)
[ I ]
Oi Oj Ok Om [ G ]
Slide courtesy: Prof. Subbarao Kambhampati, ASU Dr. Biplav Srivastava
Simple Planning Example
Blocks World
A B
Robot arm
A
B Blocks
Initial State Goal State
All robots are equivalent
Representation
States: ((On-Table A) (On-Table B) …) A B
Actions: ((Name: (Pickup ?block ?robot) Precondition: ((Clear ?block) (Arm-Empty ?robot) (On-Table ?block)) Add: ((Holding ?block ?robot)) Delete: ((Clear ?block) (Arm-Empty ?robot)))…)
A
B
Planning Process
Clear A Clear B On-Table A On-Table B Arm-Empty R1 Arm-Empty R2
On A B
Pick-up A R1
Pick-up A R2
Clear A Clear B Holding A R1 On-Table A On-Table B Holding A R2 Arm-Empty R1 Arm-Empty R2
Stack A B R1
Stack A B R2
Put-down A R2 Pick-up B R2
Pick-up B R1 Put-down A R1
Initial State Level P-0
Goal State Level P-2
Level A-0 Level P-1 Level A-1
Manufacturing Illustration
Slide courtesy Shart Sood’s slideshare presentation on Production and Operations Management, At: http://www.slideshare.net/technomgtsood/production-operations-management
Planning in a Manufacturing Process
IMACS, A System for Computer-Aided Manufacturability Analysis Slide by Marc Berhault, 2003
Complexity Example
IMACS, A System for Computer-Aided Manufacturability Analysis Slide by Marc Berhault, 2003
IMACS Approach Illustration
IMACS, A System for Computer-Aided Manufacturability Analysis Details at: http://www.cs.umd.edu/projects/cim/imacs/
Destination Control of Elevators
Reference: https://user.enterpriselab.ch/~takoehle/publications/elev/elev.html
l Schindler Lifts. – Central panel to enter
your elevator request. – Your request is scheduled
and an elevator is assigned to you.
– You can’t travel with someone going to a secure floor, emergency travel has priority, etc.
l Modeled as a planning problem and fielded in one of Schindler’s high end elevators.
Improving Manufacturing with Knowledge (Beyond Robotics)
l Enterprise Resource Planning l Enterprise Integration l Open Data and Analytics
(Manufacturing) Key Information Flow
Slide courtesy Shart Sood’s slideshare presentation on Production and Operations Management, At: http://www.slideshare.net/technomgtsood/production-operations-management
Analogical Example: Documenta3on in Health Care
Patient
Insurance
Pharmacy
Doctor’s Office
Medical History
Prescription
Medical Claims, Prescription
Drug Claims, Prescription
Bills, Policy Updates
Problem: Hand-offs between role-players are inefficient and failure-prone
Enterprise Resource Planning: Packaged Applica3on
l Packaged Applica3on – Off-‐the-‐shelf soBware to manage common business func3ons like accoun3ng,
payment and receivables, order management, customer management; or industry-‐specific func3ons like clinical trial (pharmaceu3cal), drilling (mining, oil & gas)
– Businesses buy these soBware and then engage service providers to tailor them – Enterprise Resource Planning (ERP) is a specific class of packaged applica3ons
l Market size (according to AMR Research [AMR 2008]) – The total market size for ERP soBware is currently $34.4B. SAP leads with 42 %,
followed by Oracle (23%), The Sage Group (7%), MicrosoB Dynamics (4%), and others.
– Spending on services including consul3ng, integra3on and support for Oracle, SAP, and other business applica3on vendors, called packaged enterprise applica3on services, was $103B for 2007, and expected to reach $174B by 2012.
– IBM is a prominent service provider for SAP and Oracle. l Trend is to move from Legacy applica3ons to Packaged Applica3ons
Packaged Applica3ons: Pre-‐built, Configurable, Business Processes Automa3on SoBware
Packaged Application SAP Oracle Cross-industry: ERP, CRM, SCM, PLM, SRM, HCM, e-Procurement
√ √
Industry specific solutions > 50: Aerospace &Defense, Automotive (3), Chemicals, Consumer Products (5), Construction, High Tech (4), Industrial, Life Sciences, Mill Products (5), Mining, Oil & Gas, Media (4), Services (2), Telco, Utilities (5), Waste & Recycling, Travel & Logistics (4), Banking, Insurance, Public Sector (2), Defense (2), Healthcare, Education & Research (2), Retail, Wholesale Distribution
> 50: Aerospace &Defense, Automotive (2), Chemicals, Consumer Products, Construction, High Tech (6), Industrial (3), Life Sciences, Mill Products (5), Mining, Oil & Gas (3), Media (4), Services (4), Telco, Utilities (4), Waste & Recycling, Travel & Logistics (5), Banking, Insurance, Public Sector (3), Defense (2), Healthcare (2), Education & Research (3), Retail, Wholesale Distribution
Packaged Application SW Revenue: $38B (2008) Source: AMR Research 2008 Market Share Analysis: ERP Software Worldwide, 2012 authored by Chris Pang, Yanna
Dharmasthira, Chad Eschinger, Koji Motoyoshi and Kenneth F. Brant.
Health with Data.gov.in Data (Since Feb 2013)
Conjectures: - DS1 and DS2: - Is gap in staff (e.g., pharmacists) correlated with gap in
infrastructure (e.g., primary health centers)? - Similarly other variants on staff and infrastructure
Manufacturing Implication – manufacture vaccines that can taken without staff
- DS3 and DS4: - Is increase in booster dose correlated with children mortality?
That is, do states having more women covered also have lower mortality?
Manufacturing Implication – where to market booster doses - DS1 and DS3: - Is gap in staff (e.g., pharmacists) correlated with number of
women getting booster shots? - DS2 and DS4: - Is gap in infrastructure (e.g., health centers) correlated with
children mortality?
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Data sets - * DS1:
http://data.gov.in/dataset/pharmacists-laboratory-technicians-and-nursing-staff-primary-health-centres-community-health
* DS2:
http://data.gov.in/dataset/shortfall-health-infrastructure-2011-population-provisional-indiaas-march-2011
* DS3:
http://data.gov.in/dataset/number-women-given-tt2booster
* DS4:
http://data.gov.in/dataset/under-5-mortality-rate-u5mr
Putting All Together for Manufacturing
Use AI for deciding l What should one manufacture? l For whom, at what price point and with what process? l How should the product be maintained? How does one
reduce wastage? l How can we bundle services with it and increase service
quality?
Relevant Key AI Areas
l Knowledge Representation l Ontology l Reasoning and Logic l Machine Learning l Optimization, Scheduling l Planning l Mechanism Design, Auction