Team 2 Maria Azua Dwight Bygrave Jonathan Leet Rick Rodin Evgeni Sadovski

February 16, 2010

What is the Problem?

Economic pressures are demanding more automation and efficient systems

Massive amount of data and escalating regulatory compliance laws are requiring more intelligent systems that can – Understand highly contextual information

Adjust behavior to context Can handle ambiguous situations Handle imprecise or implicit information

Cloud computing is commoditizing compute power. New low cost compute power is enabling “electronic reasoning” unaffordable a couple of years ago.

Information Overload

Consumer Dynamics Push Pull

Content Experiences

Self - Personalization

Social Consumption

Transition to Digital

New delivery channels (web, mobile)

Content type convergence – text, image, audio, video

“Born Digital” business models

Emerging competencies -- meta data, interactive experiences, multi-channel distribution, analytics.

Cross Channel relationship management - bundling

Expanding Impact of Technology

Digital Supply Chain -- workflow automation

Analytics -- Optimzation, Event management and Prediction

Resource optimization, variability and seasonality

Market Forces – The Perfect Storm

Business ModelInnovation

What is Cognition?

The Oxford dictionary defines cognition as knowing or perceiving

Cognition in Artificial intelligence – Extends the concept as an interdisciplinary study of the general principles of intelligence through a synthetic methodology termed learning by understanding[1].

1. Rolf Pheiger, C.S., Understanding Intelligence. 1999, Cambridge, MA: MIT Press. 720.

What is a Cognitive Agent?Cognitive Agents – They not just learn by trial and error…but they understand and set goals by inferring relationships using many data sources with minimum human intervention. They utilize:Uses casesTaxonomy and relationship rules that enable sensitivity of highly contextual context and situationsArtificial Neural Networks to evaluate outcomesElectronic reasoning simulation which consist of three key components: [2]

1. problem solving (planning);

2. Comprehension (story associated with the understanding)

3. Learning (remembering the outcome of use case)

2. Cox, M.T., Perpetual Self-Aware Cognitive Agents, in Intelligent Distributed Computing. 2007, American Association for Artificial Intelligence (www.aaai.org).

Cognitive Cycle

Observe the situation

Form a goal or appropriatebehavior

Create a plan to achieve

the goal

Act in accordance to the plan

Gather information about user activities

Formulate scenariosand define use cases

Apply and test application

Develop data points and algorithms

Robots using Cognitive Agents

Robots learn social cues via Cognitive Agents- Robots as a Social Technology - research by Cynthia Breazeal

Self Aware Robots - The learn their environment, understand themselves and even self-replicate – research by Hob Lipson

Robotic Comedian - it gathers audience feedback to tune its act – research from Heather Knight

PRODOGY high level design

INTRO Architecture

Context Awareness

Watson – Taxonomy & Relationships

Search Scenario

Cloud Scenario

Dynamic Cloud Images

Cloud user customize their images 36% of the time

Cloud Scenario

Cloud Adoption is Limited by Trust

Social Networks Could be used to Augment Cognition

Compliance Scenario

Compliance Scenario

Compliance Scenario

Mobile Scenario

Conclusion Enterprises embrace cloud computing design patterns

for solving problems that they otherwise would shy away from due to infrastructure constraints.

Applying cognitive agent research and principles to existing distributed businesses, real-world automation can be enabled.

Social software should be embraces not only as an enabler of collaboration but as the source of implicit and explicit connections. Being able to mine and understand these connections will result in smarter systems.

Finally, one area of concern is employee privacy. If taken too far cognitive agents could potentially appear “big brother” in nature.

References1. Azua, M., The social factor : innovate, ignite, and win through mass collaboration and social

networking. 2010, Upper Saddle River, NJ: IBM Press/Pearson. 247 p.

2. Malik, O. Wholesale Internet Bandwidth Prices Keep Falling: . 2008 [cited 2010 2010-12-04 15:21:25]; Available from: http://gigaom.com/2008/10/07/wholesale-internet-bandwidth-prices-keep-falling/.

3. Pankaj Deep Kaur, I.C., Unfolding the Distributed Computing Paradigms, in 2010 International Conference on Advances in Computer Engineering. 2010: Bangalore, India. p. 339 - 342.

4. Rolf Pheiger, C.S., Understanding Intelligence. 1999, Cambridge, MA: MIT Press. 720.

5. Cox, M.T., Perpetual Self-Aware Cognitive Agents, in Intelligent Distributed Computing. 2007, American Association for Artificial Intelligence (www.aaai.org).

6. Caprarescu, B.A., Robustness and scalability: a dual challenge for autonomic architectures, in Proceedings of the Fourth European Conference on Software Architecture: Companion Volume. 2010, ACM: Copenhagen, Denmark. p. 22-26.

7. Baral, C., et al., Using answer set programming to model multi-agent scenarios involving agents' knowledge about other's knowledge, in Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1. 2010, International Foundation for Autonomous Agents and Multiagent Systems: Toronto, Canada. p. 259-266.

8. Veloso, M. PRODIGY Project Home Page. 2010 Dec 12, 2010 [cited 2010 Dec 12, ]; Available from: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/prodigy-home.html.

9. IBM. Watson - A System Designed for Answers [Online Multimedia on IBM.com site] 2011 [cited 2011 Feb 1]; Watson cognitive agent competes on Jeopardy ]. Available from: URL: http://www.ibm.com/innovation/us/watson/.

10. Yen, N.Y., T.K. Shih, and L.R. Chao, Adaptive learning resources search mechanism, in Proceedings of the second ACM international workshop on Multimedia technologies for distance leaning. 2010, ACM: Firenze, Italy. p. 7-12.