INTRODUCTION TO A.I.

"Imagine a group of human specialists seated next to a large blackboard. The specialists are working cooperatively to solve a problem, using the blackboard as the workplace for developing the solution.

Problem solving begins when the problem and initial data are written onto the blackboard. The specialists watch the blackboard, looking for an opportunity to apply their expertise to the developing solution. When a specialist finds sufficient information to make a contribution, she records the contribution on the blackboard, hopefully enabling other specialists to apply their expertise. This process of adding contributions to the blackboard continues until the problem has been solved." [REF]

• Experts: Processing entities with domain expertise
• Blackboard: Common data storage through which knowledge sources communicate
• Control system, for deciding:
  • which experts should get which information
  • when experts should return results (how long they run)

The key is how experts are triggered by information that appears on the BB. This can be done in the BB or in the expert, or in both places.

Method I ("Soviet model"): The BB has a list of which experts are relevant for any kind of content. When content X appears on the BB, the BB will match it with its list of experts and send X to those experts that seem to be able to process and respond to it.

Method II ("market model"): In this version the BB is a dumb information bus (infobus); the BB has a complete list of all experts that exist and will simply forward all information that comes in to every expert. The experts will then have to decide, each for themselves, which of the information they are capable of acting upon.

Variation: Most systems are hybrids - a combination of the two extremes above, as method I puts too much burden on the BB and method II too much burden on the experts.

Upon receiving a problem pattern, the BB matches it against the patterns of each expert.
In this example the BB finds expert PoS-Verb-Expert-1 to be relevant for all the problems, as the last part, (PoS x ?), of

(PoS-Noun-Expert-1 (E (PoS x ?)))

matches all problems posted in S.

(H (PoS dawg noun))
(H (PoS bown noun))
(H (PoS bown adjective))*

The experts would probably post the spelling of the words that they hyopthesize the tokens to stand for, e.g. (H (PoS bown brown adjective)) and (H (PoS bown bone noun)).

They might also include an estimated probability that their hypothesis is correct: (H 0.76 (PoS bown brown adjective))

(H (PoS the det))
(H (PoS dawg noun))
(H (PoS aite verb))
(H (PoS the det))
(H (PoS bown verb))
(H (PoS bown adjective))

Constructionist AI mantra: No method off-limits - the goal is simply increased flexibility = increased machine intelligence.

Use well-known mechanisms to solve problems they apply well to

• Expand existing architectures
• Develop large architectures from scratch
• Achive human-like features
• Explore integration of very different mechanisms
• Integrate learning into heterogeneous architectures

"Hybrid architectures"

• Typically don't buy into a particular school of thought
• Very often hierarchical like the subsumption architecture, but more flexible

• Modules
• Messages
• Blackboards
• Publish-subscribe

• Based on Constructionist AI philosophy
• Module-based framework
• Bridges between programming languages
• Runs multi-module systems on multiple computers