An Algorithm for Bootstrapping Communications Jun Wang 03/20/03

Amorphous Computing Amorphous computing is the development of organizational principles and programming languages for obtaining coherent behavior from the cooperation of myriads of unreliable parts that are interconnected in un known, irregular, and time-varying ways.

Aim of amorphous computing Structuring systems so we get acceptable answers, with high probability, even in the face of unreliability

Motivation “If I were designing the human brain, how would I have the parts learn to communicate?” A kind of amorphous computing

A possible solution A,B: two agents Communication Lines: a bundle of wires with an arbitrary and unknown permutation Feature Lines: connection with outside world

Work mechanism Communication lines have four states: 1, -1, 0, x; Feature lines are named by things or actions and driven by roles; Typical feature lines: bob, mary, push … Typical roles: subject, object, verb…

Performance Evaluation Method Training cycles – data on the feature lines is sent to both agents; Test cycles – one agent has no input. How well the output of it matches the values of the other agent’s feature line?

Encoding View

An Example (1)

An Example (2)

An Example (3) A symbol is expressed by a subset of communication wires; symbol mapping: (x s,x c,x u,x n ) Inflection mapping: (role, value)

Algorithm Analysis Talk-in Talk outListen in Listen out Basic idea: each agent transmits the mapping information to other by driving the communication lines, and modify his own mapping information according to the other’s mapping information. After some cycles, the two agents will get the same symbol mapping.

Formal Automation Description(1) Talk in: Adding new elements into two mapping relation; updating communication lines states; Talk out: Driving the communication lines

Formal Automation Description(2) Listen in: (1) Symbol mapping adjustment: two agents listen to each other in every cycle and modify the “certain” subsets. Finally, they can reach to a same symbol mapping eventually; (2) Preparing the output for listen-out; (3) Inflection mapping adjustment; Listen out: output the (symbol,role) pairs to feature lines according to the understanding.

Results N w = 10000; N wps = 100; Training cycles = 1000; Thematic frames: 50 nouns, 20 verbs Results: 200 cycles for share vocabulary; 500 cycles for inflection stability

Algorithm feature Structural parsimony Robustness Shallow computation

Performance Degradation

Dissimilar Features The algorithm can share vocabulary despite the handicaps imposed like some non-shared vocabulary between them.