ConversationWorld
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Model was written in NetLogo 6.0-M6
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extensions [ls] globals [ meaning-language message-language objects relations all-meanings meaning-inputs message-inputs attempt-list accuracy-list ] patches-own [ patch-meaning ] turtles-own [ brain memory last-meaning last-message success?] to setup clear-all ls:reset set attempt-list [] set accuracy-list [] ;; get the meaning and message languages set up generate-languages ;; give each patch a message ask patches [ ;; there is a chance that the patch contains a relation, otherwise ;; there is an equal chance that it contains one or two objects ifelse (random 100) < relation-prob and num-relations != 0 [ set patch-meaning generate-meaning 2 1 ] [ifelse num-objects > 1 [ set patch-meaning generate-meaning (one-of (list 1 2)) 0 ] [ set patch-meaning generate-meaning 1 0 ]] ;; color the patch to weakly indicate what meaning it contains color-patch ] crt num-turtles [ set color red setxy random-xcor random-ycor setup-brain ] reset-ticks end to setup-test clear-all ls:reset set attempt-list [] set accuracy-list [] ;; get the meaning and message languages set up generate-languages ;; give each patch a message ask patches [ ;; there is a chance that the patch contains a relation, otherwise ;; there is an equal chance that it contains one or two objects ifelse (random 100) > relation-prob and num-relations != 0 [ set patch-meaning generate-meaning 2 1 ] [ifelse num-objects > 1 [ set patch-meaning generate-meaning (one-of (list 1 2)) 0 ] [ set patch-meaning generate-meaning 1 0 ] ]] crt num-turtles [ set color red setup-brain face patch-at min-pxcor min-pycor ] reset-ticks end to go set attempt-list [] ask turtles [ move talk update ] ;; append to the end of the list the mean accuracy of the model at this tick set accuracy-list lput mean attempt-list accuracy-list tick end ;; TURTLE PROCEDURES to setup-brain ;; sets up the turtle's brain set memory [] (ls:load-headless-model "ConversationWorldBrain.nlogo" [ set brain ? ]) ls:set-name brain (word "Brain of " self) (ls:ask brain [setup-brain ?1 ?2 ?3 ?4 ?5 ?6 ?7] meaning-language middle-layer num-middle-layers message-language meaning-to-message-iterations 10 learning-rate) end to-report map-meaning [p-meaning] ls:let inputs1 map [member? ? p-meaning] meaning-language report ls:report brain [ apply-bools1 inputs1 ] end to-report map-message [a-message] ls:let inputs2 map [member? ? a-message] message-language report ls:report brain [ apply-bools2 inputs2 ] end to update ;; send the contents of memory to child model to improve the mapping (ls:ask [brain] of self [ update-mappings ?] memory ) end to move ;; turn and move a random amount rt (random 60) - 30 forward 1 end to talk ;; pick someone to talk to let interlocutor one-of other turtles if (interlocutor != nobody) [ ;; map the meaning to a message let message ( map-meaning [patch-meaning] of patch-here ) ;; give the message to the other turtle and have them interpret it let recieved-meaning [map-message message] of interlocutor ;; check if there is a meaning mismatch ifelse recieved-meaning != map-meaning [patch-meaning] of patch-here [ set success? False ] [ set success? True ] ;; encode the present interaction in memory set last-meaning map [member? ? [patch-meaning] of patch-here ] meaning-language set last-message [map-meaning map [member? ? [patch-meaning] of patch-here ] meaning-language ] of interlocutor set memory lput (list last-meaning last-message) memory ;; if the contents of memory exceed its capacity, forget the oldest thing in memory if length memory > memory-capacity [ set memory but-first memory] ;; recorde whether the interaction was succesful ifelse (success?) [set attempt-list lput 1 attempt-list ][set attempt-list lput 0 attempt-list ] ] end ;; PATCH PROCEDURES to-report generate-meaning [ num-ob num-rel ] ;; helper function to generate messages let p-meaning ( list (n-of num-ob objects) (n-of num-rel relations)) report flatten-list p-meaning end ;; GENERAL PROCEDURES to generate-languages ;; sets up the language and meaning models generate-meaning-language generate-message-language set all-meanings generate-all-meanings end to generate-meaning-language ;; generates the sets of objects and relations, then combines them into a single list. set relations map [ word "r" ? ] (n-values num-relations [?]) set objects map [ word "o" ? ] (n-values num-objects [?]) set meaning-language flatten-list (list objects relations) end to generate-message-language ;; generates a list of words in the language set message-language map [ word "w" ? ] (n-values num-words [?]) end to-report generate-message ;; convience function to generate messages for the simple ;; brain used in the non-levelspace version of this model let word1 one-of message-language let word2 one-of message-language let word3 one-of message-language report (list word1 word2 word3) end to-report generate-all-meanings ;; generates all possible meanings with the present language let two_meanings cartesian-product objects objects let three_meanings cartesian-product two_meanings relations report reduce sentence ( list objects two_meanings three_meanings ) end to-report cartesian-product [ list1 list2 ] ;; produces a list of all possible pairs where the first element of each pair ;; comes from the first list and the second element comes from the second list report reduce sentence map [ cartesian-helper ? list2 ] list1 end to-report cartesian-helper [ element list2 ] ;; required by cartesian-product to work well. This could also be used to generate ;; a version of cartesian-product that takes an arbitrary number of lists, but since ;; I only need it to work on two lists I'm sparing the effort. report map [ ( list element ?) ] list2 end to-report flatten-list [ lst ] ; flattens nested lists to a single list. if (reduce [?1 or is-list? ?2] fput false lst) [ set lst reduce [sentence ?1 ?2] lst set lst flatten-list lst ] report lst end ;; Aesthetic functions to color-patch ;; colors the patches so that the color and hue of the patch kiiinda indicates the meaning on it let vec map [member? ? patch-meaning ] meaning-language set vec map [ifelse-value ? [1][0]] vec set vec sum map [(5 * ? * (item ? vec)) + (50 * ?) ] n-values length vec [?] set pcolor vec end ;; REPORTERS to-report windowed-accuracy [window-n] ;; indexes the last WINDOW members of accuracy-list and takes the mean ifelse window-n > length accuracy-list [ report 0 ] [ report mean map [item ? reverse accuracy-list] n-values window-n [?] ] end to-report mean-attempt-list ifelse length attempt-list > 0 [ report mean attempt-list ] [ report 0 ] end
There is only one version of this model, created over 8 years ago by Nathan Couch.
Attached files
File | Type | Description | Last updated | |
---|---|---|---|---|
AMB-FinalPaper.docx | word | The Couch (2016) referred to in the documentation. | over 8 years ago, by Nathan Couch | Download |
ConversationWorldBrain.nlogo | extension | The code for the child models for this model. | over 8 years ago, by Nathan Couch | Download |
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