pmpj1009

Perl Ghost AI uses EnVerbGen() for English verb-form inflections.

In the middle of coding ghost278.pl AI we had to go and stand in front of the television and watch Leopold Stokowski in 1969 conducting the finale of Beethoven's Symphony No. Five -- the one they sent into outer space as a message from Earth. Now back at the computer, for the first time we are trying to implement the EnVerbGen() module for English verb generation. We have gotten the InFerence() module to generate an inference when we type in "anna is a student', because the AI Mind knows that students read books. The AskUser() module seeks to verify or validate the inference by asking us, "DOES ANNA READ THE BOOKS". When we answer "no", the AI says, "THE ANNA DOES NOT READ THE BOOKS". When we answer yes, the ghost in the machine issues the faulty output of "THE ANNA READ THE BOOKS", which sounds more like an exhortation than a statement of confirmed fact with a high truth-value. We need a way to get the AI to use the third-person singular form "READS" with the singular subject. To do so, before Leopold and Ludwig interrupted us, we were embedding diagnostic messages in the EnVerbPhrase() module, trying to determine how the ghost AI was able to say "READ" as if it were the proper verb-form. The whole idea of EnVerbGen() in English or of RuVerbGen() in Russian is for the verb-phrase module to seek a particular verb-form based on parameters of person and number, and to call EnVerbGen() if the desired verb-form is not already available in auditory memory. Somehow the existing Perlmind is finding the verb "read" but not the correct form of the verb.

We discover that we can get the EnVerbPhrase() module to call EnVerbGen() when we tighten up the search-by-parameter for the correct verb form. Since EnVerbGen() is not coded yet, we get an output of "THE ANNA ERROR THE BOOKS", with "ERROR" filling in for the lacking "READS" form.

Then we need an $audbase value that we can send into EnVerbGen() as the start of the verb that needs an inflectional ending. We use a trick in the EnVerbPhrase() module to get either a second-class or a first-class (infinitive) $audbase. We test first for any form at all of the verb that has an auditory engram that can serve as a second-class $audbase, because the verb-form may be defective in some way. In the very next line of code, we test for an infinitive form of the verb having an auditory engram as a first-class $audbase, because an infinitive is easier to manipulate than some defective form of the verb.

We copied the bulk of the Russian RuVerbGen() into the English EnVerbGen() and then we did the mutatis mutandis process of making the necessary changes. At first we got "REAS" instead of "READS" because the Russian Cyrillic characters were substituting, not adding. By removing the substitution-code, we obtained the full verb "READS". At a later time we must code in the handling of verbs like "teach" or "push" which require an "-ES" ending.

pmpj0919

Students may teach the first working artificial intelligence.

In the ghost274.pl version of the Perlmind AI, we are having not Volition() but rather EnThink() call the Imperative() module to blurt out the command "TEACH ME SOMETHING". We are also trying to have Imperative() be the only module that sounds a beep for the human user, for several reasons. Although we were having the AskUser() module sound a beep to alert the user to the asking of a question, a beep can be very annoying. It is better to reserve the beep or "bell" sound for a special situation, namely the time when there has been no human input for an arbitrary period as chosen by the AI Mind maintainer and when we wish to let the Ghost in the Machine call out for some attention.

We are also eager for Netizens to set the potentially immortal ghost.pl AI running for long periods of time, both as a background process on a desktop computer or a server, and as part of a competition to see who can have the longest running AI Mind.

If a high school or college computer lab has the ghost.pl AI running on a machine off in the corner while the students are tending to other matters, a sudden beep from the AI Mind may cause students or visitors to step over to the AI and see what it wants. "TEACH ME SOMETHING" is a very neutral command, not at all like, "Shall we play a game? How about GLOBAL THERMONUCLEAR WAR?"

The teacher or professor could let any student respond to the beep by adding to the knowledge base of the AI Mind. Of course, clever students with a knowledge of Perl could put the AI Mind out on the Web for any and all visitors to interact with.

pmpj0705

Improving the storage of conceptual flag-panels during input.

In the ghost266.pl Perlmind we need to improve upon a quick-and-dirty bugfix from our last coding session. After a silent inference and the operation of AskUser() calling EnAuxVerb(), the Ghost AI was going into the verb-concept of the inference-triggering input and replacing a correct $tkb value with a zero. Apparently the time-of-verb $tvb value, set in the Enparser() module during the parsing of a verb, was being erroneously carried over from the verb of user-input to the verb 830=DO in the EnAuxVerb() module during the generation of an inference-confirming question by the AskUser() module. Therefore the time-of-verb $tvb-flag needs to be reset to zero not during the generation of a response to user-input but rather at the end of the user-input. However, we find that we may not reset time-of-verb $tvb to zero during AudInput(), apparently because only character-recognition and not yet word-recognition has taken place. The $tvb-setting for a verb must remain valid throughout AudInput() so that the EnParser() module may use the time-of-verb $tvb flag to store a direct object as the $tkb of a verb. Accordingly we reset the $tvb-flag to zero in the Sensorium() module after the call to AudInput(). We stop seeing a $tkb of zero on the verb of an input that triggers automated reasoning with logical InFerence.

pmpj0703

Keeping AskUser from storing incorrect associative tags.

The ghost265.pl version of the Perlmind has a problem after making a logical inference. Instead of getting back to normal thinking, some glitch is causing the AI to say "ANNA BE NOT ANNA".

As we troubleshoot, we notice a problem with the initial, inference-evoking input of "anna is woman". The be-verb is being stored in the psy array with a $tkb of zero instead of the required time-point of where the concept of "WOMAN" is stored. This lack of a $nounlock causes problems later on, which do not warrant their own diagnosis because they are a result of the lacking $nounlock. We need to inspect the code for where the be-verb is being stored in the psy-array, but we are not sure whether the storage is occuring in the InStantiate() module, or in OldConcept(), or in EnParser(). We see that the Ghost AI is trying to store the be-verb in the EnParser() module with the correct tkb, but afterwards a tkb, of zero is showing up. We must check whether InStantiate() is changing what was stored in the EnParser() module.

Meanwhile we notice something strange. An input of "anna is person" gets stored properly with a correct $tkb, but "anna is woman" -- causing an inference -- is stored with a $tkb, of zero. When we enter "anna is robot", causing an inference and the output "DOES ANNA WANT BEEP", there is also a zero $tkb. Upshot: It turns out that the EnAuxVerb() module, called by AskUser() after an inference, was setting a wrong, carried-over value on the time-of-verb $tvb variable, which was then causing InStantiate() to go back to the wrong time-of-verb and set a zero value on the $tkb flag. So we zero out $tvb at the start of EnAuxVerb().

mfpj0624

Logical Inference in the First Working AGI MindForth

Over the past week fifteen or twenty hours of intense work went into coding the InFerence, AskUser and KbRetro modules in the Forth version of the First Working AGI. Interactively we could see that the Forthmind was making a silent inference from our input of "anna is a woman" but the AskUser module was substandardly asking "DO ANNA HAS CHILD?" in seeking confirmation of the silent inference. When we entered "no" as an answer, we could not see if the KbRetro module was properly inserting the 250=NOT adverb into the conceptual engrams of the silent inference so as to negate the inferred idea. Therefore today in the agi00056.F version of MindForth we are starting our diagnostic display about forty-five time-points earlier than the computed value of the time-of-input tin variable so that we can see if the inferred idea is being retroactively adjusted by KbRetro. At first blush, no insertion of 250=NOT is happening, so we start inserting diagnostic messages into the code.

Our diagnostics suggest that KbRetro is not being called, but why not? It turns out that we have not yet coded 404=NO or 432=YES or 230=MAYBE into the MindBoot sequence, so we code them in. Then we start getting a faulty output after answering "no" to AskUser. The AI says, "ANNA NOT NOT CHILD". Apparently EnVerbPhrase is not properly negating the refuted silent inference. After several hours of troubleshooting, the desired output appears.

When we enter "anna is woman" and we answer "no" to the question whether Anna has a child, the conceptual array shows the silent inference below at the time-points 3068-3070:

The arrays psy{ and ear{ show your input and the AI output:
time: tru psi hlc act mtx jux pos dba num mfn pre iob seq tkb rv -- pho act audpsi

3053 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   65 0 0 A
3054 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3055 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3056 : 0 502 0 -40 0 0 5 1 1 2 0 0 800 3059 3053   65 0 502 A
3057 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3058 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   73 0 0 I
3059 : 0 800 0 -40 0 250 8 4 1 2 0 0 515 3065 3058   83 0 800 S
3060 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3061 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   87 0 0 W
3062 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   79 0 0 O
3063 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   77 0 0 M
3064 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   65 0 0 A
3065 : 0 515 0 -40 0 0 5 0 2 2 0 0 0 0 3061   78 0 515 N
 066 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   13 0 0
3067 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3068 : 0 502 0 -26 0 0 5 1 1 0 0 0 810 3069 0   32 0 0
3069 : 0 810 0 56 0 250 8 0 0 0 502 0 525 3070 0   32 0 0
3070 : 0 525 0 32 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3071 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3072 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   68 0 0 D
3073 : 0 830 0 -40 0 0 8 0 1 2 0 0 810 0 3072   79 0 830 O
3074 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3075 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3076 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   65 0 0 A
3077 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3078 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3079 : 0 502 0 -40 0 0 5 1 1 2 0 0 810 3084 3076   65 0 502 A
3080 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3081 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3082 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   72 0 0 H
3083 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   65 0 0 A
3084 : 0 810 0 -40 0 0 8 4 2 2 0 0 525 3096 3082   83 0 810 S
3085 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3086 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3087 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   84 0 0 T
3088 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   72 0 0 H
3089 : 0 117 0 -40 0 0 1 0 1 2 0 0 810 0 3087   69 0 117 E
3090 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3091 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3092 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   67 0 0 C
3093 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   72 0 0 H
3094 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   73 0 0 I
3095 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   76 0 0 L
3096 : 0 525 0 -40 0 0 5 0 1 2 0 0 810 0 3092   68 0 525 D
3097 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3098 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3099 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3100 : 0 404 0 -40 0 0 4 0 1 2 0 0 0 0 3099   79 0 404 O
 101 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   13 0 0
3102 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   65 0 0 A
3103 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3104 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3105 : 0 502 0 -42 0 0 5 1 1 2 0 0 810 3122 3102   65 0 502 A
3106 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3107 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3108 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   68 0 0 D
3109 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   79 0 0 O
3110 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   69 0 0 E
3111 : 0 830 0 -42 0 0 8 1 1 2 0 0 0 0 3108   83 0 830 S
3112 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3113 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3114 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   78 0 0 N
3115 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   79 0 0 O
3116 : 0 250 0 -42 0 0 2 1 1 2 0 0 0 0 3114   84 0 250 T
3117 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3118 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3119 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   72 0 0 H
3120 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   65 0 0 A
3121 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   86 0 0 V
3122 : 0 810 0 -42 0 250 8 4 2 2 0 0 525 3129 3119   69 0 810 E
3123 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3124 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3125 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   67 0 0 C
3126 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   72 0 0 H
3127 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   73 0 0 I
3128 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   76 0 0 L
3129 : 0 525 0 -42 0 0 5 1 1 2 0 0 0 0 3125   68 0 525 D
3130 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
3131 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   32 0 0
time: tru psi hlc act mtx jux pos dba num mfn pre iob seq tkb rv

Robot alive since 2018-06-24:
ANNA  DOES  NOT  HAVE  CHILD

Since we negate the inference with our response of "no", KbRetro inserts the adverb "250" (NOT) at the time-point 3069 to negate the verb 810=HAVE. Then the AI states the activated idea of the negation of the inference: "ANNA DOES NOT HAVE CHILD".

jmpj0613

JavaScript Mind Programming Journal (JMPJ) -- Saturday, June 13, 2015

These notes record the coding of the English tutorial AiMind.html in JavaScript for Microsoft Internet Explorer (MSIE).

Sat.13.JUN.2015 -- Troubles with InFerence in JavaScript

When we run the JavaScript AiMind.html in English and we try to show a Transcript of automated reasoning with logical InFerence, the Strong AI does indeed make an inference, but the dialog with the AI reveals that the AiMind program is failing to use some correct forms of verbs and personal pronouns. The thinking of the AI is correct and logical, but some mistakes are occurring in the expression of logical thought in proper English.

We suspect that grammatical errors are creeping in because the mind-modules related to inference are composing a sentence of thought outside of the normal routines of strictly grammatical English. We may be able to build up the same formalisms of strict grammaticality inside the inferential routines. For correct verb forms, however, we may need to start using the modules of OutBuffer and VerbGen.

Sat.13.JUN.2015 -- Troubleshooting the InFerence process

We notice that the AskUser module of the 14apr13A JSAI simply looks for the "quverb" query-verb to recall and speak, apparently without forcing the verb into the proper grammatical form, which is typically an infinitive form when a question is being asked with "DO" or "DOES" as an auxiliary verb. We should also check the Forth code and see if AskUser in MindForth has anything more advanced. Oh, the Forth code actually does test for a plural form to be used as if it were an infinitive.

The JSAI AskUser module looks for the "quobj" query-object without bothering to ensure that it will be an accusative form. The MindForth AskUser module also does not bother to check for an accusative case in the "quobj" word, so both the JavaScript AI and the MindForth AI need to be improved. The German Forth AI Wotan also seems to need improvement for grammatical forms in the AskUser module.

Mon.15.JUN.2015 -- Selecting Objects in Accusative

Now we have partially fixed the problem of ungrammatical English by inserting code into the AskUser() module to require the direct object or query-object to be in the accusative case. Instead of asking a question like "Does Mark need I?" the AI now asks, "Does Mark need me?"

However, when we answer "no" to the foregoing quesion, the AI eventually gets around to saying, "MARK DOES NOT NEEDS ME", because the AskUser() module is not insisting upon finding an infinitive form of the query-verb.

Table of Contents (TOC)

• Sat.13.JUN.2015 -- Troubles with InFerence in JavaScript

• Sat.13.JUN.2015 -- Troubleshooting the InFerence process

• Mon.15.JUN.2015 -- Selecting Objects in Accusative

mar16dkpj

The DeKi Programming Journal (DKPJ) is both a tool in coding German Wotan open-source artificial intelligence (AI) and an archival record of the history of how the German Supercomputer AI evolved over time.

1 Thurs.14.MAR.2013 -- Seeking Confirmation of Inference

In the German Wotan artificial intelligence with machine reasoning by inference, the AskUser module converts an otherwise silent inference into a yes-or-no question seeking confirmation of the inference with a yes-answer or refutation of the inference with a no-answer. Prior to confirmation or refutation, the conceptual engrams of the question are a mere proposition for consideration by the human user. When the user enters the answer, the KbRetro module must either establish associative tags from subject to verb to direct object in the case of a yes-answer, or disrupt the same tags with the insertion of a negational concept of "NICHT" for the idea known as "NOT" in English.

2 Fri.15.MAR.2013 -- Setting Parameters Properly

Although the AskUser module is asking the proper question, "HAT EVA EIN KIND" in German for "Does Eva have a child?", the concepts of the question are not being stored properly in the Psi conceptual array.

3 Sat.16.MAR.2013 -- Machine Learnig by Inference

Now we have coordinated the operation of InFerence, AskUser and KbRetro. When we input, "eva ist eine frau" for "Eva is a woman," the German AI makes a silent inference that Eva may perhaps have a child. AskUser outputs the question, "HAT EVA EIN KIND" for "Does Eva have a child?" When we answer "nein" in German for English "no", the KbRetro module adjusts the knowledge base (KB) retroactively by negating the verb "HAT" and the German AI says, "EVA HAT NICHT EIN KIND", or "Eva does not have a child" in English.

mar13dkpj

The DeKi Programming Journal (DKPJ) is both a tool in coding German Wotan open-source artificial intelligence (AI) and an archival record of the history of how the German Supercomputer AI evolved over time.

1 Sat.9.MAR.2013 -- Making Inferences in German

When the German Wotan AI uses the InFerence module to think rationally, the AI Mind creates a silent, conceptual inference and then calls the AskUser module to seek confirmation or refutation of the inference. While generating its output, the AskUser module calls the DeArticle module to insert a definite or indefinite article into the question being asked. The AI has been using the wrong article with "HAT EVA DAS KIND?" when it should be asking, "HAT EVA EIN KIND?" When we tweak the software to switch from the definite article to the indefinite article, the AI gets the gender wrong with "HAT EVA EINE KIND?"

2 Tues.12.MAR.2013 -- A Radical Departure

In the AskUsermodule, to put a German article before the direct object of the query, we may have to move the DeArticle call into the backwards search for the query-object (quobj), so that the gender of the query-object can be found and sent as a parameter into the DeArticle module.

It may seem like a radical departure to call DeArticle from inside the search-loop for a noun, but only one engram of the German noun will be retrieved, and so there should be no problem with inserting a German article at the same time. The necessary parameters are right there at the time-point from which the noun is being retrieved.

3 Wed.13.MAR.2013 -- Preventing False Parameters

When the OldConcept module recognizes a known German noun, normally the "mfn" gender of that noun is detected and stored once again as a fresh conceptual engram for that noun. However, today we have learned that in OldConcept we must store a zero value for the recognition of forms of "EIN" as the German indefinite article, because the word "EIN" has no intrinsic gender and only acquires the gender of its associated noun. When we insert the corrective code into the OldConcept module, finally we witness the German Wotan AI engaging in rational thought by means of inference when we input "eva ist eine frau", or "Eva is a woman." The German AI makes a silent inference about Eva and calls the AskUser module to ask us users, "HAT EVA EIN KIND", which means in English, "Does Eva have a child?" Next we must work on KbRetro to positively confirm or negatively adjust the knowledge base in accordance with the answer to the question.

mar5dkpj

The DeKi Programming Journal (DKPJ) is both a tool in coding German Wotan open-source artificial intelligence (AI) and an archival record of the history of how the German Supercomputer AI evolved over time.

1 Sun.3.MAR.2013 -- Problems with AskUser

In our efforts to implement InFerence in the Wotan German AI, we have gotten the AI to stop asking "HABEN EVA KIND?" but now AskUser is outputting "HAT EVA DIE KIND" as if the German noun "Kind" for "child" were feminine instead of neuter. We should investigate to see if the DeArticle module has a problem.

2 Mon.4.MAR.2013 -- Problems with DeArticle

By the use of a diagnostic message, we have learned that the DeArticle module is finding the accusative plural "DIE" form without regard to what case is required. Now we need to coordinate DeArticle more with the AskUser module, so that when AskUser is seeking a direct object, so will DeArticle. There has already long been a "dirobj" flag, but it is perhaps time to use something more sophisticated, such as "dobcon" or even "acccon" for an accusative "statuscon". After a German preposition like "mit" or "bei" that requires the dative case, we may want to use a flag like "datcon" for a dative "statuscon". So perhaps now we should use "acccon" in preparation for using also "gencon" and "datcon" or maybe even "nomcon" for nominative.

3 Tues.5.MAR.2013 -- Coordinating AskUser and DeArticle

A better "statuscon" for coordinating between AskUser and DeArticle is "dbacon", because it can be used for all four declensional cases in German. When we use "dbacon" and when we make the "LEAVE" statement come immediately after the first instance of selecting an article with the correct "dbacon", we obtain "HAT EVA DAS KIND" as the question from AskUser after the input of "eva ist eine frau". We still need to take gender into account, so we may declare a variable of "mfncon" to coordinate searches for words having the correct gender.

mar2dkpj

The DeKi Programming Journal (DKPJ) is both a tool in coding German Wotan open-source artificial intelligence (AI) and an archival record of the history of how theGerman Supercomputer AI evolved over time.

1 Sat.2.FEB.2013 -- Improving the AskUser Module

To begin a yes-or-no question in German, a form of the verb has to be generated either by a parameter-search or by VerbGen. We will first try the parameter-search using dba for person and nphrnum for number.

2 Tues.26.FEB.2013 -- Assigning Number to a New Noun

For learning a new noun in German, we need to use the OutBuffer in the process of assigning grammatical number to any new noun. We can use a previous article to suggest the number of a noun, and we may impose a default number which may be overruled first by indications obtained from OutBuffer-analysis and secondly by the continuation with a verb that reveals the number of its subject.

For OutBuffer-analysis, we may impose various rules, such as that a default presumption of singular number may be overruled by certain word-endings such as "-heiten" or "-ungen" which would rather clearly indicate a plural form. We may not so easily presume that endings in "-en" or "-e" indicate a plural, because a singular noun may have such an ending. An ensuing verb is a much better indicator of the perceived number of a noun than the ending of the noun is.

Although we may be tempted to detect the ensuing singular verb "ist" and use it to retroactively establish a noun-number as being singular, it may be simpler to use the OutBuffer to look for singular verbs that end in "-t", such as "ist" or "geht". Likewise, a verb ending in "-n" could indicate a plural subject. So should the default presumption for a German noun be singular or plural?

3 Wed.27.FEB.2013 -- Assigning Plural Number by Default

In both German and English, we should probably make the default presumption be plural for new nouns being learned. Then we have a basic situation to be changed retroactively if a singular verb is detected. So let us examine the NewConcept module to see if we can set a plural value of "2" there on the "num" which will be imposed in the InStantiate module.

When we set a num default of "2" for plural in NewConcept and we run the German AI, the value of "2" shows up for a new noun in both the ".psi" report and the ".de" lexical report. Next we need to work on retroactively changing the default value on the basis of detecting a singular verb.

We have tried various ways to detect the "T" at the end of the input of the verb "IST". In the InStantiate module, we were able to test first for a pov of external input and then for the value of the OutBuffer rightmost "b16" value. Thus we were able to detect the ending "T" on the verb. Immediately we face the problem of how retroactively to change the default number of the subject noun from "2" for plural to "1" for singular.

Changing anything retroactively is no small matter in the Wotan German AI, because other words may have intervened between the alterand subject-noun and the determinant verb. We have previously worked on assigning tqv and seq values retroactively from a direct object back to a verb, so we do have some experience here.

4 Thurs.28.FEB.2013 -- Creating the RetroSet Module

Today we will try to create a RetroSet mind-module for retroactively setting parameters like the number of a new subject-noun which has been revealed to be singular in number because it was followed by a singular verb-form, such as "IST" or "HAT" in German. First we must figure out where to place the RetroSet module in the grand scheme of a Forth AI program. Since the "T" at the end of a German verb is discovered in the InStantiate module, we could either call RetroSet from InStantiate, or use a "statuscon" variable to set a flag that will call RetroSet from higher up in the Wotan AI program. Let us create a "numcon" flag that can be set to call Retroset and then immediately be reset to zero. Since InStantiate is called from the DeParser module, we should perhaps let DeParser call RetroSet.

Now we have stubbed in the RetroSet AI mind-module just before the DeParser mind-module in the Wotan German artificial intelligence. RetroSet diagnostically displays the positive value of the numcon flag and then resets the flag to zero. In future coding, we will use the numcon flag not only to call RetroSet but also to change the default value of "2" for plural to "1" for singular in the case of a new German noun that the Wotan AI is learning for the first time.

5 Fri.1.MAR.2013 -- Implementing RetroSet in the German AI

In the German Wotan potentially superintelligent AI, the AudListen module sets time-of-seqneed ("tsn") as a time-point for searches covering only current input from the keyboard into the AI Mind. In the new RetroSet module, we may use "tsn" as a parameter to restrict a search for a subject-noun to only the most recent input to the AI. However, "tsn" is apparently being reset for each new word of input, so we switch to using time-of-voice ("tov") and we get better results. We input "eva ist eine frau" and RetroSet retroactively changes the default plural on "EVA" from a two to a one for singular. Next we need to troubleshoot why we are not getting a better question from AskUser.