Intelligent sociable computer systems

A computer system for providing emotional expression in an autonomous decision system, comprising:

• a. computer processing means for processing data;
• b. storage means for storing data on a storage medium;
• c. planning-data means for storing data for providing plan capability to said autonomous decision system;
• d. planning means for processing data regarding circumstances of said autonomous decision system to provide planning selections and planning status;
• e. status means for processing data regarding current planning status to provide emotion-source data;
• and
• f. emotion means for processing data regarding emotion-source information to provide current emotion status.

A computer system for implementing natural language functions in a humanoid autonomous decision system, comprising:

• a. computer processing means for processing data;
• b. storage means for storing data on a storage medium
• i. wherein said data comprises non-linguistic discrete data-types and, conforming to each of said discrete non-linguistic data-types, a set of non-linguistic discrete data elements;
• c. input means for providing information about current circumstances of the humanoid autonomous decision system;
• d. output means for implementing decisions of the humanoid autonomous decision system;
• e. relevance means for providing information regarding the relevance to the humanoid autonomous decision system of said current circumstances, comprising
• i. self-representation means for processing data regarding “self” to provide at least one “self” representation,
• ii. structured-situation means for processing data regarding said current circumstances to provide a first non-linguistic structured “self”-situation representation,
• iii. relational-situation storage means for providing data regarding a set of hierarchically-organized, relevant, non-linguistic relational “self”-situations, and
• iv. inclusional means for processing data to determine inclusions of a said first non-linguistic structured “self”-situation within said non-linguistic relational “self”-situations to determine any relevance of said first structured “self”-situation to a said “self” of said relevance means,
• v. wherein said data regarding said set of hierarchically-organized, relevant, non-linguistic relational “self”-situations includes data regarding
• (1) a set of hierarchically-organized problem relational “self”-situations, and
• (2) in association with essentially each of said problem relational “self”-situations, a set of hierarchically-organized plan relational “self”-situations;
• f. type-linking storage means for providing data regarding
• i. respectively linking essentially each said discrete data-type of said humanoid autonomous decision system with a respective word/phrase category of a first natural language, and
• ii. respectively linking selected words/phrases of each said linked word/phrase category of said first natural language with respective said discrete data elements of each said discrete data-type so linked with a said linked word/phrase category;
• and
• g. data transformation means for processing data regarding a first communication to be made by said humanoid autonomous decision system to transform a specified set of non-linguistic data elements into a said first communication in said first natural language, comprising
• i. identification means for processing data regarding identifying which of said discrete data elements of said discrete data-types is to form part of said first communication,
• ii. snippet means for processing data regarding selecting natural-language snippets for pointing to the said categories of said natural-language corresponding to whichever of said discrete data-types includes each said discrete data element which is to form part of said first communication,
• iii. vocabulary means for processing data regarding selecting a word/phrase of said natural-language corresponding to each said discrete data element which is to form part of said first communication, and
• iv. grammar means for processing data regarding producing from the grammar practices of said natural language and from said snippet selections and from said word/phrase selections said first communication in said natural language.

A computer system for implementing first natural language interpretation functions in a humanoid autonomous decision system interpreting incoming first natural language from an other, comprising:

• a. computer processing means for processing data;
• b. storage means for storing data on a storage medium
• i. wherein said data comprises non-linguistic discrete data-types and, conforming to each of said discrete non-linguistic data-types, a set of non-linguistic discrete data elements;
• c. type-linking storage means for providing data regarding
• i. respectively linking essentially each said discrete data-type of said humanoid autonomous decision system with a respective word/phrase category of said first natural language, and
• ii. respectively linking selected words/phrases of each said linked word/phrase category of said first natural language with respective said discrete data elements of each said discrete data-type so linked with a said linked word/phrase category;
• d. input means for providing input information about characteristics of said incoming natural language sufficient to identify each vocabulary element, snippet type for each said element, and grammatical function for each said element;
• e. translation means for processing data regarding said input information to provide a non-natural-language concrete circumstance interpretation of said input information;
• f. relevance means for providing information regarding the relevance to the humanoid autonomous decision system of said circumstance interpretation, comprising
• i. relational-situation storage means for providing data regarding a set of hierarchically-organized, relevant, non-linguistic relational “self”-situations, and
• ii. inclusional means for processing data to determine inclusions of said non-natural-language concrete circumstance interpretation within said non-linguistic relational “self”-situations to determine any relevance of said non-natural-language concrete circumstance interpretation a said “self” of said relevance means,
• iii. wherein said data regarding said set of hierarchically-organized, relevant, non-linguistic relational “self”-situations includes data regarding
• (1) a set of hierarchically-organized problem relational “self”-situations, and
• (2) in association with essentially each of said problem relational “self”-situations, a set of hierarchically-organized plan relational “self”-situations.

A computer system for machine learning from an environmental situation in an autonomous decision system, comprising:

• a. computer processing means for processing data;
• b. storage means for storing data on a storage medium;
• c. input means for providing temporally-incremental input data about a series of said environmental situations;
• d. concrete-situation means for processing data regarding said temporally-incremental input data about said series of said environmental situations to provide a temporally-incremental series, respectively, of “present” concrete self-situation representations of said respective environmental situations, wherein
• i. each said self-situation representation comprises a self representation and a set of event representations, each said event representation being represented specifically spacio-temporally relative to said self representation, and each said event representation including
• (1) a behavioral-type designation selected from a set of behavioral-type designations, each said behavioral-type designation of said set of behavioral-type designations being associated with a set of incremental behavioral self-tendencies for determining incrementally-predicted self-situation representations from a said presented self-situation representation;
• and
• (2) a set of current-behavior designations associated with each said event representation specifying the current behaviors of each said event representation.

A computer system for an entertainment system, comprising:

• a. computer simulation means for processing data regarding a user-selected “world”-representation containing user-selected spacio-temporally located “object”-representations to provide incremental simulation-stepping of said “world”-representation;
• b. computer interface means for processing data for providing a natural-language interface for user selection of non-natural-language characteristics of a said “object”-representation;
• c. computer interface means for processing data for providing an interface for user placement of a said “object”-representation into said “world”-representation;
• d. computer interface means for processing data for providing an interface for placement into said “world”-representation, as a said “object”-representation, of an autonomous decision system;
• e. wherein said autonomous decision system comprises
• i. representation means for processing data essentially from said “world”-representation for presenting a selected self-situation (for said autonomous decision system) representation, said presented self-situation representation comprising a self representation and a set of event representations, each said event representation being represented specifically spacio-temporally relative to said self representation, and
• ii. prediction means for processing data for determining the representations of a set of incrementally-predicted self-situations, predicted as incremental consequences from said presented self-situation representation;
• iii. wherein said prediction means comprises said simulation means.

A computer program, when executed on a computer system, generates a computationally-efficient representation for classifying natural objects along a concrete-to-abstract scale in such manner as to support improved correspondence with natural languages, comprising, in combination, the steps of:

• a. storing in a computer information-storage device a set of non-natural-language markers, each said marker being unique;
• b. associating a subset of said set with a representation of a said natural object;
• and
• c. identifying a more-abstract representation of said natural object by a sub-subset of said subset.

A computer program, when executed on a computer system, generates a mapping representation of “concrete” objects, usable in a computer simulation program, comprising the steps of:

• a. storing in computer storage representations of a set of “concrete” objects;
• b. associating with each said “concrete” object representation an identified first set of prediction factors selected from a second set of prediction factors;
• c. wherein each said prediction factor of said second set of prediction factors consists essentially of an instruction rule for the incremental simulation-type movement of said associated “concrete” object representation when in a specified spacio-temporal relationship with a set of identified “abstract” object representations;
• and
• d. wherein each said “abstract” object representation comprises a prediction-factor marker, each said prediction-factor marker having a 1-to-1 correspondence with a unique one of said second set of prediction factors.