Abstract: A method for finding a solution to a problem is provided. The method includes storing candidate individuals in a candidate pool and evolving the candidate individuals by performing steps including (i) testing each of the candidate individuals to obtain test results, (ii) assigning a performance measure to the tested candidate individuals, (iii) discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and (iv) adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool.

Abstract: Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.

Abstract: The technology disclosed introduces a concept of training a neural network to create an embedding space. The neural network is trained by providing a set of K+2 training documents, each training document being represented by a training vector x, the set including a target document represented by a vector xt, a favored document represented by a vector xs, and K>1 unfavored documents represented by vectors xiu, each of the vectors including input vector elements, passing the vector representing each document set through the neural network to derive an output vectors yt, ys and yiu, each output vector including output vector elements, the neural network including adjustable parameters which dictate an amount of influence imposed on each input vector element to derive each output vector element, adjusting the parameters of the neural network to reduce a loss, which is an average over all of the output vectors yiu of [D(yt,ys)?D(yt, yiu)].

Abstract: Roughly described, a system for user identification of a desired document. A database identifies a catalog of documents in an embedding space, in which the distance between documents corresponds to a measure of their dissimilarity. The system presents an initial collection of the documents toward the user from an initial candidate space which is part of the embedding space, then in response to iterative user input, refines the candidate space and subsequent collections of documents presented toward the user. The initial collection is determined using a weighted cost-based iterative addition to the initial collection of documents from the initial candidate space, trading off between two sub-objectives of representativeness and diversity.

Abstract: Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.

Abstract: Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.

Abstract: A method for identifying a desired document is provided to include calculating a Prior probability score for each document of a candidate list including a portion of documents of an embedding space, the Prior probability score indicating a preliminary probability, for each document of the candidate list, that the document is the desired document, and identifying an initial (i=0) collection of N0>1 candidate documents from the candidate list in dependence on the calculated Prior probability scores, the initial collection of candidate documents having fewer documents than the candidate list. The method further includes, for each i'th iteration in a plurality of iterations, beginning with a first iteration (i=1) and in response to user selection of an i'th selected document from the (i?1)'th collection of candidate documents, identifying an i'th collection of Ni>1 candidate documents from the candidate list in dependence on Posterior probability scores.

Abstract: A method for finding a solution to a problem is provided. The method includes storing candidate individuals in a candidate pool and evolving the candidate individuals by performing steps including (i) testing each of the candidate individuals to obtain test results, (ii) assigning a performance measure to the tested candidate individuals, (iii) discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and (iv) adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool.

Abstract: The technology disclosed introduces a concept of training a neural network to create an embedding space. The neural network is trained by providing a set of K+2 training documents, each training document being represented by a training vector x, the set including a target document represented by a vector xt, a favored document represented by a vector xs, and K>1 unfavored documents represented by vectors xiu, each of the vectors including input vector elements, passing the vector representing each document set through the neural network to derive an output vectors yt, ys and yiu, each output vector including output vector elements, the neural network including adjustable parameters which dictate an amount of influence imposed on each input vector element to derive each output vector element, adjusting the parameters of the neural network to reduce a loss, which is an average over all of the output vectors yiu of [D(yt,ys)?D(yt,yiu)].

Abstract: A method for identifying a desired document is provided to include calculating a Prior probability score for each document of a candidate list including a portion of documents of an embedding space, the Prior probability score indicating a preliminary probability, for each document of the candidate list, that the document is the desired document, and identifying an initial (i=0) collection of N0>1 candidate documents from the candidate list in dependence on the calculated Prior probability scores, the initial collection of candidate documents having fewer documents than the candidate list. The method further includes, for each i'th iteration in a plurality of iterations, beginning with a first iteration (i=1) and in response to user selection of an i'th selected document from the (i?1)'th collection of candidate documents, identifying an i'th collection of Ni>1 candidate documents from the candidate list in dependence on Posterior probability scores.

Abstract: Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.

Abstract: A method for user identification of a desired document is provided. The method includes receiving an identification of a prototype document, providing a database identifying a catalog of documents, identifying as candidate documents all documents within the catalog of documents which are within a threshold T1 relative to the prototype document, the threshold T1 being a member of the group consisting of (i) a distance representing dissimilarity and (ii) a score determined in dependence on a view of user preferences and dissimilarity, identifying a collection of fewer than all of the candidate documents, receiving, from the user, a selection of one or more documents from the collection identified toward the user, reducing the threshold T1 by a predetermined amount, and removing, from the candidate documents, all documents within the catalog of documents having a distance greater than the reduced threshold T1 from the selected one or more documents.

Abstract: Roughly described, a system for user identification of a desired document. A database identifies a catalog of documents in an embedding space, in which the distance between documents corresponds to a measure of their dissimilarity. The system presents an initial collection of the documents toward the user from an initial candidate space which is part of the embedding space, then in response to iterative user input, refines the candidate space and subsequent collections of documents presented toward the user. The initial collection is determined using a weighted cost-based iterative addition to the initial collection of documents from the initial candidate space, trading off between two sub-objectives of representativeness and diversity.

Abstract: Roughly described, a system and method for user identification of a desired document, in which a database is provided which identifies a collection of documents in an embedding space, the database identifying a distance between each pair of the documents in the embedding space corresponding to a predetermined measure of dissimilarity between the pair of documents. In dependence upon a user query, the system constrains the embedding space geometrically to develop a first candidate space, and identifies toward the user a first set of N1>1 candidate documents from the first candidate space, the first set of candidate documents being more discriminative than the average discriminativeness of set size N1 documents in the first candidate space. Preferably the placement of the documents as presented to the user is indicative of the placement of the documents in the embedding space, either in distance or in collinearity or both.

Abstract: Providing dynamic learning for software agents in a simulation is described. The software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to a player character. A game designer provides program code, from which compile-time steps determine a set of raw features. The code may identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, may be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.

Abstract: A system including at least specialized elements that are restricted to a particular domain of data analysis or processing and configurable data that permits the specialized elements to be tailored to a particular application. The configurable data expands applicability of the specialized elements to plural applications within the particular domain. The specialized elements can be provided by a supplier to a developer without the supplier having detailed knowledge of structures and internal operations used by the particular application. The particular application can be generated by the developer without the developer having detailed knowledge of internal operations used by the specialized elements.

Abstract: Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.

Abstract: A system including at least (1) specialized elements that are restricted to a particular domain of data analysis or processing and (2) configurable data that permits the specialized elements to be tailored to a particular application. The configurable data expands applicability of the specialized elements to plural applications within the particular domain. The specialized elements can be provided by a supplier to a developer without the supplier having detailed knowledge of structures and internal operations used by the particular application. The particular application can be generated by the developer without the developer having detailed knowledge of internal operations used by the specialized elements.

Abstract: Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.

Abstract: Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.