Cognizant Technology Solutions U.S. Corporation

Sentient Technologies (barbados) Limited

Ianywhere Solutions, Inc.

Genetic Finance (barbados) Limited

Sybase, Inc.

Toyota Infotechnology Center Co. Ltd.

Iloop Mobile, Inc.

Cognizant Technology Solutions

Babak Hodjat

Hormoz Shahrzad

Risto Miikkulainen

Nicholas K Treadgold

Daniel Edward Fink

Siamak Hodjat

Greg S Hornby

Kaivan Kamali

Adam John Cheyer

Benjamin K Reaves

Norikazu Endo

Masahiko Funaki

Peter Harrigan

Antoine Blondeau

Cristobal Baray

Makoto Kurahashi

Jason Zhi Liang

John R Brookes

Olivier Francon

Parmeet Chaddha

Gilles Stéphane Demaneuf

Christopher J Savoie

Makoto Amamiya

Daniel Charles Yuen

Aparna Joshi

Kanae Ishii

User-driven exploration functionality, referred to herein as a Scratchpad, is a post-learning extension for machine learning systems. For example, in ESP, consisting of the Predictor (a surrogate model of the domain) and Prescriptor (a solution generator model), the Scratchpad allows the user to modify the suggestions of the Prescriptor, and evaluate each such modification interactively with the Predictor. Thus, the Scratchpad makes it possible for the human expert and the AI to work together in designing better solutions. This interactive exploration also allows the user to conclude that the solutions derived in this process are the best found, making the process trustworthy and transparent to the user.

Framework for interactive exploration, evaluation, and improvement of AI-generated solutions

A surrogate-assisted evolutionary optimization method, ESP, discovers decision strategies in real-world applications. Based on historical data, a surrogate is learned and used to evaluate candidate policies with minimal exploration cost. Extended into sequential decision making, ESP is highly sample efficient, has low variance, and low regret, making the policies reliable and safe. As an unexpected result, the surrogate also regularizes decision making, making it sometimes possible to discover good policies even when direct evolution fails.

Process and system including an optimization engine with evolutionary surrogate-assisted prescriptions

An explainable surrogate-assisted evolutionary optimization method, E-ESP, discovers rule-based decision strategies for which actions to take to achieve certain outcomes when historical training data is limited or unavailable. The resulting rules are human readable and thus facilitate explainability and trustworthiness unlike the black box solutions resulting from neural network solutions.

Process and system including explainable prescriptions through surrogate-assisted evolution

Roughly described, a problem solving platform distributes the solving of the problem over a evolvable individuals, each of which also evolves its own pool of actors. The actors have the ability to contribute collaboratively to a solution at the level of the individual, instead of each actor being a candidate for the full solution. Populations evolve both at the level of the individual and at the level of actors within an individual. In an embodiment, an individual defines parameters according to which its population of actors can evolve. The individual is fixed prior to deployment to a production environment, but its actors can continue to evolve and adapt while operating in the production environment. Thus a goal of the evolutionary process at the level of individuals is to find populations of actors that can sustain themselves and survive, solving a dynamic problem for a given domain as a consequence.

Alife machine learning system and method

Roughly described, an evolutionary data mining system includes at least two processing units, each having a pool of candidate individuals in which each candidate individual has a fitness estimate and experience level. A first processing unit tests candidate individuals against training data, updates an individual's experience level, and assigns each candidate to one of multiple layers of the candidate pool based on the individual's experience level. Individuals within the same layer of the same pool compete with each other to remain candidates. The first processing unit selects a set of candidates to retain based on the relative novelty of their responses to the training data. The first processing unit reports successful individuals to the second processing unit, and receives individuals for further testing from the second processing unit. The second processing unit selects individuals to retain based on their fitness estimate.

Data mining technique with distributed novelty search

The technology disclosed proposes a novel asynchronous evaluation strategy (AES) that increases throughput of evolutionary algorithms by continuously maintaining a queue of K individuals ready to be sent to the worker nodes for evaluation and evolving the next generation once a fraction Mi of the K individuals have been evaluated by the worker nodes, where Mi&#x3c;&#x3c;K. A suitable value for Mi is determined experimentally, balancing diversity and efficiency. The technology disclosed is extended to coevolution of deep neural network supermodules and blueprints in the form of AES for cooperative evolution of deep neural networks (CoDeepNEAT-AES). Applied to image captioning domain, a threefold speedup is observed on 200 graphics processing unit (GPU) worker nodes, demonstrating that the disclosed AES and CoDeepNEAT-AES are promising techniques for evolving complex systems with long and variable evaluation times.

Asynchronous evaluation strategy for evolution of deep neural networks

The computer system and method herein uses a multi-objective driven evolutionary algorithm that is better able to find optimum solutions to a problem because it balances the use of objectives as composite functions, and relative novelty and diversity in evolutionary optimization. In particular, the system and method herein described herein presents an improved process which introduces novelty pulsation, i.e., a systematic method to alternate between novelty selection and local optimization.

Enhanced optimization with composite objectives and novelty pulsation

Roughly described, anomalous behavior of a machine-learned computer-implemented individual can be detected while operating in a production environment. A population of individuals is represented in a computer storage medium, each individual identifying actions to assert in dependence upon input data. As part of machine learning, the individuals are tested against samples of training data and the actions they assert are recorded in a behavior repository. The behavior of an individual is characterized from the observations recorded during training. In a production environment, the individuals are operated by applying production input data, and the production behavior of the individual is observed and compared to the behavior of the individual represented in the behavior repository. A determination is made from the comparison of whether the individual's production behavior during operation is anomalous.

Detecting behavioral anomaly in machine learned rule sets

Roughly described, in an evolutionary technique for finding optimal solutions to a provided problem, a computer system uses a grouping algorithm that is better able to find diverse and optimum solutions in data mining environment with multiple solution landscapes and a plurality of candidate individuals. Each candidate individual identifies with a potential solution, and is associated with a testing experience level and one or more partition tags. Each candidate individual is assigned into one of a plurality of competition groups in dependence upon the individual's testing experience level and partition tag. During competition among candidate individuals, a candidate individual can only replace another candidate individual if both the candidate individuals have a common partition tag and are in the same competition group. A candidate individual cannot replace another candidate individual if they have different partition tags or are in different competition groups.

Training and control system for evolving solutions to data-intensive problems using nested experience-layered individual pool

In many environments, rules are trained on historical data to predict an outcome likely to be associated with new data. Described is a ruleset which predicts the probability of a particular outcome. Roughly described, an individual identifies a ruleset, where each of the rules has a plurality of conditions and also indicates a rule-level probability of a predetermined classification. The conditions indicate a relationship (e.g., '&#x3c;' or '!&#x3c;') between an input feature and a corresponding value. The rules are evaluated against input data to derive a certainty for each condition, and aggregated to a rule-level certainty. The rule probabilities are combined using the rule-level certainty values to derive a probability output for the ruleset, which can be used to provide a basis for decisions. In an embodiment, the per-condition certainty values are fuzzy values aggregated by fuzzy logic. A novel genetic algorithm can be used to derive the ruleset.

Distributed rule-based probabilistic time-series classifier

Roughly described, a computer-implemented evolutionary system evolves candidate solutions to provided problems. It includes a memory storing a candidate gene database containing active and epigenetic individuals; a gene pool processor which tests only active individuals on training data and updates their fitness estimates; a competition module which selects active individuals for discarding in dependence upon both their updated fitness estimate and their testing experience level; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool. The gene database has an experience layered elitist pool, and individuals compete only with other individuals in their same layer. Certain individuals are made epigenetic in the procreation module, after which they are not subjected to testing and competition. Epigenetic individuals are retained in the candidate gene pool regardless of their fitness. An epigenetic individual can become an active individual by random chance in procreation.

Training and control system for evolving solutions to data-intensive problems using epigenetic enabled individuals

In many environments, rules are trained on historical data to predict an outcome likely to be associated with new data. Described is a ruleset which predicts the probability of a particular outcome. Roughly described, an individual identifies a ruleset, where each of the rules has a plurality of conditions and also indicates a rule-level probability of a predetermined classification. The conditions indicate a relationship (e.g. '&#x3c;' or '!&#x3c;') between an input feature and a corresponding value. The rules are evaluated against input data to derive a certainty for each condition, and aggregated to a rule-level certainty. The rule probabilities are combined using the rule-level certainty values to derive a probability output for the ruleset, which can be used to provide a basis for decisions. In an embodiment, the per-condition certainty values are fuzzy values aggregated by fuzzy logic. A novel genetic algorithm can be used to derive the ruleset.

Roughly described, a computer-implemented evolutionary data mining system implements a genetic algorithm. The Genetic algorithm includes a requirements checkpoint, which selects individuals for discarding from the pool of candidate genomes which do not meet a predetermined minimum behavioral requirement for operating in production. The requirements checkpoint enforces an absolute minimum threshold for a behavioral characteristic of the individual, and is different from a competition step in which individuals are selected for removal on the basis of comparisons with each other. A requirements checkpoint may be inserted at various points within the genetic algorithm flow or at reasonable intervals during the training cycle. If at any of these checkpoints the minimum requirement is not met, the candidate individual may be removed from the candidate pool.

Growing community of inventors

Dublin, CA, United States of America

Babak Hodjat