Abstract: An example operation may include one or more of identifying an external system that passes an input attribute to a process based on a workflow representation of the process, building a simulator of the external system based on attributes of the external system identified from the workflow representation, simulating future values of the input attribute to be passed to the process by the external system based on the simulator of the external system and a previous simulation run of the process performed via a workflow software application, and executing a new simulation of the process via the workflow software application based on the simulated future values of the input attribute.

Abstract: One embodiment provides a method, including: receiving a ranked listing of a plurality of candidates; within a first one of a plurality of collaboration rounds: (i) aggregating the ranked listings into an overall listing, (ii) presenting the overall listing to a first one of the job candidate matching agents, and (iii) receiving at least one modification to the overall listing from the agents; (iv) presenting a version of the overall listing to each of the remaining agents; and (v) receiving any modifications from the agents until all of the agents have had a turn; conducting others of the plurality of collaboration rounds: (i) presenting a version of the overall listing and (ii) receiving any modifications from the agents; (iii) presenting a version of the overall listing to each of the remaining agents, and (iv) receiving any modifications from the agents; and providing the final version to a user.

Abstract: One embodiment provides a computer implemented method, including: receiving information corresponding to a customer of a seller, wherein the information is related to credit information of the customer; generating a credit attribute for the customer with respect to the seller, wherein the generating includes utilizing a plurality of artificial intelligence agents that each analyze at least a subset of the information to each generate an agent version of the credit attribute; and recommending a deferral of at least a portion of a pending invoice of the seller for the customer, wherein a value of the deferral is based upon the credit attribute.

Abstract: A system, method, and computer program product for implementing automated digital agent communication and control is provided. The method includes retrieving from a digital agent, a query associated with knowledge based control process. Digital knowledge elements, associated digital skills, and a sequence of control operations are received to obtain a response to the query. A first possible set of knowledge of a set of digital knowledge elements, skills, and an associated sequence of operation are selected and the first possible set of knowledge, skills, and associated sequence of operation are transmitted to the digital agent. A sequence of skills are executed with respect to digital knowledge elements and components and a hardware interface device is enabled to interact with and control various devices for enabling operational functionality associated with devices. Knowledge based fabric code associated with future instances of enabling the hardware interface device is updated.

Abstract: Methods, systems, and computer program products for a collaborative cognition platform for creating and hosting social machines are provided herein. A computer-implemented method includes creating a social machine for collaborative tasks, wherein the social machine comprises (i) one or more human agents, (ii) one or more machine-based agents, (iii) an algorithm, and (iv) a set of rules prescribed for executing the collaborative tasks. The method also includes generating one or more collaborative resolutions for the collaborative tasks by executing, in an automated fashion, the collaborative tasks via implementation of the algorithm, wherein the algorithm facilitates, in accordance with the set of rules, systematic iterations of collaboration among (i) the one or more human agents and (ii) the one or more machine-based agents. Further, the method includes outputting the one or more collaborative resolutions to at least one user.

Abstract: One embodiment provides a computer implemented method, including: receiving information corresponding to a customer of a seller, wherein the information is related to credit information of the customer; generating a credit attribute for the customer with respect to the seller, wherein the generating includes utilizing a plurality of artificial intelligence agents that each analyze at least a subset of the information to each generate an agent version of the credit attribute; and recommending a deferral of at least a portion of a pending invoice of the seller for the customer, wherein a value of the deferral is based upon the credit attribute.

Abstract: A method and associated systems provide real-time response to a request received from a real-time system like a self-driving vehicle or a device that communicates interactively with its environment. The response is selected from a set of candidate feasible responses by a group of computerized agents that each sort the feasible responses in order of that agent's specific preferences, based on that agent's particular priorities or expertise. The agents then reconcile their differences through an iterative procedure. During each iteration, each agent decides whether to retain its current preferences or to adopt the preferences of another agent. This decision is made by determining which preferences are most similar to that agent's own initial preferences, and by which preferences would be most useful in helping to achieve that agent's particular goals. When the agents reach consensus, the group's most-preferred response is returned quickly enough to provide real-time, interactive response.

Abstract: One embodiment provides a computer implemented method, including: receiving billing information related to a billing contract of a customer of a seller, wherein the billing contract identifies amounts of invoices and an invoice frequency; identifying, utilizing one or more artificial intelligence agents, one or more risk factors associated with generation of a pending invoice based upon the billing information; and recommending, utilizing the one or more artificial intelligence agents, a generation date for the pending invoice based upon the one or more risk factors, wherein the recommending includes selecting a generation date to facilitate timely payment of the pending invoice by the customer.

Abstract: A computer-implemented method is disclosed which includes receiving an initial decision from each of a plurality of agents based on the same criteria, wherein at least a subset of the agents are computing nodes, and wherein the criteria pertain to a resource allocation request; assigning an equal weight to resources specific to each of the agents; generating, for each of the initial decisions, a revised decision based on (i) comparing the initial decisions to each other, (ii) modeling aggregate group behavior represented by the initial decisions of the plurality of agents, and (iii) modeling a behavior of each of the agents in view of the initial decisions; updating the weights assigned to the resources specific to each of the agents based at least in part on the revised decisions; and dynamically allocating resources in response to the resource allocation request based at least in part on the updated weights.

Abstract: Methods, systems, and computer program products for designing conflict minimizing outreach strategies are provided herein. A computer-implemented method includes obtaining data from a job matching service corresponding to jobs listed on the job matching service and candidates; generating job clusters for the jobs based on clustering criteria; identifying, for each respective one of the job clusters, a set of candidates from among the candidates to reduce inter-cluster conflict; selecting, for each of the jobs in a given one of the job clusters, individual candidates from among the set of candidates corresponding to the given job cluster to reduce intra-cluster conflict with respect to the plurality of candidates; and outputting one or more optimized lists of the candidates for the jobs based on the identifying and the selecting.

Abstract: Methods, systems, and computer program products for generating collaborative orderings of information pertaining to products to present to target users are provided herein.

Abstract: One embodiment provides a method, including: receiving a ranked listing of a plurality of candidates; within a first one of a plurality of collaboration rounds: (i) aggregating the ranked listings into an overall listing, (ii) presenting the overall listing to a first one of the job candidate matching agents, and (iii) receiving at least one modification to the overall listing from the agents; (iv) presenting a version of the overall listing to each of the remaining agents; and (v) receiving any modifications from the agents until all of the agents have had a turn; conducting others of the plurality of collaboration rounds: (i) presenting a version of the overall listing and (ii) receiving any modifications from the agents; (iii) presenting a version of the overall listing to each of the remaining agents, and (iv) receiving any modifications from the agents; and providing the final version to a user.

Abstract: Methods, systems, and computer program products for generating capacity planning schedules while protecting the privacy of stakeholder preferences of a set of metrics are provided herein. A computer-implemented method includes identifying stakeholders associated with capacity planning for a project; determining metrics to be used in the capacity planning; obtaining, from each of the stakeholders, an initial preferred order of emphasis of the metrics; calculating similarity scores between the initial preferred orders of emphasis; outputting, to each of the stakeholders, the similarity scores, wherein the identity of the stakeholders has been masked; obtaining, from each of the stakeholders, at least a second iteration of a preferred order of emphasis of the metrics; generating a final order of emphasis of the multiple metrics upon a determination that the stakeholders provided at least a predetermined number of identical preferred orders of emphasis; and outputting the final order of emphasis of the metrics.

Abstract: Methods, systems, and computer program products for generating collaborative orderings of information pertaining to products to present to target users are provided herein.

Abstract: Methods, systems, and computer program products for generating capacity planning schedules while protecting the privacy of stakeholder preferences of a set of metrics are provided herein. A computer-implemented method includes identifying stakeholders associated with capacity planning for a project; determining metrics to be used in the capacity planning; obtaining, from each of the stakeholders, an initial preferred order of emphasis of the metrics; calculating similarity scores between the initial preferred orders of emphasis; outputting, to each of the stakeholders, the similarity scores, wherein the identity of the stakeholders has been masked; obtaining, from each of the stakeholders, at least a second iteration of a preferred order of emphasis of the metrics; generating a final order of emphasis of the multiple metrics upon a determination that the stakeholders provided at least a predetermined number of identical preferred orders of emphasis; and outputting the final order of emphasis of the metrics.

Abstract: A computer-implemented method is disclosed which includes receiving an initial decision from each of a plurality of agents based on the same criteria, wherein at least a subset of the agents are computing nodes, and wherein the criteria pertain to a resource allocation request; assigning an equal weight to resources specific to each of the agents; generating, for each of the initial decisions, a revised decision based on (i) comparing the initial decisions to each other, (ii) modeling aggregate group behavior represented by the initial decisions of the plurality of agents, and (iii) modeling a behavior of each of the agents in view of the initial decisions; updating the weights assigned to the resources specific to each of the agents based at least in part on the revised decisions; and dynamically allocating resources in response to the resource allocation request based at least in part on the updated weights.

Abstract: Methods, systems, and computer program products for a collaborative cognition platform for creating and hosting social machines are provided herein. A computer-implemented method includes creating a social machine for collaborative tasks, wherein the social machine comprises (i) one or more human agents, (ii) one or more machine-based agents, (iii) an algorithm, and (iv) a set of rules prescribed for executing the collaborative tasks. The method also includes generating one or more collaborative resolutions for the collaborative tasks by executing, in an automated fashion, the collaborative tasks via implementation of the algorithm, wherein the algorithm facilitates, in accordance with the set of rules, systematic iterations of collaboration among (i) the one or more human agents and (ii) the one or more machine-based agents. Further, the method includes outputting the one or more collaborative resolutions to at least one user.

Abstract: One embodiment provides a method, including: receiving a requisition for a job position, the requisition having a plurality of recruiters, each having influence in selecting a candidate; generating a profile for an ideal candidate comprising (i) a plurality of attributes and (ii) weights corresponding to each of the attributes; receiving, for a plurality of candidates, profiles for each the candidates; comparing the profile of each of the plurality of candidates against the ideal candidate, using a distance method computation to determine the distance between the plurality of candidates and the ideal candidate based upon the weights; ranking the plurality of candidates and providing the ranking to each of the plurality of recruiters; receiving input from each of the plurality of recruiters that modifies the ranking, recalculating the weights of the attributes based upon the modified ranking, and modifying the ranking; and providing a final ranking of the plurality of candidates.

Abstract: One embodiment provides a method, including: accessing, for an identified individual, a sentiment profile comprising at least one average sentiment score, wherein the sentiment profile is generated by analyzing interactions of the identified individual with other individuals and wherein the sentiment profile reflects the identified individual's personality in interactions with other individuals; receiving input corresponding to a current interaction between the identified individual and at least one other individual; generating a current sentiment score for the identified individual corresponding to the current interaction using a sentiment analysis tool; determining a sentiment intent of the identified individual corresponding to the current interaction, wherein the determining a sentiment intent comprises comparing the current sentiment score to the at least one average sentiment score from the sentiment profile; and providing to a user, feedback of the determined sentiment intent of the identified individu

Abstract: A method and associated systems provide real-time response to a request received from a real-time system like a self-driving vehicle or a device that communicates interactively with its environment. The response is selected from a set of candidate feasible responses by a group of computerized agents that each sort the feasible responses in order of that agent's specific preferences, based on that agent's particular priorities or expertise. The agents then reconcile their differences through an iterative procedure. During each iteration, each agent decides whether to retain its current preferences or to adopt the preferences of another agent. This decision is made by determining which preferences are most similar to that agent's own initial preferences, and by which preferences would be most useful in helping to achieve that agent's particular goals. When the agents reach consensus, the group's most-preferred response is returned quickly enough to provide real-time, interactive response.