Selecting a plurality of individuals and AI agents to accomplish a task

The present invention provides a method, system, and computer program for selecting a plurality of workers to accomplish a task. The method includes: identifying a task from a description; extracting at least one cognitive skill from the description of the task using machine learning methods; generating a group cognitive map which includes the at least one cognitive skill; and selecting a plurality of workers to accomplish the task based on at least the group cognitive map, wherein the plurality of workers comprises at least on artificial intelligent (AI) agent.

FIELD OF THE INVENTION

The present invention is related to accomplishing a task with a plurality of workers. More particularly, the present invention is related to using artificial intelligent (AI) agents to assist in complementing the skills of a plurality of workers to accomplish a task.

BACKGROUND OF THE INVENTION

Several jobs and projects require a various number of cognitive skills to accomplish the given job and/or project. Skills of an individual are generally identified by evaluating their prior work. Cognitive skills are each individual's innate or intuitive based skills that are carded out in any task. Cognitive skills have to do with the mechanisms of how we learn, remember, problem-solve, and pay attention rather than with any actual knowledge individuals may have acquired in education or previous jobs There have been previous methods of matching a person's cognitive skill to a task by training a given individual to improve abilities. These systems only measure and analyze mental abilities, and they fall short of attempting to compliment the abilities of workers so that the task can be accomplished more efficiently. Many systems do not asses the cognitive ability of each worker in a group and recognize the need for an additional cognitive ability. Therefore, in order to complement some of the cognitive abilities of individuals, the use of artificial intelligent agents can be quicker and more cost-effective.

SUMMARY OF THE INVENTION

One aspect of the present invention provides a method for selecting a plurality of workers to accomplish a task. The method includes: identifying a task from a description; extracting at least one cognitive skill from the description of the task using machine learning methods; generating a group cognitive map which includes the at least one cognitive skill; and selecting a plurality of workers to accomplish the task based on at least the group cognitive map, wherein the plurality of workers comprises at least on artificial intelligent (AI) agent.

Another aspect of the present invention provides a system for selecting a plurality of workers to accomplish a task. The system comprising: a memory; a processor coupled to the memory; and a cognitive skill selector module coupled to the memory and the processor, wherein the cognitive skill selector module performs the steps of a method include: identifying a task from a description; extracting at least one cognitive skill from the description of the task using machine learning methods; generating a group cognitive map which includes the at least one cognitive skill; and selecting a plurality of workers to accomplish the task based on at least the group cognitive map, wherein the plurality of workers comprises at least on artificial intelligent (AI) agent.

Another aspect of the present invention provides a computer program product for selecting a plurality of workers to accomplish a task, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor performs the steps of the method described above.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention provides a method, system, and apparatus for selecting a plurality of workers to accomplish a task, in which the plurality of workers includes at least one artificial intelligent (AI) agent.

Some embodiments of the invention can be implemented in a cloud computing environment. Specifically, for example selecting the plurality of workers from a crowd of individuals that are connected via a cloud interface.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and a cognitive skill selector96. The cognitive skill selector can select the cognitive skills it needs from individuals and/or artificial agents to accomplish the task.

FIG. 4depicts a flowchart of the method of selecting a plurality of workers to accomplish a task according to the present invention. The first step401, is to identify a task from a description. The description can be presented in any form known in the relevant art. Accordingly, a description can be, but is not limited to, statement of work contract, proposal, a department's objectives, a submitted description and/or any other method of describing a task known in the relevant art. In step401, the task that is identified can be a specific short-term task or a broad long-term task. For example, a task can be filing a patent application to building a new railroad system.

Referring toFIG. 1, the next step402is to extract at least one cognitive skill from the description of the task using machine learning methods. The system uses machine learning methods, which can be, but are not limited to, latent Dirchlet allocation, natural language processing, and/or automatic question and answer processing, and/or any other machine learning method known in the relevant art.

Referring to step402, the cognitive skills that are extracted can be any skill that defines the ability of an entity or human to process thoughts and critical thinking skills. These cognitive skills include but are not limited to motor skills, complex/continuous motor skills, time required to move the stimulus, movement smoothness, complex eye hand coordination, hand-hand coordination, and/or eye-foot coordination. The system can include a database for storing such skillset where the data may relate to cognitive norms, hand-eye coordination, smooth movement, spatial orientation, route planning, mental rotation, speed/distance/time estimation, visual search, attention allocation/focusing, distractibility, split attention, scene perception, simple and choice reaction time, decision-making, risk-taking, short-term memory (STM), location memory, memory for names, information encoding strategies, retrieval from long-term memory (LTM), response to time pressure (TP), ability to inhibit planned action, problem-solving, and linguistic skills.

According to an embodiment of the present invention, natural language processing is a machine learning method used to identify the cognitive skills needed to accomplish a task. Natural language processing is the process of translating natural language input into a computer/artificial language. Accordingly, a description can be processed so that the system can understand the description and extract the given task specified in the description.

According to another embodiment of the present invention, Latent Dirchlet Allocation (LDA) is used to extract the at least one cognitive skills necessary to accomplish the test. Latent Dirchlet Allocation is the process of classifying text content to an associated category. This can be, but is not limited to, text classification based on statistical method and text classification method based on Bayesian text classification.

Accordingly, a description can be classified according to previous tasks that have been classified and associated with specific category of tasks. For example, writing an original patent application verses writing an office action. More specifically, LDA can preclude the use of a bag of words approach, each text as a word frequency-to-model digital information. Each article text represents a probability distribution of some of the topics posed, and each topic, they also represent a lot of words posed probability distribution.

Another embodiment of the present invention can use question and answering (Q&A) system to extract at least one cognitive skill from the description. Question and answering system outputs an answer itself if a question sentence expression in a natural language is inputted.

Referring toFIG. 4, the next step403is to generate a group cognitive map that includes the at least one cognitive skill. The group cognitive map involves mapping from a set of cognitive skills that have been extracted from the description.

Referring toFIG. 4, the next step404is to select a plurality of workers to accomplish the task based on at least the group cognitive map, wherein the plurality of workers includes at least one artificial intelligent (AI) agent. The given set of workers can range from a small finite group to a large indefinite number. The workers can range from being inputted into a database or can be selected from a varying social networking website. For example, it can be the employees associated to a specific department, the graduating class from a certain school, and/or it can be all of the members who are participants to a social networking website (i.e. LinkedIn).

According to the present invention, the cognitive skills of individuals are determined using methods known in the relevant art to determine the at least one cognitive skill of each worker. Methods of determining the cognitive skills of each worker can be, but are not limited to, testing the preliminary cognitive level of a worker, analyzing their profiles on various social media websites, and/or crowdsourcing methods known in the relevant area. Furthermore, cognitive skills can be extracted from the already completed tasks/crowdsourcing repositories and/or by active monitoring of a user, during their interaction with systems(s)/agent(s) and other humans. Furthermore, cognitive skills can be determined by mining a log of user activities and correlating it to the taxonomy that brings together skills and tasks.

According to another embodiment of the present invention, the system can supply “tags” that specify one or more cognitive skills that are likely to be useful and/or necessary to accomplish the task. These tags can be generated automatically according to the description of the task or a user can input such tags. Furthermore, the tags associated with the cognitive skills can change over time if the cognitive skills required to accomplish the task change over time

For example, the task can be to develop a gaming program for a specific game environment. For this specific task 3D spatial skills are a necessary skill to accomplish the task. As a result, a tag for “3D spatial skills” can be supplied by the system to assist in selecting the workers to accomplish the task.

According to the present invention, the plurality of workers includes at least one artificial intelligent (AI) agent. Typically the at least one AI agent compliments the cognitive skill of the human workers so that the task can be completed efficiently. Complimentary AI agents can be generated from a template, wherein certain knowledge bases and capabilities may be removed in order to not overlap with existing capabilities of humans in the subset.

According to the present invention, several tasks rely on the skills of more than one AI/human agent. Identifying at least one “complimentary AI” agent to include in the subset is useful for tasks that call for collaborative approach and bring together digital and human intelligence. For example, if a team of workers is deficient in language processing in a particular language such as French, an AI agent can be enlisted to complement the team's skills.

AI planning technology can be applied to create agents/agent capabilities. Based on the user context and goal, AI planning can assemble a sequence of actions that agent can execute to achieve a certain goal.

Referring toFIG. 4, the next step405according to an embodiment of the present invention is evaluating the gain of task quality according to known methods in the relevant art. The task quality is the overall evaluation of the probability of the task being accomplished in the given time and meeting the given parameters. This can be based on but not limited to the team configuration, given skillset, and risk of each member, and/or marketplace conditions.

According to an embodiment of the present invention, decision theory can be used to evaluate the gain of the task quality. Decision theory requires systems makes a decision about whether to engage a certain team member or not. The success is determined by the realized percentage of agents/team members who perform the tasks exceeding a threshold.

According to an embodiment of the present invention, after evaluating the gain of task quality the present invention provides a confidence value (C) for the task. The confidence value represents the likelihood that the task will be completed on time within the given parameters. Such parameters include but are not limited to budget; deadline; location; weather and etc.

According to another embodiment of the present invention, there is a continuous evaluation of the gain of task quality. According to the present invention, the system can recommend changing the plurality of workers based on the gain of the task quality. This can be performed via methods known in the relevant art.

The continuous evaluation can also change the confidence value and alert the supervisor(s) of the task to make specific changes to the task so that the task is accomplished according to the given requirements. This alert can be triggered if the confidence level drops below a certain threshold. Accordingly, the present invention can also provide recommendations of changes to be made to the plurality of workers so that the confidence level stays above a given threshold.

In another embodiment of the present invention, the confidence value can recommend adding an AI agent to assist in team building. More specifically, if C is deficient in compatibility, meeting productivity, and/or group dynamics, the AI agent can assist in determining proactive activities to assist in team building. For example, the AI agent could be added to assist by offering suggestions for forming, storming, norming, and performing based on its intelligent knowledge of psychology, team building, and group dynamics.

FIG. 5depicts a group cognitive map according to an embodiment of the present invention. For example, according toFIG. 5the task is identified as counting the number of infected blood cells on an image. The skills necessary can be counting, visually recognizing a square/border, visually revognixzng cells, pattern recognition, and/or relationship recognition.FIG. 5ashows a cognitive map at the beginning of the task.FIG. 5bshows the cognitive map after a period of time.

The group cognitive map is represented in an abstract continuous metric space, such that semantic relations among representations are reflected in geometric relations in the indexing space. For example, in a spatial cognitive map, the metrics may be proportional to the perceived distances between associated landmarks in the physical world. In this case, the cognitive map is essentially a model of perceived space.

According the present invention, as the time of the task proceeds the cognitive map can change according to the selected plurality of workers' skills. Cognitive map is continuously updated based on the feedback/skills observed (their level of competence) and tasks completed as shown inFIG. 5. This will allow the supervision of the task to determine whether a change needs to be made to the selected plurality of workers.

According to the present invention, the description can include but is not limited to the location, size, capacity, budget, specific requirements, design limitations/preferences, licensing limitations, legal limitations, various technical/skills that are used to accomplish, timeline and/or other areas that affect the task. Determining the description of the task can be submitted to the system and/or be automatically evaluated a task. To be submitted a user can merely submit a specification to the system. The task can also be generated by the system. For example, if there is an ongoing task, and then the system realizes there is a second task to be accomplished as well, the system can automatically generate a task.

According the present invention, there are several different parameters that can be used to select the plurality of workers. This includes, budget, timeline, location, weather.

In another embodiment the system selects the optimal workers from a larger set of workers to accomplish a task. Accordingly, the present invention can generate an iterative loop over a network to incorporate specific parameters of the task to determine of the optimal worker for a task. The network may parameterize worker links (edges) with compatibility scores between the various workers (nodes), and can be inserted into a data space of tasks for an order embedding algorithm for nonlinear dimensionality reduction. Examples of an order embedding algorithm include an elastic map, a Sammon's map, a Kohonen map, or a combination thereof. Next, association linkages are associated between a worker and at least one pair of data points in the data space, each association link representing a different cognitive skill requirement's likeness to the cognitive skill of a worker. A total energy of the order embedding algorithm is computed.

For example, the total energy can be computed as a sum of an approximation energy related to the association links and distortion energy of the network. Based on the total energy being above a settable threshold and the system recommends a change in the plurality of workers or change in dynamic the system can perform one or more of the following: i) one or more of the worker links are re-associated between a previously unassociated pair of workers; ii) one or more of the association links to a previously associated pair of tasks in the data space are re-associated between a previously unassociated pair of tasks. The iterative loop as outlined above in the previous paragraph is repeated. In one example, the re-associating automatically re-associates the association link between a previously unassociated pair of the data points in the data space and a worker in response to the re-association resulting in lower total energy.

In another embodiment of the present invention the system takes into account compatibility of the workers using known methods in the related art.

In another embodiment for the present invention the system can determine the training needed to exemplify the cognitive skills needed to accomplish the task.

In another embodiment of the present invention the system calculates the risk associated with the selected workers accomplishing the task according to known methods in the relevant art.