Abstract: Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Abstract: In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.

Abstract: Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Abstract: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.

Abstract: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.

Abstract: A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.

Abstract: Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Abstract: A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.

Abstract: In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.

Abstract: A system for orchestrating an operation is disclosed. The system includes an case orchestration engine to identify a discrepancy in the operation, and to generate a plurality of hypotheses for resolving the discrepancy. The case orchestration engine further collects evidence pertaining to the discrepancy in the operation, evaluates each of the plurality of hypotheses based on a dialogue-driven feedback received from a user, and selects one of the plurality of hypotheses for resolving the discrepancy based on the evidence and an expected outcome of the operation. The case orchestration engine provides reasons for the discrepancy along with remedial measures for resolving the discrepancy based on the selected hypothesis, and then generates a plan for performing the operation to achieve the expected outcome based on the remedial measures.

Abstract: Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Abstract: The current application is directed to methods, and to systems employing the methods, that allow built-in indexing methods for facts, to additionally be applied to rules within a logic-programming system. The methods and systems to which the current application is directed convert an original set of rules into an equivalent set of fact/rule pairs. In addition, a new set of rules may be directly encoded as a new set of fact/rule pairs by these methods and systems. The equivalent set of fact/rule pairs implement the original set of rules, preserving the meaning of the original rules, but the equivalent set of fact/rule pairs are indexed by built-in indexing methods for facts. The new fact/rule pairs are also indexed by built-in indexing methods for facts.

Abstract: The current application is directed to methods, and to systems employing the methods, that allow built-in indexing methods for facts, to additionally be applied to rules within a logic-programming system. The methods and systems to which the current application is directed convert an original set of rules into an equivalent set of fact/rule pairs. In addition, a new set of rules may be directly encoded as a new set of fact/rule pairs by these methods and systems. The equivalent set of fact/rule pairs implement the original set of rules, preserving the meaning of the original rules, but the equivalent set of fact/rule pairs are indexed by built-in indexing methods for facts. The new fact/rule pairs are also indexed by built-in indexing methods for facts.

Abstract: The method of the invention enables the situating of reasoning engines which have the capability to store premises as knowledge representation expressions, as well as to conclude knowledge representation expressions and/or to evaluate (or test), knowledge representation expressions and/or to respond to delivered directive events that request the engine to perform reasoning. The result of the method is a situated reasoning system which extends the given reasoning engine's functionality by augmenting it with procedural attachments, using zero or more provided attached procedures. This extension of functionality permits especially the capabilities for sensing and/or effecting and/or responding to delivered directive events. The method permits attachment of the attached procedures to be dynamic or static.