Abstract: Techniques for autonomously generating a domain model and/or an action model based on unstructured data are provided. In one example, a computer implemented method can comprise extracting, by a system operatively coupled to a processor, a plurality of actions from a non-numerical language. The plurality of actions can achieve a goal. The computer-implemented method can also comprise generating, by the system, a domain model based on the plurality of actions. Further, the computer-implemented method can comprise generating, by the system, an action model based on the domain model. In various embodiments, the action model can comprise an action transition for accomplishing the goal.

Abstract: Techniques for translating graphical representations of domain knowledge are provided. In one example, a computer-implemented method comprises receiving, by a device operatively coupled to a processor, a graphical representation of domain knowledge. The graphical representation comprises information indicative of a central concept and at least one chain of events associated with the central concept. The computer-implemented method further comprises translating, by the device, the graphical representation into an artificial intelligence planning problem. The artificial intelligence planning problem is expressed in an artificial intelligence description language. The translating comprises parsing the graphical representation into groupings of terms. A first grouping of terms of the grouping of terms comprises an event from the at least one chain of events and a second grouping of terms of the grouping of terms comprises the information indicative of the central concept.

Abstract: Techniques for translating graphical representations of domain knowledge are provided. In one example, a computer-implemented method comprises receiving, by a device operatively coupled to a processor, a graphical representation of domain knowledge. The graphical representation comprises information indicative of a central concept and at least one chain of events associated with the central concept. The computer-implemented method further comprises translating, by the device, the graphical representation into an artificial intelligence planning problem. The artificial intelligence planning problem is expressed in an artificial intelligence description language. The translating comprises parsing the graphical representation into groupings of terms. A first grouping of terms of the grouping of terms comprises an event from the at least one chain of events and a second grouping of terms of the grouping of terms comprises the information indicative of the central concept.

Abstract: Techniques for scenario planning are provided. In one example, a computer-implemented method can comprise analyzing, by a device operatively coupled to a processor, content using a topic model. The content can be associated with a defined source and is related to one or more current events. The computer-implemented method can also comprise determining, by the device, one or more portions of the analyzed content that are relevant to one or more key risk drivers using a risk driver model. The computer-implemented method can also comprise aggregating, by the device, the determined one or more portions into one or more emerging storylines based on values of one or more attributes of the topic model.

Abstract: Techniques are provided for recognizing goals using an artificial intelligence planner, a model of a domain, a set of observations associated with the domain, and a set of possible goals. In one example, a computer-implemented method comprises, in response to receiving a set of possible goals of an agent, a model of a domain, and a set of observations associated with the domain, transforming, by a system operatively coupled to a processor, a goal recognition problem into an artificial intelligence planning problem; determining, by the system, a set of plans using an artificial intelligence planner on the artificial intelligence planning problem; and determining, by the system, a probability distribution over the set of possible goals based on the set of plans.

Abstract: Techniques for autonomously generating a domain model and/or an action model based on unstructured data are provided. In one example, a computer implemented method can comprise extracting, by a system operatively coupled to a processor, a plurality of actions from a non-numerical language. The plurality of actions can achieve a goal. The computer-implemented method can also comprise generating, by the system, a domain model based on the plurality of actions. Further, the computer-implemented method can comprise generating, by the system, an action model based on the domain model. In various embodiments, the action model can comprise an action transition for accomplishing the goal.

Abstract: In at least one embodiment, a method and a system include receiving a trace into a hypotheses generator from a source a trace, translating the trace and a state transition model into a planning problem using the hypotheses generator, producing a set of plans for the trace using at least one planner, translating each plan into hypothesis using the hypotheses generator and/or the planner, and returning the hypotheses from the hypotheses generator. In a further embodiment, the trace includes at least one of a future observation and a past observation. In at least one embodiment, the system includes at least one planner that develops a set of plans, a hypothesis generator, a database, at least one analytic, and at least one sensor where the hypotheses generator and/or the at least one planner converts each plan into a respective hypothesis.

Abstract: A mechanism is provided for identifying a set of top-in clusters from a set of top-k plans. A planning problem and an integer value k indicating a number of top plans to be identified are received. A set of top-k plans are generated with at most size k, where the set of top-k plans is with respect to a given measure of plan quality. Each plan in the set of top-k plans is clustered based on a similarity between plans such that each cluster contains similar plans and each plan is grouped only into one cluster thereby forming the set of top-m clusters. A representative plan from each top-m cluster is presented to the user.

Abstract: A mechanism is provided for identifying a set of top-m clusters from a set of top-k plans. A planning problem and an integer value k indicating a number of top plans to be identified are received. A set of top-k plans are generated with at most size k, where the set of top-k plans is with respect to a given measure of plan quality. Each plan in the set of top-k plans is clustered based on a similarity between plans such that each cluster contains similar plans and each plan is grouped only into one cluster thereby forming the set of top-m clusters. A representative plan from each top-m cluster is presented to the user.

Abstract: In at least one embodiment, a method and a system for determining a set of plans that best match a set of preferences. The method may include receiving into a goal specification interface at least one goal to be accomplished by the set of plans; receiving into a preference engine a pattern that includes preferences; generating a planning problem by using the preference engine; generating a set of plans by at least one planner; and providing the set of plans for selection of one plan to deploy. In a further embodiment, the preferences may be an occurrence or non-occurrence of at least one component, an occurrence of one component over another component, an ordering between at least two components, an existence or non-existence of at least one tag in a final stream, an existence of one tag over another tag in the final stream.

Abstract: In at least one embodiment, a method and a system for determining a set of plans that best match a set of preferences. The method may include receiving into a goal specification interface at least one goal to be accomplished by the set of plans; receiving into a preference engine a pattern that includes preferences; generating a planning problem by using the preference engine; generating a set of plans by at least one planner; and providing the set of plans for selection of one plan to deploy. In a further embodiment, the preferences may be an occurrence or non-occurrence of at least one component, an occurrence of one component over another component, an ordering between at least two components, an existence or non-existence of at least one tag in a final stream, an existence of one tag over another tag in the final stream.

Abstract: A mechanism is provided for computing a solution to a plan recognition problem. The plan recognition problem includes the model and a partially ordered sequence of observations or traces. The plan recognition is transformed into an AI planning problem such that a planner can be used to compute a solution to it. The approach is general. It addresses unreliable observations: missing observations, noisy observations (or observations that need to be discarded), and ambiguous observations). The approach does not require plan libraries or a possible set of goals. A planner can find either one solution to the resulting planning problem or multiple ranked solutions, which maps to the most plausible solution to the original problem.

Abstract: A mechanism is provided for identifying a set of top-m clusters from a set of top-k plans. A planning problem and an integer value k indicating a number of top plans to be identified are received. A set of top-k plans are generated with at most size k, where the set of top-k plans is with respect to a given measure of plan quality. Each plan in the set of top-k plans is clustered based on a similarity between plans such that each cluster contains similar plans and each plan is grouped only into one cluster thereby forming the set of top-m clusters. A representative plan from each top-m cluster is presented to the user.

Abstract: A mechanism is provided for identifying a set of top-in clusters from a set of top-k plans. A planning problem and an integer value k indicating a number of top plans to be identified are received. A set of top-k plans are generated with at most size k, where the set of top-k plans is with respect to a given measure of plan quality. Each plan in the set of top-k plans is clustered based on a similarity between plans such that each cluster contains similar plans and each plan is grouped only into one cluster thereby forming the set of top-m clusters. A representative plan from each top-m cluster is presented to the user.

Abstract: A method for automated composition of an application including: receiving a customizable template for application composition and a composition goal, wherein the goal comprises a plurality of tags and the goal is incomplete such that more than one possible composition matches the goal; refining the goal by automatically adding refinement tags to the goal; and generating an application flow that matches the customizable template and the refined goal, wherein the application flow comprises data sources, data processing operators, and outputs of the application flow.

Abstract: In at least one embodiment, a method and a system for determining a set of plans that best match a set of preferences. The method may include receiving into a goal specification interface at least one goal to be accomplished by the set of plans; receiving into a preference engine a pattern that includes preferences; generating a planning problem by using the preference engine; generating a set of plans by at least one planner; and providing the set of plans for selection of one plan to deploy. In a further embodiment, the preferences may be an occurrence or non-occurrence of at least one component, an occurrence of one component over another component, an ordering between at least two components, an existence or non-existence of at least one tag in a final stream, an existence of one tag over another tag in the final stream.

Abstract: In at least one embodiment, a method and a system include receiving a trace into a hypotheses generator from a source a trace, translating the trace and a state transition model into a planning problem using the hypotheses generator, producing a set of plans for the trace using at least one planner, translating each plan into hypothesis using the hypotheses generator and/or the planner, and returning the hypotheses from the hypotheses generator. In a further embodiment, the trace includes at least one of a future observation and a past observation. In at least one embodiment, the system includes at least one planner that develops a set of plans, a hypothesis generator, a database, at least one analytic, and at least one sensor where the hypotheses generator and/or the at least one planner converts each plan into a respective hypothesis.