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: 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 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: Performance of a computer running a plan recognition application is improved by obtaining, with a user interface implemented on the computer, a specification of a plan recognition problem, including a plurality of candidate observations; formulating at least one planning problem, with the computer, based on the specification; solving the at least one planning problem, with the computer, to determine at least one plan. The at least one plan is post-processed, with the computer, to determine at least one of the candidate observations which should be selected to solve the plan recognition problem; and the plan recognition problem is solved, with the computer, using the at least one of the candidate observations which should be selected to solve the plan recognition problem. Less CPU time is typically required for the solution as compared to techniques without guidance for selecting the observations.

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 regarding autonomous generation of one or more sets of diverse plans are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a planner component, operably coupled to the processor, that can generate a first plan based on a planning task. The computer executable components can also comprise a modification component, operably coupled to the processor, that can generate a modification to the planning task to facilitate generation of a second plan by the planner component. The second plan can be a variant of the first plan.

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: 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: 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: 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: A method for improving performance of at least one hardware processor solving a top-k planning problem includes obtaining, in a memory coupled to the at least one processor, a specification of the planning problem in a planning language; obtaining, in a first iteration carried out by the at least one processor, at least one solution to the planning problem; and modifying the planning problem, in the first iteration carried out by the at least one processor, to forbid the at least one solution. The method further includes repeating, by the at least one processor, the obtaining of the at least one solution and the modifying to forbid the at least one solution, for a plurality of additional iterations, after the first iteration, until a desired number, k, of solutions to the planning problem are found or until no further solutions exist, whichever comes first.

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 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: Embodiments for learning personalized actionable domain models by a processor. A domain model may be generated according to a plurality of actions, extracted from one or more online data sources, of a plurality of cluster representatives. The plurality of actions achieve a goal. A hierarchical action model may be generated based on probabilities of the domain model and the plurality of actions. The hierarchical action model comprises a sequence of actions of the plurality of actions for achieving the goal. The hierarchical action model may be personalized by filtering to a selected set of actions according to weighted actions of the plurality of actions.

Abstract: Techniques for solving a multi-agent plan recognition problem are provided. In one example, a computer-implemented method comprises transforming, by a device operatively coupled to a processor, a problem model and an at least partially ordered sequence of observations into an artificial intelligence planning problem through a transform algorithm. The problem model can comprises a domain description from a plurality of agents and a durative action. Furthermore, at least one of the observations of the at least partially ordered sequence of observations can be a condition that changes over time. The computer-implemented method further comprises determining, by the device, plan information using an artificial intelligence planner on the artificial intelligence planning problem. The computer-implemented method further comprises translating, by the device, the plan information into information indicative of a solution to the artificial intelligence planning problem.

Abstract: Techniques for solving a multi-agent plan recognition problem are provided. In one example, a computer-implemented method comprises transforming, by a device operatively coupled to a processor, a problem model and an at least partially ordered sequence of observations into an artificial intelligence planning problem through a transform algorithm. The problem model can comprises a domain description from a plurality of agents and a durative action. Furthermore, at least one of the observations of the at least partially ordered sequence of observations can be a condition that changes over time. The computer-implemented method further comprises determining, by the device, plan information using an artificial intelligence planner on the artificial intelligence planning problem. The computer-implemented method further comprises translating, by the device, the plan information into information indicative of a solution to the artificial intelligence planning problem.

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.