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 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: 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 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 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: 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: 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: Systems, methods, and products for pull data transfer in a request-response model are provided herein. One aspect provides for generating output data utilizing at least one data generation station; and communicating via the at least one data generation station output data related to at least one data request received from at least one data requesting station responsive to at least one criterion, the at least one criterion comprising one of expiration of a time period or generation of a threshold amount of output data. Other embodiments and aspects are also described herein.

Abstract: Systems, methods, and products for pull data transfer in a request-response model are provided herein. One aspect provides for generating output data utilizing at least one data generation station; and communicating via the at least one data generation station output data related to at least one data request received from at least one data requesting station responsive to at least one criterion, the at least one criterion comprising one of expiration of a time period or generation of a threshold amount of output data. Other embodiments and aspects are also described herein.

Abstract: A method for security planning with hard security constraints includes: receiving security-related requirements of a network to be developed using system inputs and processing components; and generating the network according to the security-related requirements, wherein the network satisfies hard security constraints.

Abstract: A method for security planning with soft security constraints, include: receiving security-related requirements of a workflow to be developed using system inputs and processing components; and generating at least one proposed workflow according to the security-related requirements, wherein the at least one proposed workflow satisfies soft constraints.

Abstract: A method for protecting information in a distributed stream processing system, including: assigning a principal label to a processing component; assigning a first channel label to a first communication channel that is input to the processing component; comparing the principal label to the first channel label to determine if the processing component can read data attributes of the first channel label; and reading the data attributes of the first channel label when the principal label is equal to or has precedence over the first channel label, wherein the principal label includes a read label and a write label and at least one of a selection label, an addition label or a suppression label.

Abstract: A method for protecting information in a distributed stream processing system, including: assigning a principal label to a processing component; assigning a first channel label to a first communication channel that is input to the processing component; comparing the principal label to the first channel label to determine if the processing component can read data attributes of the first channel label; and reading the data attributes of the first channel label when the principal label is equal to or has precedence over the first channel label, wherein the principal label includes a read label and a write label and at least one of a selection label, an addition label or a suppression label.

Abstract: A method for protecting information in a distributed stream processing system, including: assigning a principal label to a processing component; assigning a first channel label to a first communication channel that is input to the processing component; comparing the principal label to the first channel label to determine if the processing component can read data attributes of the first channel label; and reading the data attributes of the first channel label when the principal label is equal to or has precedence over the first channel label, wherein the principal label includes a read label and a write label and at least one of a selection label, an addition label or a suppression label.

Abstract: A method for security planning with soft security constraints, include: receiving security-related requirements of a workflow to be developed using system inputs and processing components; and generating at least one proposed workflow according to the security-related requirements, wherein the at least one proposed workflow satisfies soft constraints.

Abstract: A method and apparatus for solving a planning problem generates multiple efficient, alternate plans, one of which is selected for execution. Cost and quality metrics are used for describing and comparing the multiple plans. A planner narrows down a large number of possible solutions or plans to solve the problem based on a domain description including parameters that play a role in cost and quality computation. The method and apparatus can be used in planning stream processing workflows, threat detection and prevention, including intrusion detection, travel planning and planetary exploration.

Abstract: A method for protecting information in a distributed stream processing system, including: assigning a principal label to a processing component; assigning a first channel label to a first communication channel that is input to the processing component; comparing the principal label to the first channel label to determine if the processing component can read data attributes of the first channel label; and reading the data attributes of the first channel label when the principal label is equal to or has precedence over the first channel label, wherein the principal label includes a read label and a write label and at least one of a selection label, an addition label or a suppression label.