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Knowledge base comprising executable storiesRelated Patent Categories: Data Processing: Artificial Intelligence, Knowledge Processing System, Knowledge Representation And Reasoning TechniqueKnowledge base comprising executable stories description/claimsThe Patent Description & Claims data below is from USPTO Patent Application 20060112045, Knowledge base comprising executable stories. Brief Patent Description - Full Patent Description - Patent Application Claims
TECHNICAL FIELD [0001] The present invention relates to artificial intelligence and, more particularly, to systems and methods for decision making utilizing a knowledge base comprising executable stories. BACKGROUND OF THE INVENTION [0002] There are different approaches for organizing information content extracted from text, such as decision graphs or data tables, namely, a knowledge-based approach and a data-based approach. Using the knowledge-based approach, a person (known as a knowledge engineer) interviews an expert in a given field to obtain knowledge about the given field. The knowledge engineer and expert first determine the distinctions of the subject matter that are important for decision making in the field of the expert. These distinctions correspond to questions about the variables in the domain of interest, referred to as the hypotheses. For example, if a decision graph is to be used to predict the age of a customer based on the products that customer bought in a store, there would be a variable for "age" and a variable for all relevant products. The knowledge engineer and the expert next determine the structure of the decision graph and the corresponding parameter values that quantify the associated uncertainty. [0003] In the database approach, the knowledge engineer and the expert first determine the variables of the domain. Next, data is accumulated for those variables, and an algorithm is applied that creates one or more decision graphs from this data. The accumulated data comes from real world instances of the domain or hypothesis. That is, real world instances of decision making in a given field. For some decision-making applications, however, it can be difficult in practice to find sufficient applicable data to construct a viable decision network. [0004] Decision makers often find it difficult to mentally combine evidence since the human tendency is to postpone risky decisions when data is incomplete, jump to conclusions, or refuse to consider conflicting data. Those versed in classical (frequentist) statistics realize that in situations where evidence is sparse, the use of a decision network is desirable. Unfortunately, traditional means of generating a decision network are labor intensive, requiring many hours of labor from an analyst knowledgeable about the desired application. SUMMARY OF THE INVENTION [0005] In accordance with one aspect of the present invention, an assisted decision-making system is provided. A knowledge base comprises a plurality of executable stories. A given story comprises a belief network containing at least one associated hypothesis and evidence associated with the at least one hypothesis. Each hypothesis can have labeled characteristics and values of characteristics. The system further comprises at least one decision algorithm having respective associated input formats. A translation interface is operative to convert a given story from the knowledge base into a decision network having a format associated with the at least one decision algorithm. [0006] In accordance with another aspect of the present invention, a method for assisting decision making is provided. A plurality of executable stories are stored in a knowledge base. A story from the plurality of executable stories is converted into a decision network in a format associated with a decision algorithm associated with the knowledge base. The decision network is processed at the decision algorithm to produce a revised decision network. The revised decision network is converted back into an executable story. BRIEF DESCRIPTION OF THE DRAWINGS [0007] FIG. 1 illustrates an assisted decision making system in accordance with an aspect of the present invention. [0008] FIG. 2 illustrates a representation of a belief network in accordance with an aspect of the present invention. [0009] FIG. 3 illustrates an assisted decision making system utilizing an executable knowledge base in accordance with an aspect of the present invention. [0010] FIG. 4 illustrates two stories that can be stored in a knowledge base in accordance with an aspect of the present invention. [0011] FIG. 5 illustrates the union of the two stories illustrated in FIG. 5. [0012] FIG. 6 illustrates the intersection of the two stories illustrated in FIG. 5. [0013] FIG. 7 illustrates a methodology for constructing a decision network from a plurality of text documents in accordance with an aspect of the present invention. [0014] FIG. 8 illustrates a schematic block diagram of an exemplary operating environment for a system configured in accordance with an aspect of the present invention. DETAILED DESCRIPTION OF INVENTION [0015] The present invention relates to systems and methods for assisted decision making utilizing a knowledge base that comprises a plurality of stories, where a story is an executable belief network augmented by one or more characteristics of the hypotheses comprising the belief network, the evidence, and the content from which the evidence was extracted. By constructing the knowledge base from executable elements, the stories can be manipulated and updated in real time with new evidence. For example, a union or intersection between two stories or portions of stories can be formed, with appropriate belief values calculated from the known values, without the need for human intervention. Further, the knowledge base can be meaningfully shared between decision makers who may utilize different bodies of evidence. [0016] In certain aspects of the invention, the stories comprising the knowledge base can include Dempster-Shafer belief networks. Dempster-Shafer belief network include node parameters that conform to the Dempster-Shafer combination rule, which is based on an evidential interval: the sum of a belief value, a disbelief value, and an unknown value is equal to one. The Dempster-Shafer Combination Rule for fusion of evidence provides for nodes in a network represented as evidential intervals with values from the set of real numbers (0<=n<=1). Three parameters specify each node: "belief" (B), "unknown" (U) and "disbelief" (D). The unknown parameter is computed as: U=1-B-D. The Dempster-Shafer Combination Rule is symmetric, bounded, commutative, and associative. [0017] FIG. 1 illustrates an assisted decision making system 10 utilizing a knowledge base 12 comprising a plurality of executable stories. The knowledge base 12 is made available to a plurality of decision networks 14 and 16 through a translation interface 18. A given story comprises a belief network, a body of evidence utilized by its associated decision making algorithm (e.g., 14), and the context from which the evidence was extracted. The stories, along with other stories stored in the knowledge base 12 can be manipulated by a fusion engine to consolidate evidence associated with the stories. For example, two stories stored in the knowledge base can be merged, with the evidence from common hypothesis being combined to provide a belief value for the common hypotheses. [0018] It will be appreciated that the stories in the knowledge base incorporate executable belief networks and a body of evidence for evaluating their associated belief network. By executable, it is meant that the stories in the knowledge base are objects capable of independently updating their internal values (e.g., in accordance with the Dempster-Shafer combinational rule or Bayesian theory) to achieve a mathematically correct state given an external perturbation. The executable nature of these stories allows an analyst a great degree of freedom in forecasting the response of the various decision algorithms 14 and 16 to new evidence or changed, as a change in the central knowledge base can be quickly recognized and incorporated into the analysis of the decision algorithms 14 and 16. Further, the executable nature of the stories allows a given story to be evaluated independently of the knowledge base, allowing the stories to be easily transferred between multiple systems or analysts. [0019] The plurality of decision algorithms 14 and 16 have respective input formats for importing decision networks associated with the assisted decision making system 10. For example, a case based reasoning algorithm can process decision networks in tabular form, comprising a table of criteria and scores relating to a plurality of options considered by the algorithm. The knowledge base 12 and the decision algorithms are connected via the translation interface 18 that is operative to convert an executable story into a decision network in one or more of the input formats associated with the plurality of decision algorithms. For example, where one of the decision algorithms (e.g., 14) is a case based reasoning algorithm, a story or portion of a story from the knowledge base 12 can be converted into a tabular form consistent with the case based reasoning algorithm at the translation interface 18 and then provided to the algorithm for analysis. Accordingly, all of the decision algorithms can draw on the central knowledge base while receiving data in their associated formats. Continue reading about Knowledge base comprising executable stories... 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