Evidence gathering for hypothesis resolution using judicial evidential reasoning

Jaunzemis, Andris D.; Holzinger, Marcus J.; Chan, Moses W.; Shenoy, Prakash P.

doi:10.1016/j.inffus.2018.09.010

Cited by 23 publications

(5 citation statements)

References 32 publications

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“…Hypothesis-based sensor tasking. An alternative method for sensor network scheduling, called the hypothesis-based approach, directly quantifies decision-making questions as testable hypotheses that can be interrogated using evidence gathered from sensor observations (Jaunzemis, Holzinger, Chan, & Shenoy, 2019, Jaunzemis et al, 2017. Typically, a hypothesis belief structure is either converted to a probability distribution (as a Bayesian approximation of the belief structure; Cobb & Shenoy, 2006) or collapsed to a probability formulation that allows a decision maker to place bets on the hypothesis given the available evidence using familiar Bayesian constructs (Smets & Kennes, 1994;Tessem, 1993).…”

Section: Candidate Sensor Network Scheduling Approachesmentioning

confidence: 99%

“…Consider, as an illustrative example of hypothesis-based sensor tasking, a simplified version of the Judicial Evidential Reasoning (JER) algorithm (Jaunzemis, Holzinger, Chan, & Shenoy, 2018;Jaunzemis et al, 2019Jaunzemis et al, , 2017. For this hypothesis-based approach, entropy becomes the primary measurement of effectiveness, as low entropy signifies that the gathered evidence points toward a consistent and specific hypothesis resolution.…”

Section: Candidate Sensor Network Scheduling Approachesmentioning

confidence: 99%

“…A primary function of hypothesis-based sensor tasking, applied to SSA, is to analyze candidate tasking schedules to estimate hypothesis resolution based on the current hypothesis prioritization (CWR-10). Proposed approaches, such as JER (Jaunzemis et al, 2018(Jaunzemis et al, , 2019(Jaunzemis et al, , 2017, are predicated upon developing mappings from acquired data to evidence, so any evidential reasoning application must satisfactorily address CWR-3. Through the use of hypothesis entropy, quantifying both conflict and ambiguity, evidential reasoning provides a means for tracking hypothesis resolution against prescribed thresholds (CWR-6).…”

Section: Design Requirements Addressedmentioning

confidence: 99%

“…Typically, a hypothesis belief structure is either converted to a probability distribution (as a Bayesian approximation of the belief structure; Cobb & Shenoy, 2006) or collapsed to a probability formulation that allows a decision maker to place bets on the hypothesis given the available evidence using familiar Bayesian constructs (Smets & Kennes, 1994; Tessem, 1993). However, recent work has shown the utility of quantifying and incorporating ambiguity (Jaunzemis et al, 2019, 2017) and evidential reasoning methods such as Dempster–Shafer theory (Shafer, 1986) are more expressive in representing ambiguity than Bayesian approaches (Jirousek & Shenoy, 2018). Hypothesis entropy reduces a complex belief structure to a single measure that represents both the ambiguity (or nonspecificity) and conflict inherent in the current hypothesis knowledge, consider entropy as the hypothesis-resolution uncertainty, analogous to the state covariance being the orbit state uncertainty.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cognitive Systems Engineering Applied to Decision Support in Space Situational Awareness

Jaunzemis¹,

Feigh

Holzinger

et al. 2019

Journal of Cognitive Engineering and Decision Making

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Existing approaches for sensor network tasking in space situational awareness (SSA) rely on techniques from the 1950s and limited application areas while also requiring significant human-in-the-loop involvement. Increasing numbers of space objects, sensors, and decision-making needs create a demand for improved methods of gathering and fusing disparate information to resolve hypotheses about the space object environment. This work focuses on the cognitive work in SSA sensor tasking approaches. The application of a cognitive work analysis for the SSA domain highlights capabilities and constraints inherent to the domain that can drive SSA operations toward decision-maker goals. A control task analysis is also conducted to derive requirements for cognitive work and information relationships that support the information fusion and sensor allocation tasks of SSA. A prototype decision-support system is developed using a subset of the derived requirements. This prototype is evaluated in a human-in-the-loop experiment using both a hypothesis-based and covariance-based scheduling approaches. Results from this preliminary evaluation show operator ability to address SSA decision-maker hypotheses using the prototype decision-support system (DSS) using both scheduling approaches.

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Section: Candidate Sensor Network Scheduling Approachesmentioning

confidence: 99%

Section: Candidate Sensor Network Scheduling Approachesmentioning

confidence: 99%

Section: Design Requirements Addressedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cognitive Systems Engineering Applied to Decision Support in Space Situational Awareness

Jaunzemis¹,

Feigh

Holzinger

et al. 2019

Journal of Cognitive Engineering and Decision Making

View full text Add to dashboard Cite

show abstract

“…One of the most important reasons why D‐S evidence theory is used to handle uncertainty and imprecision is that the utilization of D‐S theory avoids the necessity of assigning prior probabilities (which would be extremely difficult to estimate) and provides more intuitive tools for managing uncertain knowledge . Belief structure is efficient to model different kinds of uncertainty .…”

Section: Introductionmentioning

confidence: 99%

Dependent evidence combination based on decision‐making trial and evaluation laboratory method

Deng

2019

Int J Intell Syst

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Dempster‐Shafer is widely used to address the problems of uncertainty. One assumption mentioned in this theory is that the distribution of information should be independent. In practice, the requirement cannot be fulfilled. One of the efficient methods to deal with dependent evidence is to calculate the correlation discounting. However, existing coefficient can only be applied to show the direct relation between evidence A and B but do not take the indirect relationship into consideration. To address this issue, in this paper, a new method to combine dependent evidence based on decision‐making trial and evaluation laboratory is presented, not only considering the relation between evidence A and B and the relation between evidence B and C, but also considering the transitive influence between evidence A and C. Finally, the experiments on some benchmark data sets are illustrated to show the efficiency of the proposed method.

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A new soft likelihood function based on power ordered weighted average operator

Song

Deng

2019

Int J Intell Syst

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The likelihood function is widely used in data processing but the classical likelihood function is too strict to deal with data with extreme values in real applications.To address this issue, a soft likelihood function based on power ordered weighted average (POWA) operator for probability distribution is proposed. Compared with the existing soft likelihood functions, one of the main features of the proposed likelihood function is that the weighted mean is displayed by the geometric mean of the weighted probability products. As a result, the effect on final result caused by extreme data can be efficiently decreased. Numerical examples are used to illustrate the efficiency of the proposed soft likelihood function. K E Y W O R D S geometric mean, likelihood function, OWA operator, POWA operator, soft likelihood function | INTRODUCTIONDealing with uncertainty information plays an important role in real applications. 1-3 Many math tools, such as fuzzy sets, 4-6 rough sets, 7 Z-numbers, 8-11 belief structures, 12-14 D numbers 15-17 entropy function, 18,19 and evidence theory, 20-22 are presented to handle different types of uncertainty information. Among these tools, probability is heavily studied and is widely used in many applications, including decision making, 23-26 estimation, 27,28 and uncertain measurement. [29][30][31][32] One of the most used models in probability is the likelihood function, which is used when data are available to describe plausibility of a parameter value. [33][34][35] However, it is very strict in real applications since the original likelihood function made up with the probability product is too strict to show the compatibility probabilities for the different pieces of evidence since any piece of probabilities, which are low-value would greatly reduce the likelihood function.How to cite this article: Song Y, Deng Y. A new soft likelihood function based on power ordered weighted average operator. Int J Intell

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Evidence gathering for hypothesis resolution using judicial evidential reasoning

Cited by 23 publications

References 32 publications

Cognitive Systems Engineering Applied to Decision Support in Space Situational Awareness

Cognitive Systems Engineering Applied to Decision Support in Space Situational Awareness

Dependent evidence combination based on decision‐making trial and evaluation laboratory method

A new soft likelihood function based on power ordered weighted average operator

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