Belief Revision in Structured Probabilistic Argumentation

In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates

Reasoning about Uncertain Information and Conflict Resolution through Trust Revision

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A simulated study of implicit feedback models

In this paper we report on a study of implicit feedback models for unobtrusively tracking the information needs of searchers. Such models use relevance information gathered from searcher interaction and can be a potential substitute for explicit relevance feedback. We introduce a variety of implicit feedback models designed to enhance an Information Retrieval (IR) system's representation of searchers' information needs. To benchmark their performance we use a simulation-centric evaluation methodology that measures how well each model learns relevance and improves search effectiveness. The results show that a heuristic-based binary voting model and one based on Jeffrey's rule of conditioning [5] outperform the other models under investigation

Decision-analytic cost-effectiveness model to compare prostate cryotherapy to androgen deprivation therapy for treatment of radiation recurrent prostate cancer

Objective: To determine the cost-effectiveness of salvage cryotherapy (SC) in men with radiation recurrent prostate cancer (RRPC). Design: Cost-utility analysis using decision analytic modelling by a Markov model. Setting and methods: Compared SC and androgen deprivation therapy (ADT) in a cohort of patients with RRPC (biopsy proven local recurrence, no evidence of metastatic disease). A literature review captured published data to inform the decision model, and resource use data were from the Scottish Prostate Cryotherapy Service. The model was run in monthly cycles for RRPC men, mean age of 70 years. The model was run over the patient lifetime, to assess changes in patient health states and the associated quality of life, survival and cost impacts. Results are reported in terms of the discounted incremental costs and discounted incremental quality-adjusted life years (QALYs) gained between the 2 alternative interventions. Probabilistic sensitivity analysis used a 10 000 iteration Monte Carlo simulation. Results: SC has a high upfront treatment cost, but delays the ongoing monthly cost of ADT. SC is the dominant strategy over the patient lifetime; it is more effective with an incremental 0.56 QALY gain (95% CI 0.28 to 0.87), and less costly with a reduced lifetime cost of £29 719 (€37 619) (95% CI −51 985 to −9243). For a ceiling ratio of £30 000, SC has a 100% probability to be cost-effective. The cost neutral point was at 3.5 years, when the upfront cost of SC (plus any subsequent cumulative cost of side effects and ADT) equates the cumulative cost in the ADT arm. Limitations of our model may arise from its insensitivity to parameter or structural uncertainty. Conclusions: The platform for SC versus ADT cost-effective analysis can be employed to evaluate other treatment modalities or strategies in RRPC. SC is the dominant strategy, costing less over a patient's lifetime with improvements in QALYs

CASP-DM: Context Aware Standard Process for Data Mining

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs

The SURE Reliability Analysis Program

The SURE program is a new reliability analysis tool for ultrareliable computer system architectures. The program is based on computational methods recently developed for the NASA Langley Research Center. These methods provide an efficient means for computing accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. Once a semi-Markov model is described using a simple input language, the SURE program automatically computes the upper and lower bounds on the probability of system failure. A parameter of the model can be specified as a variable over a range of values directing the SURE program to perform a sensitivity analysis automatically. This feature, along with the speed of the program, makes it especially useful as a design tool