Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators for this multi-agent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating on direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Yager's rule is shown to be the better operator under various parameter values, i.e. convergence to the best state, robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen

The Application of NCaRBS to the Trendelenburg Test and Total Hip Arthroplasty Outcome

This paper compares the frontal plane hip func- tion of subject’s known to have had hip arthroplasty via either the lateral (LA) or posterior (PA) surgical approaches and a group of subjects associated with no pathology (NP). This is investigated through the Trendelenburg test using 3D motion analysis and classification. Here, a recent develop- ment on the Classification and Ranking Belief Simplex (CaRBS) technique, able to undertake n-state classification, so termed NCaRBS is employed. The relationship between post-operative hip function measured during a Trendelen- burg Test using three patient characteristics (pelvic obliquity, frontal plane hip moment and frontal plane hip power) of LA, PA and NP subjects are modelled together. Using these characteristics, the classification accuracy was 93.75% for NP, 57.14% for LA, 38.46% for PA. There was a clear distinction between NP and post-surgical function. 3/6 LA subjects and 6/8 PA subjects were misclassified as having NP function, implying that greater function is restored following the PA to surgery. NCaRBS achieved a higher accuracy (65.116%) than through a linear discriminant analysis (48.837%). A Neural Network with two-nodes achieved the same accuracy (65.116%) and as expected was further improved with three-nodes (69.767%). A valuable benefit to the employment of the NCaRBS technique is the graphical exposition of the contribution of patient characteristics to the classification analysis