
| Items   | Arcs   | Labeled   | Labeled after Prediction   | Time   | Solutions   |
|---------|--------|-----------|----------------------------|--------|-------------|
| 20      | 30     | 4/20      | 9/20                       | 0,010  | 180         |
| 20      | 30     | 10/20     | 12/20                      | 0,010  | 126         |
| 50      | 100    | 5/50      | 50/50                      | 0,020  | 24          |
| 50      | 200    | 10/50     | 48/50                      | 0,030  | 3           |
| 50      | 250    | 20/50     | 45/50                      | 0,030  | 32          |
| 50      | 250    | 35/50     | 45/50                      | 0,030  | 32          |
| 50      | 300    | 20/50     | 49/50                      | 0,040  | 2           |
| 50      | 400    | 20/50     | 50/50                      | 0,040  | 1           |
| 60      | 480    | 20/60     | 47/60                      | 0,060  | 6           |
| 60      | 480    | 30/60     | 47/60                      | 0,030  | 6           |

## 5 Preliminary Evaluation

It is very hard to evaluate the performance of SINDI in its entirety without extensive field deployment and analysis. We are working towards this goal but do not yet have hard results from field experiments that are long enough and diverse enough to yield scientific significance. We are also working on a sort of "canned" evaluation of the inference engine: we generate ad-hoc sensor data by simulating the behavior of the patient with an agent simulator (Repast) and create specific situations that test the functionality of the inference. Since this evaluation is not yet completed we offer here some other evaluation data regarding the sensor network and the ASP interpreter performance. We did several tests on the WSN and on the inference engine. The localization algorithm recognizes the correct area 90% of the time without further filtering techniques, and movement recognition is correct 95% of the time.

With respect to the inference engine, we evaluated ASP programs by using *Gringo* as grounder and the *Clasp* solver (Gebser et al. 2007) that supports constraints, choice rules and weight rules (Niemelä and Simons 2001) and can solve complex reasoning tasks very efficiently due to the heuristics used, combining ASP expressivity with boolean constraint solving.

While the reasoning processes for context interpretation and evaluation takes advantage of the nonmonotonic nature of ASP to reason about collected and aggregated data, reasoning tasks for health assessment also rely on the computational power of ASP: the prediction task is based on a complex reasoning process that takes into account the whole graph of dependencies to find maximal consistent labellings; local explanation can consider an arbitrary class of items to find subsets of dependency arcs that represent a minimal common explanation, obtained by using qualitative and quantitative optimization techniques; prevention is based on a compact representation of feedback policies that are automatically mapped into ASP and enforced by applying adaptive optimization strategies taking into account contextual settings.