We introduce a stepping methodology for answer-set programming (ASP) that allows for debugging answer-set programs and is based on the stepwise application of rules. Similar to debugging in imperative languages, where the behaviour of a program is observed during a step-by-step execution, stepping for ASP allows for observing the effects that rule applications have in the computation of an answer set. While the approach is inspired from debugging in imperative programming, it is conceptually different to stepping in other paradigms due to non-determinism and declarativity that are inherent to ASP. In particular, unlike statements in an imperative program that are executed following a strict control flow, there is no predetermined order in which to consider rules in ASP during a computation. In our approach, the user is free to decide which rule to consider active in the next step following his or her intuition. This way, one can focus on interesting parts of the debugging search space. Bugs are detected during stepping by revealing differences between the actual semantics of the program and the expectations of the user. As a solid formal basis for stepping, we develop a framework of computations for answer-set programs. For fully supporting different solver languages, we build our framework on an abstract ASP language that is sufficiently general to capture different solver languages. To this end, we make use of abstract constraints as an established abstraction for popular language constructs such as aggregates. Stepping has been implemented in SeaLion, an integrated development environment for ASP. We illustrate stepping using an example scenario and discuss the stepping plugin of SeaLion. Moreover, we elaborate on methodological aspects and the embedding of stepping in the ASP development process.

Lameness in sows has an economic impact on pig production and is a major welfare concern. The aim of the present project was to develop methods to evaluate and quantify lameness in breeding sows. Five methods to study lameness were compared between themselves and with visual gait scoring used as a reference: footprint analysis, kinematics, accelerometers, lying-to-standing transition and foot lesion observation. Fifty sows of various parities and stages of gestation were selected using visual gait scoring and distributed into three groups: lame (L), mildly lame (ML) and non-lame (NL). They were then tested using each method. Kinematics showed that L sows had a lower walking speed than NL sows (L: 0.83 ± 0.04, NL: 0.96 ± 0.03 m/s; P < 0.05), a shorter stride length than ML sows (L: 93.0 ± 2.6, ML: 101.2 ± 1.5 cm; P < 0.05) and a longer stance time than ML and NL sows (L: 0.83 ± 0.03, ML: 0.70 ± 0.03, NL: 0.69 ± 0.02 s; P < 0.01). Accelerometer measurements revealed that L sows spent less time standing over a 24-h period (L: 6.3 ± 1.3, ML: 13.7 ± 2.4, NL: 14.5 ± 2.4%; P < 0.01), lay down earlier after feeding (L: 33.4 ± 4.6, ML: 41.7 ± 3.1, NL: 48.6 ± 2.9 min; P < 0.05) and tended to step more often during the hour following feeding (L: 10.1 ± 2.0, ML: 6.1 ± 0.5, NL: 5.4 ± 0.4 step/min standing; P = 0.06) than NL sows, with the ML sows having intermediate values. Visual observation of back posture showed that 64% of L sows had an arched back, compared with only 14% in NL sows (P = 0.02). Finally, footprint analysis and observation of lying-to-standing transition and foot lesions were not successful in detecting significant differences between L, ML and NL sows. In conclusion, several quantitative variables obtained from kinematics and accelerometers proved to be successful in identifying reliable indicators of lameness in sows. Further work is needed to relate these indicators with causes of lameness and to develop methods that can be implemented on the farm.