There are many reasons why software can be hard to implement. For important classes of applications, the main source of complexity is the domain knowledge that is involved. One such class is that of configuration software, which serves to assist a user in making choices in accordance with certain constraints. For instance, consider an application that helps students compose a study program that complies with all relevant university regulations. The reason why this may be difficult to implement is that these regulations can get quite complicated, making them hard to handle, at least for imperative programming methods. A better approach might be to follow the paradigm of a knowledge base system: explicitly represent the domain knowledge in a declarative way, and implement the behavior of the application by performing various logical inference methods on it. Doing this well, however, requires that a number of different components be got right. Most importantly, we need an expressive and purely declarative knowledge representation language, together with a set of useful inference methods. In this paper, we present a framework for implementing this kind of software, based on a rich extension of first-order logic.

Abstract. Due to the development of efficient solvers, declarative problem solving frameworks based on model generation are becoming more and more applicable in practice. However, there are almost no tools to support debugging in these frameworks. For several reasons, current solvers are not suitable for debugging by tracing. In this paper, we propose a new solver algorithm for one of these frameworks, namely Model Expansion, that allows for debugging by tracing. We explain how to explore the trace of this solver in order to quickly locate a bug and we compare our debugging method with existing ones for Answer Set Programming and the Alloy system.

The fragment ∃∀SO(ID) of second order logic extended with inductive definitions is expressive, and many interesting problems, such as conformant planning, can be naturally expressed as finite domain satisfiability problems of this logic. Such satisfiability problems are computationally hard (Σ P 2 ). In this paper, we develop an approximate, sound but incomplete method for solving such problems that transforms a ∃∀SO(ID) to a ∃SO(ID) problem. The finite domain satisfiability problem for the latter language is in NP and can be handled by several existing solvers. We show that this provides an effective method for solving practically useful problems, such as common examples of conformant planning. We also propose a more complete translation to ∃SO(F P ), existential SO extended with nested inductive and coinductive definitions.

Objectives One identified solution to prevent obesity in cats is to control and limit their calorie intake. The objective of the present work was to better elucidate the impact of calorie cut-off on the feeding behaviour of cats. Methods A control (n = 31) and a test group of cats (n = 38) were included in the present study. Both groups received the same food variety during the study. A period of ad libitum feeding was initially set (T0), followed by a 9-month mild calorie restriction period for the test group only (T9; average calorie restriction = 6%), and a final period of ad libitum feeding (T10). The individual cat feeding behaviours were measured via an electronic feeding system, and agonistic interactions between cats during food anticipation via video observations. Generalised linear mixed models were fitted to compare all feeding parameters between periods by group. No statistical analyses could be performed on the agonistic interactions data owing to their structure. Results The feeding behaviour of the control group remained stable during the entire study, while the test group showed fewer but larger meals taken at shorter time intervals and a faster eating rate in response to calorie restriction. The average total number of agonistic interactions per cat increased during the calorie cut-off period in the test group only. One month after returning to ad libitum feeding, all behaviours were largely restored to baseline values. Conclusions and relevance Behavioural changes expressed by cats under calorie restriction can explain some of the difficulties obtaining cat owners’ compliance with dietary restriction, especially in multi-cat households. Feeding strategies should be utilised to help cats be less impulsive and maintain normal feeding patterns when moving away from ad libitum feeding.

Sensing actions, which allow an agent to increase its knowledge about the environment, are problematic for traditional planning languages. In this paper we propose a very general framework for representing both changes to the real world and to the knowledge of an agent, based on a first order linear time calculus. Our framework is more general than most existing approaches, because our semantics explicitly represents, for each point in time, not only the agent's knowledge about that timepoint, but also about the past and the future. By applying a general approximation method for classical logic to this framework, we obtain an efficient and sound but incomplete reasoning method.