Iterative Learning of Answer Set Programs from Context Dependent Examples

Abstract

In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. This paper is under consideration for acceptance in TPLP.

On evidence preservation requirements for forensic-ready systems

“…Note that the scalability results purely depend on the open-source prototype tool 7 used to support the specification synthesis. This could be significantly improved by deploying learning techniques for context-dependent learning [32], and distributed reasoning [34].…”

The complexity and generality of learning answer set programs

“…In [24], we showed that context-dependent examples could be used to simplify the encoding of certain tasks, by splitting the background knowledge into contexts that were only relevant to particular examples. Although any I L P …”

“…Consequently, the scope of ILP has recently been extended to learning answer set programs from examples of partial solutions of a given problem, with the intention being to provide algorithms that support automated learning of complex declarative knowledge. Learning ASP programs allows us to learn a variety of declarative non-monotonic, common-sense theories, including for instance Event Calculus [20] theories [21] and theories for scheduling problems and agents' preference models, both from real user data [22] and from synthetic data [23,24].…”

“…Learning choice rules is different from probabilistic ILP settings such as [25][26][27] where, in similar coins problems the focus would be on learning the probabilities of the two outcomes of are coin. Learning weak constraints enables a natural extension of ILP to preference learning [23], which has resulted to be effective in problem domains such as learning preference models for scheduling [23] and for urban mobility [24].…”

“…To tackle this aspect of learning ASP programs, we have extended the Learning from Answer Sets framework to Learning from Ordered Answer Sets (I L P L O A S ) [23] and demonstrated that our algorithm 1 is able to learn preferences in a scheduling domain. More recently, we have extended the I L P L O A S framework to I L P context L O A S , with context-dependent examples, which come together with extra contextual information [24].…”