1,079 research outputs found
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
We present a heuristic based algorithm to induce \textit{nonmonotonic} logic
programs that will explain the behavior of XGBoost trained classifiers. We use
the technique based on the LIME approach to locally select the most important
features contributing to the classification decision. Then, in order to explain
the model's global behavior, we propose the LIME-FOLD algorithm ---a
heuristic-based inductive logic programming (ILP) algorithm capable of learning
non-monotonic logic programs---that we apply to a transformed dataset produced
by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm.
Our experiments with UCI standard benchmarks suggest a significant improvement
in terms of classification evaluation metrics. Meanwhile, the number of induced
rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system
Answer Sets for Consistent Query Answering in Inconsistent Databases
A relational database is inconsistent if it does not satisfy a given set of
integrity constraints. Nevertheless, it is likely that most of the data in it
is consistent with the constraints. In this paper we apply logic programming
based on answer sets to the problem of retrieving consistent information from a
possibly inconsistent database. Since consistent information persists from the
original database to every of its minimal repairs, the approach is based on a
specification of database repairs using disjunctive logic programs with
exceptions, whose answer set semantics can be represented and computed by
systems that implement stable model semantics. These programs allow us to
declare persistence by defaults and repairing changes by exceptions. We
concentrate mainly on logic programs for binary integrity constraints, among
which we find most of the integrity constraints found in practice.Comment: 34 page
Super Logic Programs
The Autoepistemic Logic of Knowledge and Belief (AELB) is a powerful
nonmonotic formalism introduced by Teodor Przymusinski in 1994. In this paper,
we specialize it to a class of theories called `super logic programs'. We argue
that these programs form a natural generalization of standard logic programs.
In particular, they allow disjunctions and default negation of arbibrary
positive objective formulas.
Our main results are two new and powerful characterizations of the static
semant ics of these programs, one syntactic, and one model-theoretic. The
syntactic fixed point characterization is much simpler than the fixed point
construction of the static semantics for arbitrary AELB theories. The
model-theoretic characterization via Kripke models allows one to construct
finite representations of the inherently infinite static expansions.
Both characterizations can be used as the basis of algorithms for query
answering under the static semantics. We describe a query-answering interpreter
for super programs which we developed based on the model-theoretic
characterization and which is available on the web.Comment: 47 pages, revised version of the paper submitted 10/200
The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for different
types of instances of hard combinatorial problems. As a consequence, most
solvers for the propositional satisfiability problem (SAT) expose parameters
that allow them to be customized to a particular family of instances. In the
international SAT competition series, these parameters are ignored: solvers are
run using a single default parameter setting (supplied by the authors) for all
benchmark instances in a given track. While this competition format rewards
solvers with robust default settings, it does not reflect the situation faced
by a practitioner who only cares about performance on one particular
application and can invest some time into tuning solver parameters for this
application. The new Configurable SAT Solver Competition (CSSC) compares
solvers in this latter setting, scoring each solver by the performance it
achieved after a fully automated configuration step. This article describes the
CSSC in more detail, and reports the results obtained in its two instantiations
so far, CSSC 2013 and 2014
Reasoning with minimal models: efficient algorithms and applications
AbstractReasoning with minimal models is at the heart of many knowledge-representation systems. Yet it turns out that this task is formidable, even when very simple theories are considered. In this paper, we introduce the elimination algorithm, which performs, in linear time, minimal model finding and minimal model checking for a significant subclass of positive CNF theories which we call positive head-cycle-free (HCF) theories. We also prove that the task of minimal entailment is easier for positive HCF theories than it is for the class of all positive CNF theories. Finally, we show how variations of the elimination algorithm can be applied to allow queries posed on disjunctive deductive databases and disjunctive default theories to be answered in an efficient way
ARC-TEC : acquisition, representation and compilation of technical knowledge
A global description of an expert system shell for the domain of mechanical engineering is presented. The ARC-TEC project constitutes an AI approach to realize the CIM idea. Along with conceptual solutions, it provides a continuous sequence of software tools for the acquisition, representation and compilation of technical knowledge. The shell combines the KADS knowledge-acquisition methodology, the KL-ONE representation theory and the WAM compilation technology. For its evaluation a prototypical expert system for production planning is developed. A central part of the system is a knowledge base formalizing the relevant aspects of common sense in mechanical engineering. Thus, ARC-TEC is less general than the CYC project but broader than specific expert systems for planning or diagnosis
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