Logic Production System (LPS) is a logic-based framework for modelling
reactive behaviour. Based on abductive logic programming, it combines reactive
rules with logic programs, a database and a causal theory that specifies
transitions between the states of the database. This paper proposes a
systematic mapping of the Kernel of this framework (called KELPS) into an
answer set program (ASP). For this purpose a new variant of KELPS with finite
models, called $n$-distance KELPS, is introduced. A formal definition of the
mapping from this $n$-distance KELPS to ASP is given and proven sound and
complete. The Answer Set Programming paradigm allows to capture additional
behaviours to the basic reactivity of KELPS, in particular proactive,
preemptive and prospective behaviours. These are all discussed and illustrated
with examples. Then a hybrid framework is proposed that integrates KELPS and
ASP, allowing to combine the strengths of both paradigms. Under consideration
in Theory and Practice of Logic Programming (TPLP).

本文提出了一种将 Logic Production System (LPS) 中的 Kernel（KELPS）系统地映射成 Answer Set Programming (ASP) 的方法，并以一个新的有限模型变体 n-distance KELPS 作为实现基础。最后，提出了一个整合 KELPS 和 ASP 优点的混合框架。

响应式答案集编程

Reactive Answer Set Programming

Uncertain information is being taken into account in an increasing number of
application fields. In the meantime, abduction has been proved a powerful tool
for handling hypothetical reasoning and incomplete knowledge. Probabilistic
logical models are a suitable framework to handle uncertain information, and in
the last decade many probabilistic logical languages have been proposed, as
well as inference and learning systems for them. In the realm of Abductive
Logic Programming (ALP), a variety of proof procedures have been defined as
well. In this paper, we consider a richer logic language, coping with
probabilistic abduction with variables. In particular, we consider an ALP
program enriched with integrity constraints `a la IFF, possibly annotated with
a probability value. We first present the overall abductive language, and its
semantics according to the Distribution Semantics. We then introduce a proof
procedure, obtained by extending one previously presented, and prove its
soundness and completeness.

本文讨论了使用带有变量的概率性假设建模的丰富逻辑语言进行概率性逆推的问题，提出了一种证明过程，该过程的有效性得到了证明。

具有概率完整性约束的非基于地面的非完整推理逻辑编程

Nonground Abductive Logic Programming with Probabilistic Integrity  Constraints

We present the CIFF proof procedure for abductive logic programming with
constraints, and we prove its correctness. CIFF is an extension of the IFF
proof procedure for abductive logic programming, relaxing the original
restrictions over variable quantification (allowedness conditions) and
incorporating a constraint solver to deal with numerical constraints as in
constraint logic programming. Finally, we describe the CIFF system, comparing
it with state of the art abductive systems and answer set solvers and showing
how to use it to program some applications. (To appear in Theory and Practice
of Logic Programming - TPLP).

本文介绍了 CIFF 证明过程，证明了它的正确性，它是 IF 证明过程的扩展，用于处理带有数量约束的突发逻辑编程问题，同时介绍了 CIFF 系统，与最先进的突发系统和答案集求解器进行比较，并展示了如何使用它来编程一些应用程序。

带约束的归纳逻辑编程 CIFF 证明过程：理论、实现和实验

The CIFF Proof Procedure for Abductive Logic Programming with  Constraints: Theory, Implementation and Experiments

Abductive logic programming offers a formalism to declaratively express and
solve problems in areas such as diagnosis, planning, belief revision and
hypothetical reasoning. Tabled logic programming offers a computational
mechanism that provides a level of declarativity superior to that of Prolog,
and which has supported successful applications in fields such as parsing,
program analysis, and model checking. In this paper we show how to use tabled
logic programming to evaluate queries to abductive frameworks with integrity
constraints when these frameworks contain both default and explicit negation.
The result is the ability to compute abduction over well-founded semantics with
explicit negation and answer sets. Our approach consists of a transformation
and an evaluation method. The transformation adjoins to each objective literal
$O$ in a program, an objective literal $not(O)$ along with rules that ensure
that $not(O)$ will be true if and only if $O$ is false. We call the resulting
program a {\em dual} program. The evaluation method, \wfsmeth, then operates on
the dual program. \wfsmeth{} is sound and complete for evaluating queries to
abductive frameworks whose entailment method is based on either the
well-founded semantics with explicit negation, or on answer sets. Further,
\wfsmeth{} is asymptotically as efficient as any known method for either class
of problems. In addition, when abduction is not desired, \wfsmeth{} operating
on a dual program provides a novel tabling method for evaluating queries to
ground extended programs whose complexity and termination properties are
similar to those of the best tabling methods for the well-founded semantics. A
publicly available meta-interpreter has been developed for \wfsmeth{} using the
XSB system.

本文介绍了一个将表扬逻辑编程与表格逻辑编程结合的方法，可以在显式否定和答案集的情况下计算基于显式否定和答案集的抽象，同时提供了一个用于评估查询的新型表格方法。