Argumentation is used to make important decisions based on the information available. We tackle the question of whether it is possible to automatically extract argumentation from a given set of documents to justify a ...
详细信息
Argumentation is used to make important decisions based on the information available. We tackle the question of whether it is possible to automatically extract argumentation from a given set of documents to justify a given claim. We propose a framework that (i) extracts an argumentation structure from a given set of documents using Large Language Models (LLMs), (ii) represents it as an answer set program, (iii) and then uses the answer set program to prove the claim. We use a semantics-guided approach that leverages the FrameNet lexical database to generate sub-claims that are anchored to the concepts related to the claim. We demonstrate the efficacy of our method with an example. 2023 Copyright for this paper by its authors.
We are interested in automatizing reasoning with and about study regulations, catering to various stakeholders, ranging from administrators, over faculty, to students at different stages. Our work builds on an extensi...
详细信息
We are interested in automatizing reasoning with and about study regulations, catering to various stakeholders, ranging from administrators, over faculty, to students at different stages. Our work builds on an extensive analysis of various study programs at the University of Potsdam. The conceptualization of the underlying principles provides us with a formal account of study regulations. In particular, the formalization reveals the properties of admissible study plans. With these at end, we propose an encoding of study regulations in Answer Set programming that produces corresponding study plans. Finally, we show how this approach can be extended to a generic user interface for exploring study plans. 2023 Copyright for this paper by its authors.
We introduce new semantics for acyclic probabilistic logic programs in terms of Pearl’s functional causal models. Further, we show that our semantics is consistent with the classical distribution semantics and CP-log...
详细信息
This paper develops a new formalism CDLP by combining ASP and constrained default logic to facilitate modeling questions with incomplete information, such that both Reiter’s defaults and constraint defaults can be re...
详细信息
In the process of creating a declarative program, the programmer transforms a problem specification expressed in a natural language into an executable specification. We study the case when the given specification is e...
详细信息
The context of the paper is developing logic-based components for hybrid – machine learning plus logic – commonsense question answering systems. The paper presents the main principles and several lessons learned fro...
详细信息
Probabilistic logic programs are logic programs in which some of the clauses are annotated with probabilistic facts. The behaviour of relations in these clauses can be very complex, leading to scalability issues. Asym...
详细信息
Probabilistic logic programs are logic programs in which some of the clauses are annotated with probabilistic facts. The behaviour of relations in these clauses can be very complex, leading to scalability issues. Asymptotic representations, in which queries are completely independent of the domain size and which approximate a probabilistic logic program on large domains, allow us to gain an understanding on how a probabilistic logic programs will behave for increasing domain sizes, and can be computed without actually having to execute the logic program. In particular, every probabilistic logic program under the distribution semantics is asymptotically equivalent to an acyclic probabilistic logic program consisting only of determinate clauses over probabilistic facts. We present asymptoticplp, a Prolog implementation of an algorithm which computes this. The transformation proceeds in several, modular steps which are of independent interest. These steps include rewriting the probabilistic logic program to a formula of least fixed point logic and then applying asymptotic quantifier elimination on the formula. Quantifier-free first-order formulas are then rewritten as acyclic determinate stratified DATALOG formulas, which together with the original probabilistic facts form a (probabilistic) logic program. 2023 Copyright for this paper by its authors.
For modeling the assumption-based intelligent agents who make assumptions and use them to construct their belief sets, this paper proposes a logicprogramming language AASP (Assumable Answer Set programming) by extend...
详细信息
We present a model checker for Linear Temporal logic using Goal-Directed Answer Set programming under Costable model semantics (CoASP). Costable model semantics allows for positive loops to succeed unlike Stable model...
详细信息
We present a novel neurosymbolic framework called NeSyFOLD to extract logic rules from a CNN and create a NeSyFOLD model to classify images. NeSyFOLD’s learning pipeline is as follows: (i) We first pre-train a CNN on...
详细信息
We present a novel neurosymbolic framework called NeSyFOLD to extract logic rules from a CNN and create a NeSyFOLD model to classify images. NeSyFOLD’s learning pipeline is as follows: (i) We first pre-train a CNN on the input image dataset and extract activations of the last layer kernels as binary values;(ii) Next, we use the FOLD-SE-M rule-based machine learning algorithm to generate a logic program that can classify an image—represented as a vector of binary activations corresponding to each kernel—while producing a logical explanation. The rules generated by the FOLD-SE-M algorithm have kernel numbers as predicates. We have devised a novel algorithm for automatically mapping the CNN kernels to semantic concepts in the images. This mapping is used to replace predicate names (kernel numbers) in the rule-set with corresponding semantic concept labels. The resulting rule-set is interpretable, and can be intuitively understood by humans. We compare our NeSyFOLD framework with the ERIC system that uses a decision-tree like algorithm to obtain the rules. Our framework has the following advantages over ERIC: (i) In most cases, NeSyFOLD generates smaller rule-sets without compromising on the accuracy and fidelity;(ii) NeSyFOLD generates the mapping of filter numbers to semantic labels automatically. 2023 Copyright for this paper by its authors.
暂无评论