Answer Set Research Articles

Human Conditional Reasoning in Answer Set Programming

AbstractGiven a conditional sentence “ ${\varphi}\Rightarrow \psi$ " (if ${\varphi}$ then $\psi$ ) and respective facts, four different types of inferences are observed in human reasoning: Affirming the antecedent (AA) (or modus ponens) reasons $\psi$ from ${\varphi}$ ; affirming the consequent (AC) reasons ${\varphi}$ from $\psi$ ; denying the antecedent (DA) reasons $\neg\psi$ from $\neg{\varphi}$ ; and denying the consequent (DC) (or modus tollens) reasons $\neg{\varphi}$ from $\neg\psi$ . Among them, AA and DC are logically valid, while AC and DA are logically invalid and often called logical fallacies. Nevertheless, humans often perform AC or DA as pragmatic inference in daily life. In this paper, we realize AC, DA and DC inferences in answer set programming. Eight different types of completion are introduced, and their semantics are given by answer sets. We investigate formal properties and characterize human reasoning tasks in cognitive psychology. Those completions are also applied to commonsense reasoning in AI.

An answer set programming-based implementation of epistemic probabilistic event calculus

We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent's own action executions and environmentally triggered events. Some of the agent's actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC's own declarative, Bayesian-inspired semantics.

Veil: A Storage and Communication Efficient Volume-Hiding Algorithm

This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage, coupled with prior knowledge about data distribution and/or previously executed queries, can reveal both ciphertexts and current user queries. We develop a solution to prevent volume leakage, entitled Veil, that partitions the dataset by randomly mapping keys to a set of equi-sized buckets. Veil provides a tunable mechanism for data owners to explore a trade-off between storage and communication overheads. To make buckets indistinguishable to the adversary, Veil uses a novel padding strategy that allow buckets to overlap, reducing the need to add fake records. Both theoretical and experimental results show Veil to significantly outperform existing state-of-the-art.

DValidator: An approach for validating dependencies in build configurations

Reusing components is a well-established practice in modern software engineering and brings many advantages, such as a reduction of development costs and time. However, there are still several problems when reusing software components, such as the management of the dependencies of a project. Modern build systems provide dependency managers to support developers when dealing with dependencies. But even with this tool support, dependency management is an error-prone task which can lead to dependency hell if it gets out of control.In this paper, we propose DValidator, an approach that considers dependencies on project level and method call level for validating dependencies in build configurations. First, DValidator encodes a project’s dependency graph as specified in a build configuration and its call graph into a representation using Answer Set Programming (ASP). Then it uses Clingo as a solver to detect problems with the dependencies in that build configuration. In a preliminary evaluation with four open source Maven projects we show that our approach can detect selected dependency smells in less than eight seconds. Next steps concern the investigation of our approach for automatically improving dependency configurations, such as automatically repairing dependency smells and conflicts.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Assumption-based argumentation for extended disjunctive logic programming and its relation to nonmonotonic reasoning

The motivation of this study is that Reiter’s default theory as well as assumption-based argumentation frameworks corresponding to default theories have difficulties in handling disjunctive information, while a disjunctive default theory (ddt) avoids them. This paper presents the semantic correspondence between generalized assumption-based argumentation (ABA) and extended disjunctive logic programming as well as the correspondence between ABA and nonmonotonic reasoning approaches such as disjunctive default logic and prioritized circumscription. To overcome the above-mentioned difficulties of ABA frameworks corresponding to default theories, we propose an assumption-based framework (ABF) translated from an extended disjunctive logic program (EDLP) since an EDLP can be translated into a ddt. Our ABF incorporates explicit negation and the connective of disjunction “|” to Heyninck and Arieli’s ABF induced by a disjunctive logic program. In this paper, first, we show how arguments are constructed from disjunctive rules in our proposed ABF. Then, we show the correspondence between answer sets of an EDLP P and stable extensions of the ABF translated from P with trivialization rules. After defining rationality postulates, we show answer sets of a consistent EDLP are captured by consistent stable extensions of the translated ABF with no trivialization rules. Finally, we show the correspondence between ABA and disjunctive default logic (resp. prioritized circumscription). The relation between ABA and possible model semantics of EDLPs is also discussed.

Scalable Neural-Probabilistic Answer Set Programming

The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for probabilistic logic programming to be carried out via the probability estimations of deep neural networks (DNNs). However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation. In our work, we propose an easy integration of tractable probabilistic inference within a DPPL. To this end, we introduce SLASH, a novel DPPL that consists of Neural-Probabilistic Predicates (NPPs) and a logic program, united via answer set programming (ASP). NPPs are a novel design principle allowing for combining all deep model types and combinations thereof to be represented as a single probabilistic predicate. In this context, we introduce a novel +/− notation for answering various types of probabilistic queries by adjusting the atom notations of a predicate. To scale well, we show how to prune the stochastically insignificant parts of the (ground) program, speeding up reasoning without sacrificing the predictive performance. We evaluate SLASH on various tasks, including the benchmark task of MNIST addition and Visual Question Answering (VQA).

Comparison of Answers between ChatGPT and Human Dieticians to Common Nutrition Questions.

More people than ever seek nutrition information from online sources. The chatbot ChatGPT has seen staggering popularity since its inception and may become a resource for information in nutrition. However, the adequacy of ChatGPT to answer questions in the field of nutrition has not been investigated. Thus, the aim of this research was to investigate the competency of ChatGPT in answering common nutrition questions. Dieticians were asked to provide their most commonly asked nutrition questions and their own answers to them. We then asked the same questions to ChatGPT and sent both sets of answers to other dieticians (N = 18) or nutritionists and experts in the domain of each question (N = 9) to be graded based on scientific correctness, actionability, and comprehensibility. The grades were also averaged to give an overall score, and group means of the answers to each question were compared using permutation tests. The overall grades for ChatGPT were higher than those from the dieticians for the overall scores in five of the eight questions we received. ChatGPT also had higher grades on five occasions for scientific correctness, four for actionability, and five for comprehensibility. In contrast, none of the answers from the dieticians had a higher average score than ChatGPT for any of the questions, both overall and for each of the grading components. Our results suggest that ChatGPT can be used to answer nutrition questions that are frequently asked to dieticians and provide encouraging support for the role of chatbots in offering nutrition support.

PREVALENCE AND CAUSES OF AUTONOMIC DYSFUNCTION SYNDROME IN PUBERTY CHILDREN

Annotation. This scientific study is devoted to the study of autonomic dysfunction syndrome (АVD), which is most often formed in adolescents. The results of the analysis of outpatient records of children are presented, the frequency of occurrence is determined and risk factors for АVD in students of secondary specialized educational institutions are identified. The purpose of the study: is to study the frequency of occurrence and identify risk factors for the development of autonomic dysfunction in puberty children. Materials and methods: To study the frequency of occurrence and identify risk factors for the development of autonomic dysfunction syndrome, 100 adolescents enrolled in secondary specialized educational institutions aged 12-17 years were examined. A prerequisite was the informed consent of the teenager or parents to participate in the study. The study was conducted in the form of a questionnaire and a survey algorithm was created. Questionnaire forms with the identified factors were filled out for all examined adolescents. Results and discussions According to the survey data, it was revealed that the greatest factors of the development of АVD in children aged 12-15 years are mental fatigue (50%), hormonal imbalance, pre- and puberty (44%), stress during pregnancy (41%), etc. Therefore, taking into account the established risk factors, it is possible to predict the development of АVD in children, especially with burdened obstetric pathologies and hereditary factors. Conclusion. The problem of АVD in children and adolescents is relevant not only for pediatricians and pediatric cardiologists but also for doctors of other medical specialties: therapists, neurologists, psychotherapists, and endocrinologists. The syndrome of autonomic dysfunction (АVD) and its manifestations in children still cause a large number of questions to which there are the same set of answers, and not always unambiguous.

Enhancing question answering in educational knowledge bases using question-aware graph convolutional network

In this paper, a question answering method is proposed for educational knowledge bases (KBQA) using a question-aware graph convolutional network (GCN). KBQA provides instant tutoring for learners, improving their learning interest and efficiency. However, most open domain KBQA methods model question sentences and candidate answer entities independently, limiting their effectiveness. The proposed method extracts description information and query entity sets for a specific question, processes them with Transformer and pre-trained embeddings of the knowledge base, and extracts a subgraph of candidate answer sets from the knowledge base. The node information is updated by GCN with two attention mechanisms expressed by the question description and query entity set, respectively. The query description information, query entity set, and candidate entity representation are fused to calculate the score and predict the answer. Experiments on MOOC Q&A dataset show that the proposed method outperforms benchmark models.

Syntactic ASP forgetting with forks

Answer Set Programming (ASP) constitutes nowadays one of the most successful paradigms for practical Knowledge Representation and declarative problem solving. The formal analysis of ASP programs is essential for a rigorous treatment of specifications, the correct construction of solvers and the extension with other representational features. In this paper, we present a syntactic transformation, called the unfolding operator, that allows forgetting an atom in a logic program (under ASP semantics). The main advantage of unfolding is that, unlike other syntactic operators, it is always applicable and guarantees strong persistence, that is, the result preserves the same stable models with respect to any context where the forgotten atom does not occur. The price for its completeness is that the result is an expression that may contain the fork operator. Yet, we illustrate how, in some cases, the application of fork properties may allow us to reduce the fork to a logic program.

Lifted inference for statistical statements in probabilistic answer set programming

In 1990, Halpern proposed the distinction between Type 1 and Type 2 statements: the former express statistical information about a domain of interest while the latter define a degree of belief. An example of Type 1 statement is “30% of the elements of a domain share the same property” while an example of Type 2 statement is “the element x has the property y with probability p”. Recently, Type 1 statements were given an interpretation in terms of probabilistic answer set programs under the credal semantics in the PASTA framework. The algorithm proposed for inference requires the enumeration of all the answer sets of a given program, and so it is impractical for domains of not trivial size. The field of lifted inference aims to identify programs where inference can be computed without grounding the program. In this paper, we identify some classes of PASTA programs for which we apply lifted inference and develop compact formulas to compute the probability bounds of a query without the need to generate all the possible answer sets.

DendryScope: Narrative Designer Support via Symbolic Analysis

Quality-based narratives (QBN) are hypertexts with extensive implicit linked structure. The observation of one passage can have non-obvious long-range implications for the reachability of other passages, which poses an authoring challenge. To help narrative designers address this issue, we produced an interface which visually summarizes all possible playtraces. Our interface leverages answer set programming to produce a query language over possible playtraces, allowing narrative designers to drill down to interesting scenarios. We introduce this interface through the DendryScope tool, which accepts most QBNs written in the Dendry language. We evaluated DendryScope by interviewing four narrative designers as they used the tool to explore Bee, a notable QBN written by Emily Short.

Metric dynamic equilibrium logic

In temporal extensions of Answer Set Programming (ASP) based on linear-time, the behaviour of dynamic systems is captured by sequences of states. While this representation reflects their relative order, it abstracts away the specific times associated with each state. In many applications, however, timing constraints are important like, for instance, when planning and scheduling go hand in hand. We address this by developing a metric extension of linear-time Dynamic Equilibrium Logic, in which dynamic operators are constrained by intervals over integers. The resulting Metric Dynamic Equilibrium Logic provides the foundation of an ASP-based approach for specifying qualitative and quantitative dynamic constraints. As such, it constitutes the most general among a whole spectrum of temporal extensions of Equilibrium Logic. In detail, we show that it encompasses Temporal, Dynamic, Metric and regular Equilibrium Logic, as well as its classic counterparts once the law of the excluded middle is added.

A general framework for preferences in answer set programming

We introduce a general, flexible, and extensible framework for quantitative and qualitative preferences among the stable models of logic programs. Since it is straightforward to capture propositional theories and constraint satisfaction problems with logic programs, our approach is also relevant to optimization in satisfiability testing and constraint processing. We show how complex preference relations can be specified through user-defined preference types and their arguments. We describe how preference specifications are handled internally by so-called preference programs, which are used for dominance testing. We also provide algorithms for computing one, or all, preferred stable models of a logic program, and study the complexity of these problems. We implemented our approach in the asprin system by means of multi-shot answer set solving technology. We demonstrate the generality and flexibility of our methodology by showing how easily existing preference languages can be implemented in asprin. Finally, we empirically evaluate our contributions and contrast them with dedicated implementations.

Response planning during question-answering: does deciding what to say involve deciding how to say it?

To answer a question, speakers must determine their response and formulate it in words. But do they decide on a response before formulation, or do they formulate different potential answers before selecting one? We addressed this issue in a verbal question-answering experiment. Participants answered questions more quickly when they had one potential answer (e.g., Which tourist attraction in Paris is very tall?) than when they had multiple potential answers (e.g., What is the name of a Shakespeare play?). Participants also answered more quickly when the set of potential answers were on average short rather than long, regardless of whether there was only one or multiple potential answers. Thus, participants were not affected by the linguistic complexity of unselected but plausible answers. These findings suggest that participants select a single answer before formulation.

Forming We-intentions under breakdown situations in human-robot interactions

Background and Objective: When agents (e.g. a person and a social robot) perform a joint activity to achieve a joint goal, they require sharing a relevant group intention, which has been defined as a We-intention. In forming We-intentions, breakdown situations due to conflicts between internal and “external” intentions are unavoidable, particularly in healthcare scenarios. To study such We-intention formation and “reparation” of conflicts, this paper has a two-fold objective: introduce a general computational mechanism allowing We-intention formation and reparation in interactions between a social robot and a person; and exemplify how the formal framework can be applied to facilitate interaction between a person and a social robot for healthcare scenarios.Method: The formal computational framework for managing We-intentions was defined in terms of Answer set programming and a Belief-Desire-Intention control loop. We exemplify the formal framework based on earlier theory-based user studies consisting of human-robot dialogue scenarios conducted in a Wizard of Oz setup, video-recorded and evaluated with 20 participants. Data was collected through semi-structured interviews, which were analyzed qualitatively using thematic analysis. N=20 participants (women n=12, men=8, age range 23-72) were part of the study. Two age groups were established for the analysis: younger participants (ages 23-40) and older participants (ages 41-72).Results: We proved four theoretical propositions, which are well-desired characteristics of any rational social robot. In our study, most participants suggested that people were the cause of breakdown situations. Over half of the young participants perceived the social robot's avoidant behavior in the scenarios.Conclusions: This work covered in depth the challenge of aligning the intentions of two agents (for example, in a person-robot interaction) when they try to achieve a joint goal. Our framework provides a novel formalization of the We-intentions theory from social science. The framework is supported by formal properties proving that our computational mechanism generates consistent potential plans. At the same time, the agent can handle incomplete and inconsistent intentions shared by another agent (for example, a person). Finally, our qualitative results suggested that this approach could provide an acceptable level of action/intention agreement generation and reparation from a person-centric perspective.

Management of anterior cruciate ligament revision in adults: the 2022 ESSKA consensus: part II—surgical strategy

PurposeThe aim of this ESSKA consensus is to give recommendations based on scientific evidence and expert opinion to improve the diagnosis, preoperative planning, indication and surgical strategy in Anterior Cruciate Ligament revision.MethodsPart 2, presented herein, followed exactly the same methodology as Part 1: the so-called ESSKA formal consensus derived from the Delphi method. Eighteen questions were ultimately asked. The quality of the answers received the following grades of recommendation: Grade A (high level scientific support), Grade B (scientific presumption), Grade C (low level scientific support) or Grade D (expert opinion). All answers were scored from 1 to 9 by the raters. Once a general consensus had been reached between the steering and rating groups, the question–answer sets were submitted to the peer-review group. A final combined meeting of all the members of the consensus was then held to ratify the document.ResultsThe review of the literature revealed a rather low scientific quality of studies examining the surgical strategy in cases of ACL reconstruction failure. Of the 18 questions, only 1 received a Grade A rating; 5, a Grade B rating; and 9, grades of C or D. The three remaining complex questions received further evaluations for each portion of the question and were looked at in more detail for the following grades: B and D; A, C and D; or A, B, C and D. The mean rating of all questions by the rating group was 8.0 + − 1.1. The questions and recommendations are listed in the article.ConclusionACL revision surgery, especially the surgical strategy, is a widely debated subject with many different opinions and techniques. The literature reveals a poor level of standardization. Therefore, this international European consensus project is of great importance and clinical relevance for guiding the management of ACL revision in adults.Level of evidenceLevel II.

Explanations for Answer Set Programming

Explanations for Answer Set Programming

Reliable Natural Language Understanding with Large Language Models and Answer Set Programming

Reliable Natural Language Understanding with Large Language Models and Answer Set Programming

Predicting weighted unobserved nodes in a regulatory network using answer set programming

BackgroundThe impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network. Previously proposed methods that study this problem fail to deal with a real-size regulatory network, computing predictions sensitive to perturbation and quantifying the predicted species behaviour more finely.ResultsTo address previously mentioned limitations, we develop a new method based on Answer Set Programming, MajS. It takes a regulatory network and a discrete partial set of observations as input. MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool.ConclusionsMajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species. It provides fine-grained discrete predictions by outputting the weight of the predicted sign as a piece of additional information. MajS’ output, thanks to its weight, could easily be integrated with metabolic network modelling.