arXiv subjects

Gopal Gupta

Publications and source records attributed to Gopal Gupta.

At least 19 recordsLinked to original sources

arXiv2026

An Approach to the Abstract Interpretation of Goal-Directed Answer Set Programming

Abstract Interpretation infers and verifies program properties by over-approximating program semantics. It has been highly successful for (Constraint) Logic Programming, enabling the analysis of determinism, types, aliasing, and resource usage, as well as application in verification and program optimization. However, Abstract Interpretation has not yet been studied in the context of Goal Directed Answer Set Programming (ASP). In this work, we take a first step in this direction. We present a top-down algorithm based on the PLAI fixpoint, implemented in the abstract interpreter of the Ciao Prolog Preprocessor, to perform abstract interpretation of goal-directed ASP. We also introduce the Shared-Constraints abstract domain, designed to capture potential relations among variables induced by constraints. Finally, we study the practicality of the approach in s(CASP) through three applications: detection of false odd loops over negation, efficient forall evaluation enabled by the Shared-Constraints domain, and abstract specialization (including the simplification of required global constraints). Our results show that compile-time static analysis can improve the evaluation of goal-directed ASP programs.

cs.LO↗

arXiv2026

Complex Autonomous UAV Task Execution and Decision-Making With s(CASP)

Autonomous unmanned aerial vehicles (UAVs) must operate safely in dynamic environments and adapt to changing mission conditions. Although deep learning approaches have shown strong performance for navigation and perception, they are often difficult to explain, verify, and modify for safety-critical tasks. We propose a symbolic state-centered UAV agent using the s(CASP) answer set programming system, enabling autonomous task execution with constraint-based commonsense reasoning in a high-fidelity Unreal Engine 5 environment. We fully implement prior work on the VECSR-A system to support multi-step autonomous behaviors including navigation, search, debris detection, precision spraying, object transport, and inspection. The UAV reasons over environmental and spatial constraints, dynamically revising plans when tasks fail or data is insufficient. Because decisions are based on commonsense reasoning, they are guaranteed to be correct and explainable. We evaluate the feasibility of s(CASP) for UAV control in realistic simulated missions. Results show that our framework enables explainable, adaptive autonomy without retraining, handling complex constraint-aware decisions and dynamic task reevaluation.

cs.LO↗

arXiv2026

Event Calculus Meets Hybrid ASP

Event Calculus (EC) implemented in answer set programming (ASP) has proven suitable for specifying requirements on safety-critical systems thanks to its elegant representation of both discrete and continuous changes and its semantic closeness to semi-formal natural language. However, continuous changes and the size of value domains of time and system properties (fluents) pose significant challenges. Grounding-based ASP solvers, e.g., clingo, which implement Discrete EC (DEC), lead to combinatorial explosion in program size and inaccurate representation. The grounding-free s(CASP) does not discretize but struggles with non-termination due to its top-down execution. This paper introduces Hybrid EC, an extended axiomatization of DEC, that tackles the challenges via functional fluents and a mapping of time to abstract steps. We implement it using clingcon and clingo-lpx (Hybrid ASP systems over integers and rationals, respectively) where the value (dense) domains of fluents and time are represented as linear constraints and evaluated by external solvers, while ensuring termination whenever solutions exist. We validate both implementations on a number of examples and observe that they are unaffected by the size of the domains and that handling rationals does not impact scalability. Most importantly, the ability of clingo-lpx to handle dense domains enables accurate modeling of continuous change.

cs.LO↗

arXiv2026

Correcting Autonomous Driving Object Detection Misclassifications with Automated Commonsense Reasoning

Autonomous Vehicle (AV) technology has been heavily researched and sought after, yet there are no SAE Level 5 AVs available today in the marketplace. We contend that over-reliance on machine learning technology is the main reason. Use of automated commonsense reasoning technology, we believe, can help achieve SAE Level 5 autonomy. In this paper, we show how automated common-sense reasoning technology can be deployed in situations where there are not enough data samples available to train a deep learning-based AV model that can handle certain abnormal road scenarios. Specifically, we consider two situations where (i) a traffic signal is malfunctioning at an intersection and (ii) all the cars ahead are slowing down and steering away due to an unexpected obstruction (e.g., animals on the road). We show that in such situations, our commonsense reasoning-based solution accurately detects traffic light colors and obstacles not correctly captured by the AV's perception model. We also provide a pathway for efficiently invoking commonsense reasoning by measuring uncertainty in the computer vision model and using commonsense reasoning to handle uncertain scenarios. We describe our experiments conducted using the CARLA simulator and the results obtained. The main contribution of our research is to show that automated commonsense reasoning effectively corrects AV-based object detection misclassifications and that hybrid models provide an effective pathway to improving AV perception.

cs.AI↗

arXiv2026

On Zeno-like Behaviors in the Event Calculus with Goal-directed Answer Set Programming

It has been argued that Event Calculus (EC) is suitable for modeling high-level specifications of safety-critical cyber-physical systems. The primary advantage lies in the rather small semantic gap between EC models and requirements expressed in a semi-formal natural language. Moreover, its use of continuous time and variables avoids imprecision that stems from discretization. In the past, we have shown that a goal-directed ASP system can be used for implementing these EC models. However, precise representation of time as an infinitesimally divisible continuous quantity leads to Zeno-like behaviors and to non-termination in such a system. In this work, we model a number of well-known example problems from the literature to systematically study various natural EC modeling patterns that yield these Zeno-like behaviors, and propose ways to deal with them. Moreover, we also propose a technique to automatically detect all such cases.

cs.LO↗

arXiv2025

Computing Supported Models via Transformation to Stable Models

Answer Set Programming (ASP) with stable model semantics has proven highly effective for knowledge representation and reasoning. However, the minimality requirement of stable models can be restrictive for applications requiring exploration of non-minimal but logically consistent solution spaces. Supported models, introduced by Apt, Blair, and Walker in 1988, relax this minimality constraint while maintaining a support condition ensuring every true atom is justified by some rule. Despite their theoretical significance, supported models lack practical computational tools integrated with modern ASP solvers. We present a novel transformation-based method enabling computation of supported models using standard ASP infrastructure. Our approach transforms any ground logic program into an equivalent program whose stable models correspond exactly to the supported models of the original program. We implement this transformation for Clingo, providing the first practical tool for computing supported models with state-of-the-art ASP solvers. We demonstrate applications in software verification, medical diagnosis, and planning where supported models enable valuable exploratory reasoning capabilities beyond those provided by stable models. We also provide an empirical evaluation to justify the practical utility of our approach compared to established methods. Our implementation is publicly available and compatible with standard ASP syntax.

cs.LO↗

arXiv2025

Logic-Driven Cybersecurity: A Novel Framework for System Log Anomaly Detection using Answer Set Programming

This study explores the application of Answer Set Programming (ASP) for detecting anomalies in system logs, addressing the challenges posed by evolving cyber threats. We propose a novel framework that leverages ASP's declarative nature and logical reasoning capabilities to encode complex security rules as logical predicates. Our ASP-based system was applied to a real-world Linux system log dataset, demonstrating its effectiveness in identifying various anomalies such as potential brute-force attacks, privilege escalations, frequent network connections from specific IPs, and various system-level issues. Key findings highlight ASP's strengths in handling structured log data, rule flexibility, and event correlation. The approach shows promise in providing explainable alerts from real-world data. This research contributes to computer forensics by demonstrating a logic-based paradigm for log analysis on a practical dataset, opening avenues for more nuanced and adaptive cyber intelligence systems.

cs.CR↗

arXiv2025

Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification

Recently, the demand for Machine Learning (ML) models that can balance accuracy, efficiency, and interpreability has grown significantly. Traditionally, there has been a tradeoff between accuracy and explainability in predictive models, with models such as Neural Networks achieving high accuracy on complex datasets while sacrificing internal transparency. As such, new rule-based algorithms such as FOLD-SE have been developed that provide tangible justification for predictions in the form of interpretable rule sets. The primary objective of this study was to compare FOLD-SE and FOLD-R++, both rule-based classifiers, in binary classification and evaluate how FOLD-SE performs against XGBoost, a widely used ensemble classifier, when applied to multi-category classification. We hypothesized that because FOLD-SE can generate a condensed rule set in a more explainable manner, it would lose upwards of an average of 3 percent in accuracy and F1 score when compared with XGBoost and FOLD-R++ in multiclass and binary classification, respectively. The research used data collections for classification, with accuracy, F1 scores, and processing time as the primary performance measures. Outcomes show that FOLD-SE is superior to FOLD-R++ in terms of binary classification by offering fewer rules but losing a minor percentage of accuracy and efficiency in processing time; in tasks that involve multi-category classifications, FOLD-SE is more precise and far more efficient compared to XGBoost, in addition to generating a comprehensible rule set. The results point out that FOLD-SE is a better choice for both binary tasks and classifications with multiple categories. Therefore, these results demonstrate that rule-based approaches like FOLD-SE can bridge the gap between explainability and performance, highlighting their potential as viable alternatives to black-box models in diverse classification tasks.

cs.LG↗

arXiv2025

P2C: Path to Counterfactuals

Machine-learning models are increasingly driving decisions in high-stakes settings, such as finance, law, and hiring, thus, highlighting the need for transparency. However, the key challenge is to balance transparency -- clarifying `why' a decision was made -- with recourse: providing actionable steps on `how' to achieve a favourable outcome from an unfavourable outcome. Counterfactual explanations reveal `why' an undesired outcome occurred and `how' to reverse it through targeted feature changes (interventions). Current counterfactual approaches have limitations: 1) they often ignore causal dependencies between features, and 2) they typically assume all interventions can happen simultaneously, an unrealistic assumption in practical scenarios where actions are typically taken in a sequence. As a result, these counterfactuals are often not achievable in the real world. We present P2C (Path-to-Counterfactuals), a model-agnostic framework that produces a plan (ordered sequence of actions) converting an unfavourable outcome to a causally consistent favourable outcome. P2C addresses both limitations by 1) Explicitly modelling causal relationships between features and 2) Ensuring that each intermediate state in the plan is feasible and causally valid. P2C uses the goal-directed Answer Set Programming system s(CASP) to generate the plan accounting for feature changes that happen automatically due to causal dependencies. Furthermore, P2C refines cost (effort) computation by only counting changes actively made by the user, resulting in realistic cost estimates. Finally, P2C highlights how its causal planner outperforms standard planners, which lack causal knowledge and thus can generate illegal actions.

cs.AI↗

arXiv2025

MC3G: Model Agnostic Causally Constrained Counterfactual Generation

Machine learning models increasingly influence decisions in high-stakes settings such as finance, law and hiring, driving the need for transparent, interpretable outcomes. However, while explainable approaches can help understand the decisions being made, they may inadvertently reveal the underlying proprietary algorithm: an undesirable outcome for many practitioners. Consequently, it is crucial to balance meaningful transparency with a form of recourse that clarifies why a decision was made and offers actionable steps following which a favorable outcome can be obtained. Counterfactual explanations offer a powerful mechanism to address this need by showing how specific input changes lead to a more favorable prediction. We propose Model-Agnostic Causally Constrained Counterfactual Generation (MC3G), a novel framework that tackles limitations in the existing counterfactual methods. First, MC3G is model-agnostic: it approximates any black-box model using an explainable rule-based surrogate model. Second, this surrogate is used to generate counterfactuals that produce a favourable outcome for the original underlying black box model. Third, MC3G refines cost computation by excluding the ``effort" associated with feature changes that occur automatically due to causal dependencies. By focusing only on user-initiated changes, MC3G provides a more realistic and fair representation of the effort needed to achieve a favourable outcome. We show that MC3G delivers more interpretable and actionable counterfactual recommendations compared to existing techniques all while having a lower cost. Our findings highlight MC3G's potential to enhance transparency, accountability, and practical utility in decision-making processes that incorporate machine-learning approaches.

cs.AI↗

arXiv2025

Modeling Deontic Modal Logic in ASP

We consider the problem of implementing deontic modal logic. We show how (deontic) modal operators can be elegantly and directly expressed using default negation (negation-as-failure) and strong negation present in answer set programming (ASP). We propose using global constraints of ASP to represent obligations, prohibitions, and permissions in deontic modal logic. We show that our proposed representation results in the various decades-old paradoxes of deontic modal logic being simply and elegantly resolved. Our method also serves as a means for modeling conditional obligations and conditional prohibitions in knowledge representation.

cs.AI↗

arXiv2025

A Community-driven vision for a new Knowledge Resource for AI

The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge resources like WordNet, ConceptNet, Wolfram|Alpha and other commercial knowledge graphs, verifiable, general-purpose widely available sources of knowledge remain a critical deficiency in AI infrastructure. Large language models struggle due to knowledge gaps; robotic planning lacks necessary world knowledge; and the detection of factually false information relies heavily on human expertise. What kind of knowledge resource is most needed in AI today? How can modern technology shape its development and evaluation? A recent AAAI workshop gathered over 50 researchers to explore these questions. This paper synthesizes our findings and outlines a community-driven vision for a new knowledge infrastructure. In addition to leveraging contemporary advances in knowledge representation and reasoning, one promising idea is to build an open engineering framework to exploit knowledge modules effectively within the context of practical applications. Such a framework should include sets of conventions and social structures that are adopted by contributors.

cs.AI↗

arXiv2025

Building Trustworthy AI by Addressing its 16+2 Desiderata with Goal-Directed Commonsense Reasoning

Current advances in AI and its applicability have highlighted the need to ensure its trustworthiness for legal, ethical, and even commercial reasons. Sub-symbolic machine learning algorithms, such as the LLMs, simulate reasoning but hallucinate and their decisions cannot be explained or audited (crucial aspects for trustworthiness). On the other hand, rule-based reasoners, such as Cyc, are able to provide the chain of reasoning steps but are complex and use a large number of reasoners. We propose a middle ground using s(CASP), a goal-directed constraint-based answer set programming reasoner that employs a small number of mechanisms to emulate reliable and explainable human-style commonsense reasoning. In this paper, we explain how s(CASP) supports the 16 desiderata for trustworthy AI introduced by Doug Lenat and Gary Marcus (2023), and two additional ones: inconsistency detection and the assumption of alternative worlds. To illustrate the feasibility and synergies of s(CASP), we present a range of diverse applications, including a conversational chatbot and a virtually embodied reasoner.

cs.AI↗

arXiv2025

Symbolic Rule Extraction from Attention-Guided Sparse Representations in Vision Transformers

Recent neuro-symbolic approaches have successfully extracted symbolic rule-sets from CNN-based models to enhance interpretability. However, applying similar techniques to Vision Transformers (ViTs) remains challenging due to their lack of modular concept detectors and reliance on global self-attention mechanisms. We propose a framework for symbolic rule extraction from ViTs by introducing a sparse concept layer inspired by Sparse Autoencoders (SAEs). This linear layer operates on attention-weighted patch representations and learns a disentangled, binarized representation in which individual neurons activate for high-level visual concepts. To encourage interpretability, we apply a combination of L1 sparsity, entropy minimization, and supervised contrastive loss. These binarized concept activations are used as input to the FOLD-SE-M algorithm, which generates a rule-set in the form of logic programs. Our method achieves a 5.14% better classification accuracy than the standard ViT while enabling symbolic reasoning. Crucially, the extracted rule-set is not merely post-hoc but acts as a logic-based decision layer that operates directly on the sparse concept representations. The resulting programs are concise and semantically meaningful. This work is the first to extract executable logic programs from ViTs using sparse symbolic representations. It bridges the gap between transformer-based vision models and symbolic logic programming, providing a step forward in interpretable and verifiable neuro-symbolic AI.

cs.CV↗

arXiv2025

Reliable Collaborative Conversational Agent System Based on LLMs and Answer Set Programming

As the Large-Language-Model-driven (LLM-driven) Artificial Intelligence (AI) bots became popular, people realized their strong potential in Task-Oriented Dialogue (TOD). However, bots relying wholly on LLMs are unreliable in their knowledge, and whether they can finally produce a correct outcome for the task is not guaranteed. The collaboration among these agents also remains a challenge, since the necessary information to convey is unclear, and the information transfer is by prompts: unreliable, and malicious knowledge is easy to inject. With the help of knowledge representation and reasoning tools such as Answer Set Programming (ASP), conversational agents can be built safely and reliably, and communication among the agents made more reliable as well. We propose a Manager-Customer-Service Dual-Agent paradigm, where ASP-driven bots share the same knowledge base and complete their assigned tasks independently. The agents communicate with each other through the knowledge base, ensuring consistency. The knowledge and information conveyed are encapsulated and invisible to the users, ensuring the security of information transmission. To illustrate the dual-agent conversational paradigm, we have constructed AutoManager, a collaboration system for managing the drive-through window of a fast-food restaurant such as Taco Bell in the US. In AutoManager, the customer service bot takes the customer's order while the manager bot manages the menu and food supply. We evaluated our AutoManager system and compared it with the real-world Taco Bell Drive-Thru AI Order Taker, and the results show that our method is more reliable.

cs.AI↗

arXiv2025

VECSR: Virtually Embodied Common Sense Reasoning System

The development of autonomous agents has seen a revival of enthusiasm due to the emergence of LLMs, such as GPT-4o. Deploying these agents in environments where they coexist with humans (e.g., as domestic assistants) requires special attention to trustworthiness and explainability. However, the use of LLMs and other deep learning models still does not resolve these key issues. Deep learning systems may hallucinate, be unable to justify their decisions as black boxes, or perform badly on unseen scenarios. In this work, we propose the use of s(CASP), a goal-directed common sense reasoner based on Answer Set Programming, to break down the high-level tasks of an autonomous agent into mid-level instructions while justifying the selection of these instructions. To validate its use in real applications we present a framework that integrates the reasoner into the VirtualHome simulator and compares its accuracy with GPT-4o, running some of the "real" use cases available in the domestic environments of VirtualHome. Additionally, since experiments with VirtualHome have shown the need to reduce the response time (which increases as the agent's decision space grows), we have proposed and evaluated a series of optimizations based on program analysis that exploit the advantages of the top-down execution of s(CASP).

cs.LO↗

arXiv2025

Proceedings 40th International Conference on Logic Programming

Since the first conference In Marseille in 1982, the International Conference on Logic Programming (ICLP) has been the premier international event for presenting research in logic programming. These proceedings include technical communications about, and abstracts for presentations given at the 40th ICLP held October 14-17, in Dallas Texas, USA. The papers and abstracts in this volume include the following areas and topics. Formal and operational semantics: including non-monotonic reasoning, probabilistic reasoning, argumentation, and semantic issues of combining logic with neural models. Language design and programming methodologies such as answer set programming. inductive logic programming, and probabilistic programming. Program analysis and logic-based validation of generated programs. Implementation methodologies including constraint implementation, tabling, Logic-based prompt engineering, and the interaction of logic programming with LLMs.

cs.LO↗

arXiv2025

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters

There has been significant focus on creating neuro-symbolic models for interpretable image classification using Convolutional Neural Networks (CNNs). These methods aim to replace the CNN with a neuro-symbolic model consisting of the CNN, which is used as a feature extractor, and an interpretable rule-set extracted from the CNN itself. While these approaches provide interpretability through the extracted rule-set, they often compromise accuracy compared to the original CNN model. In this paper, we identify the root cause of this accuracy loss as the post-training binarization of filter activations to extract the rule-set. To address this, we propose a novel sparsity loss function that enables class-specific filter binarization during CNN training, thus minimizing information loss when extracting the rule-set. We evaluate several training strategies with our novel sparsity loss, analyzing their effectiveness and providing guidance on their appropriate use. Notably, we set a new benchmark, achieving a 9% improvement in accuracy and a 53% reduction in rule-set size on average, compared to the previous SOTA, while coming within 3% of the original CNN's accuracy. This highlights the significant potential of interpretable neuro-symbolic models as viable alternatives to black-box CNNs.

cs.CV↗