Pathological images hold significant importance for disease diagnosis. When employing artificial intelligence deep learning for pathological image segmentation, numerous challenges arise. For example, difficulties in data collection and annotation, substantial individual variations affecting training, and the lack of interpretability in model decisions, which leads to low levels of physician trust. This study proposes the MSDAUnet segmentation method. Firstly, an image denoising and enhancement module composed of a Dynamic Residual Attention Network (DRAN) and an image enhancement method based on Improved Color Histogram Equalization (MCHE) is constructed to remove image noise and improve image clarity. Then, a Dual Attention Module (DAM) is proposed, which connects the Squeeze-andExcitation Block (SE Block) and the Spatial Attention Module (SAM) in series to extract information from both the channel and spatial dimensions and obtain key features. Finally, the Atrous Spatial Pyramid Pooling (ASPP) module is introduced to obtain multi-scale information.The combination of these modules is used for automatic pathological image segmentation. Compared with the traditional and typical UNet model, the segmentation effect of MSDAUNet is more excellent. On the Monash University dataset, its IoU index reaches$\mathbf{7 2. 7 \%}$, nearly$\mathbf{7 \%}$higher than UNet, and the DSC index is 84.9 %, also about 7 % higher than UNet. It vigorously promotes the development of medical imaging processing technology and provides more accurate AI assistance for medical decision-making.

Explanation plays an important role in the decisions of both symbolic and neural network-based AI systems. Logic programs under answer set semantics (ASP) have been a typical declarative reasoning and problem-solving paradigm that has extensive applications in various AI domains. In this paper, we consider the issue of explanation for logic programs with abstract constraint atoms (c-atoms) under SPT-answer set semantics. Such c-atoms are general enough to capture complex constructors of logic programs, including aggregates, and the SPT-answer sets exclude circular justifications that other semantics have. We propose a minimal reduct for logic programs with c-atoms that yields a new semantic characterization of SPT-answer sets, and then introduce an extension of resolution for clauses with c-atoms. As we show, every atom in an SPT-answer set enjoys an extended resolution proof from the minimal reduct of its logic program. Finally, we present minimal sufficient subsets of logic programs (witnesses) to structure such an extended resolution proof for an atom in an SPT-answer set. Our results contribute to the justification of answer sets and provide a basis for explainability of ASP-based applications.

Compositional zero-shot learning (CZSL) aims to recognize novel compositions of known attributes and objects without requiring additional training data. Recent CZSL methods based on vision-language models(e.g., CLIP) suffer from relying solely on text prompts and neglecting the crucial primitive features within compositions, which limits generalization to unseen compositions. To overcome these limitations, we propose a Multi-modal Prompt and Primitives Enhancement method, termed MPPE, which incorporates two key aspects. First, MPPE introduces both text and visual prompts. The text prompts consist of the composition and its corresponding attribute and object prompts, while the visual prompts leverage image masks generated by the segment anything model (SAM). These masks are integrated via an additional Alpha branch to strengthen the CLIP visual encoder to focus on regions of interest within the image. Second, we design a primitives enhancement (PE) module based on cross-attention, which refines attribute and object features obtained from the CLIP text encoder, thereby enriching the representation of novel composition features. Extensive experiments demonstrate the effectiveness of our approach, achieving state-of-the-art performance on three widely-used CZSL benchmarks in both closed-world and open-world CZSL scenarios. Codes are available at https://github.com/YtJin-git/MPPE.

Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with semantic prototypes. However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process. To address the aforementioned challenges, this article proposes a visual-semantic graph matching net (VSGMN), which leverages semantic relationships among classes to aid in visual-semantic embedding. VSGMN uses a graph build net (GBN) and a graph matching net (GMN) to achieve two-stage visual-semantic alignment. Specifically, GBN first uses an embedding-based approach to build visual and semantic graphs in the semantic space and align the embedding with its prototype for first-stage alignment. In addition, to supplement unseen class relationships in these graphs, GBN also builds the unseen class nodes based on semantic relationships. In the second stage, GMN continuously integrates neighbor and cross-graph information into the constructed graph nodes and aligns the node relationships between the two graphs under the class relationship constraint. Extensive experiments on three benchmark datasets demonstrate that VSGMN achieves superior performance in both conventional and generalized ZSL (GZSL) scenarios. The implementation of our VSGMN and experimental results are available at github: https://github.com/dbwfd/VSGMN.

Abstract Possibilistic logic programs (poss-programs) under stable models are a major variant of answer set programming. While its semantics (possibilistic stable models) and properties have been well investigated, the problem of inductive reasoning has not been investigated yet. This paper presents an approach to extracting poss-programs from a background program and examples (parts of intended possibilistic stable models). To this end, the notion of induction tasks is first formally defined, its properties are investigated and two algorithms ilpsm and ilpsmmin for computing induction solutions are presented. An implementation of ilpsmmin is also provided and experimental results show that when inputs are ordinary logic programs, the prototype outperforms a major inductive learning system for normal logic programs from stable models on the datasets that are randomly generated.

Multicontext systems provide an effective representation and reasoning framework for integrating heterogeneous knowledge obtained from different sources and have been applied in different fields. Because many application fields in real life have to deal with uncertain and fuzzy knowledge, this article aims to combine the multicontext system and fuzzy logic theory effectively and systematically to deal with the representation and reasoning of uncertainty in heterogeneous contexts. The current research in this area is still relatively limited, especially in terms of systematic integration. Specifically, this article proposes a class of heterogeneous nonmonotonic fuzzy multicontext systems based on nonmonotonic multicontext systems, in which an abstract logic is proposed to capture different types of logic and is used as a theoretical basis for fuzzy multicontext knowledge representation and setting up bridging rules to integrate heterogeneous knowledge. Fuzzy equilibria are used to describe the semantics of fuzzy multicontext systems. The syntactic and semantic framework of heterogeneous nonmonotonic fuzzy multicontext systems is then systematically established. Finally, we show that the proposed fuzzy multicontext system not only extends the nonmonotonic multicontext system to fuzzy settings, but also could expand the probabilistic multicontext system and the possibilistic multicontext system in the similar way.

In this article, we consider Answer Set Programming (ASP). It is a declarative problem solving paradigm that can be used to encode a problem as a logic program whose answer sets correspond to the solutions of the problem. It has been widely applied in various domains in AI and beyond. Given that answer sets are supposed to yield solutions to the original problem, the question of “why a set of atoms is an answer set” becomes important for both semantics understanding and program debugging. It has been well investigated for normal logic programs. However, for the class of disjunctive logic programs, which is a substantial extension of that of normal logic programs, this question has not been addressed much. In this article, we propose a notion of reduct for disjunctive logic programs and show how it can provide answers to the aforementioned question. First, we show that for each answer set, its reduct provides a resolution proof for each atom in it. We then further consider minimal sets of rules that will be sufficient to provide resolution proofs for sets of atoms. Such sets of rules will be called witnesses and are the focus of this article. We study complexity issues of computing various witnesses and provide algorithms for computing them. In particular, we show that the problem is tractable for normal and headcycle-free disjunctive logic programs, but intractable for general disjunctive logic programs. We also conducted some experiments and found that for many well-known ASP and SAT benchmarks, computing a minimal witness for an atom of an answer set is often feasible.

Computation Tree Logic (CTL) is one of the central formalisms in formal verification. As a specification language, it is used to express a property that the system at hand is expected to satisfy. From both the verification and the system design points of view, some information content of such property might become irrelevant for the system due to various reasons, e.g., it might become obsolete by time, or perhaps infeasible due to practical difficulties. Then, the problem arises on how to subtract such piece of information without altering the relevant system behaviour or violating the existing specifications over a given signature. Moreover, in such a scenario, two crucial notions are informative: the strongest necessary condition (SNC) and the weakest sufficient condition (WSC) of a given property. To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model and over a given signature. We study its theoretical properties and also show that our notion of forgetting satisfies existing essential postulates of knowledge forgetting. Furthermore, we analyse the computational complexity of some basic reasoning tasks for the fragment CTLAF in particular.

The theory of (variable) forgetting has received significant attention in nonmonotonic reasoning, especially, in answer set programming. However, the problem of establishing a theory of forgetting for some expressive nonmonotonic logics such as McCarthy's circumscription is rarely explored.In this paper a theory of forgetting for propositional circumscription is proposed, which is not a straightforward adaption of existing approaches. In particular, some properties that are essential for existing proposals do not hold any longer or have to be reformulated. Several useful properties of the new forgetting are proved, which demonstrate suitability of the forgetting for circumscription. A sound and complete algorithm for the forgetting is developed and an analysis of computational complexity is given.

The ability of discarding or hiding irrelevant information has been recognized as an important feature for knowledge based systems, including answer set programming. The notion of strong equivalence in answer set programming plays an important role for different problems as it gives rise to a substitution principle and amounts to knowledge equivalence of logic programs. In this paper, we uniformly propose a semantic knowledge forgetting, called HT- and FLP-forgetting, for logic programs under stable model and FLP-stable model semantics, respectively. Our proposed knowledge forgetting discards exactly the knowledge of a logic program which is relevant to forgotten variables. Thus it preserves strong equivalence in the sense that strongly equivalent logic programs will remain strongly equivalent after forgetting the same variables. We show that this semantic forgetting result is always expressible; and we prove a representation theorem stating that the HT- and FLP-forgetting can be precisely characterized by Zhang-Zhou's four forgetting postulates under the HT- and FLP-model semantics, respectively. We also reveal underlying connections between the proposed forgetting and the forgetting of propositional logic, and provide complexity results for decision problems in relation to the forgetting. An application of the proposed forgetting is also considered in a conflict solving scenario.

Logic programs with abstract constraint atoms proposed by Marek and Truszczynski are very general logic programs.They are general enough to captureaggregate logic programs as well asrecently proposed description logic programs.In this paper, we propose a well-founded semantics for basic logic programs with arbitrary abstract constraint atoms, which are sets of rules whose heads have exactly one atom. Weshow that similar to the well-founded semanticsof normal logic programs, it has many desirable properties such as that it can becomputed in polynomial time, and is always correct with respect to theanswer set semantics. This paves the way for using our well-founded semanticsto simplify these logic programs. We also show how our semantics can be applied toaggregate logic programs and description logic programs, and compare itto the well-founded semantics already proposed for these logic programs.

Abstract Description Logic Programs (dl-programs) proposed by Eiter et al. constitute an elegant yet powerful formalism for the integration of answer set programming with description logics, for the Semantic Web. In this paper, we generalize the notions of completion and loop formulas of logic programs to description logic programs and show that the answer sets of a dl-program can be precisely captured by the models of its completion and loop formulas. Furthermore, we propose a new, alternative semantics for dl-programs, called the canonical answer set semantics, which is defined by the models of completion that satisfy what are called canonical loop formulas. A desirable property of canonical answer sets is that they are free of circular justifications. Some properties of canonical answer sets are also explored.