As artificial intelligence (AI) systems advance, we move towards broad AI: systems capable of performing well on diverse tasks, understanding context, and adapting rapidly to new scenarios. A central challenge for broad AI systems is to generalize over tasks in related domains and being robust to distribution shifts. Neuro-symbolic (NeSy) AI bridges the gap between symbolic and sub-symbolic paradigms to address these challenges, enabling adaptable, generalizable, and more interpretable systems. The development of broad AI requires advancements in domain adaptation (DA), enabling models trained on source domains to effectively generalize to unseen target domains. Traditional approaches often rely on parameter optimization and fine-tuning, which can be impractical due to high costs and risks of catastrophic forgetting. NeSy AI systems use multiple models and methods to generalize to unseen domains and maintain performance across varying conditions. We analyze common DA and NeSy approaches with a focus on deep domain-invariant learning, extending to real-world challenges such as adapting to continuously changing domains and handling large domain gaps. We showcase state-of-the-art model-selection methods for scenarios with limited samples and introduce domain-specific adaptations without gradient-based updates for cases where model tuning is infeasible. This work establishes a framework for scalable and generalizable broad AI systems applicable across various problem settings, demonstrating how symbolic reasoning and large language models can build universal computational graphs that generalize across domains and problems, contributing to more adaptable AI approaches for real-world applications.

Summary: This paper provides a novel program synthesis approach by inferring the intermediate states: execution trace. Unlike conventional methods that treat program synthesis as an end-to-end task based on input/output (i/o) examples, in this manuscript, the authors attempt to infer the execution trace from i/o examples, and further synthesize the program given the i/o and execution trace. The motivation is clear and straightforward since generating programs from execution traces should be easier. Experiments on Karel program synthesis task show the effectiveness of the proposed approach. The research question is significant and very hard.

The paper presents an approach to neural guided search for program synthesis of LISP programs. A modified miniKanren constraint solver is used to synthesize a program from example IO pairs by "inverting" the EVAL function. The search for candidate programs in the solver is guided by a neural network (either a GNN or an RNN with other layers on top). The approach is compared to baselines and three symbolic methods. Pros: - The paper is very well written and definitely relevant to NIPS - The evaluation against symbolic methods is reasonable - While the idea of scoring evaluation expansions is not novel, applying it to the EVAL function is Cons: - Novelty is somewhat limited - Links to some recent related work are missing - No comparison to other neural methods In general, I think that paper is clear and sound enough.

The paper presents a modification of a previous baseline method in the application area of inductive program synthesis (IPS). This new architecture, PCCoder, shows some considerable improvement over DeepCoder, the baseline main method it compared against. Within the setting of IPS, the goal of the model is, provided with some input/output examples meant to represent a partial program specification, output a program that successfully maps from input to output for each I/O example in the set. A program is considered correct if it accomplishes the mapping on all provided I/O pairs (there is no notion of generalization to unseen I/O pairs as sometimes done in previous work, especially in string processing domains). The input/output pairs consist of integers, and the program is constructed from the relatively expressive Domain Specific Language defined in the DeepCoder paper.

Shielding is a common method used to guarantee the safety of a system under a black-box controller, such as a neural network controller from deep reinforcement learning (DRL), with simpler, verified controllers. Existing shielding methods rely on formal verification through Markov Decision Processes (MDPs), assuming either known or finite-state models, which limits their applicability to DRL settings with unknown, continuous-state systems. This paper addresses these limitations by proposing a data-driven shielding methodology that guarantees safety for unknown systems under black-box controllers. The approach leverages Deep Kernel Learning to model the systems' one-step evolution with uncertainty quantification and constructs a finite-state abstraction as an Interval MDP (IMDP). By focusing on safety properties expressed in safe linear temporal logic (safe LTL), we develop an algorithm that computes the maximally permissive set of safe policies on the IMDP, ensuring avoidance of unsafe states. The algorithms soundness and computational complexity are demonstrated through theoretical proofs and experiments on nonlinear systems, including a high-dimensional autonomous spacecraft scenario.

The ability to think abstractly and reason by analogy is a prerequisite to rapidly adapt to new conditions, tackle newly encountered problems by decomposing them, and synthesize knowledge to solve problems comprehensively. We present TransCoder, a method for solving abstract problems based on neural program synthesis, and conduct a comprehensive analysis of decisions made by the generative module of the proposed architecture. At the core of TransCoder is a typed domain-specific language, designed to facilitate feature engineering and abstract reasoning. In training, we use the programs that failed to solve tasks to generate new tasks and gather them in a synthetic dataset. As each synthetic task created in this way has a known associated program (solution), the model is trained on them in supervised mode. Solutions are represented in a transparent programmatic form, which can be inspected and verified. We demonstrate TransCoder's performance using the Abstract Reasoning Corpus dataset, for which our framework generates tens of thousands of synthetic problems with corresponding solutions and facilitates systematic progress in learning.

The concept of code-driven law, i.e. of "legal norms or policies that have been articulated in computer code" by some actors with normative competence, has been convincingly elaborated by Hildebrandt [1]. Its introduction has the merit to refocus the discussion on the role of artificial devices in the legal activity, rather than on ontological positions expressed under code-is-law or law-is-code banners, which are present, with various interpretations and changing fortunes, in the literature and practice of contemporary regulatory technologies, and technology-oriented legal scholarship (see the overview in [2]). According to Hildebrandt, code-driven law should be distinguished from data-driven law, i.e. computational decision-making derived from statistical or other inductive methods, and from text-driven law, i.e. the legal activity performed by humans by means of sources of norms such as statutory and case law. A crucial difference between these forms of "law" is that the linguistic artifacts used in text-driven law are characterized by open-textured concepts (e.g.

Formal Concept Analysis (FCA) is an approach to creating a conceptual hierarchy in which a \textit{concept lattice} is generated from a \textit{formal context}. That is, a triple consisting of a set of objects, $G$, a set of attributes, $M$, and an incidence relation $I$ on $G \times M$. A \textit{concept} is then modelled as a pair consisting of a set of objects (the \textit{extent}), and a set of shared attributes (the \textit{intent}). Implications in FCA describe how one set of attributes follows from another. The semantics of these implications closely resemble that of logical consequence in classical logic. In that sense, it describes a monotonic conditional. The contributions of this paper are two-fold. First, we introduce a non-monotonic conditional between sets of attributes, which assumes a preference over the set of objects. We show that this conditional gives rise to a consequence relation that is consistent with the postulates for non-monotonicty proposed by Kraus, Lehmann, and Magidor (commonly referred to as the KLM postulates). We argue that our contribution establishes a strong characterisation of non-monotonicity in FCA. Typical concepts represent concepts where the intent aligns with expectations from the extent, allowing for an exception-tolerant view of concepts. To this end, we show that the set of all typical concepts is a meet semi-lattice of the original concept lattice. This notion of typical concepts is a further introduction of KLM-style typicality into FCA, and is foundational towards developing an algebraic structure representing a concept lattice of prototypical concepts.

The KLM approach to defeasible reasoning introduces a weakened form of implication into classical logic. This allows one to incorporate exceptions to general rules into a logical system, and for old conclusions to be withdrawn upon learning new contradictory information. Standpoint logics are a group of logics, introduced to the field of Knowledge Representation in the last 5 years, which allow for multiple viewpoints to be integrated into the same ontology, even when certain viewpoints may hold contradicting beliefs. In this paper, we aim to integrate standpoints into KLM propositional logic in a restricted setting. We introduce the logical system of Defeasible Restricted Standpoint Logic (DRSL) and define its syntax and semantics. Specifically, we integrate ranked interpretations and standpoint structures, which provide the semantics for propositional KLM and propositional standpoint logic respectively, in order to introduce ranked standpoint structures for DRSL. Moreover, we extend the non-monotonic entailment relation of rational closure from the propositional KLM case to the DRSL case. The main contribution of this paper is to characterize rational closure for DRSL both algorithmically and semantically, showing that rational closure can be characterized through a single representative ranked standpoint structure. Finally, we conclude that the semantic and algorithmic characterizations of rational closure are equivalent, and that entailment-checking for DRSL under rational closure is in the same complexity class as entailment-checking for propositional KLM.

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