A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Framework development for providing accessibility to qualitative spatial calculi

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Qualitative spatial reasoning deals with knowledge about an infinite spatial domain using a finite set of qualitative relations without using numerical computation. Qualitative knowledge is relative knowledge where we obtain the knowledge on the basis of comparison of features with in the object domain rather then using some external scales. Reasoning is an intellectual facility by which, conclusions are drawn from premises and is present in our everyday interaction with the geographical world. The kind of reasoning that human being relies on is based on commonsense knowledge in everyday situations. During the last decades a multitude of formal calculi over spatial relations have been proposed by focusing on different aspects of space like topology, orientation and distance. Qualitative spatial reasoning engines like SparQ and GQR represents space and reasoning about the space based on qualitative spatial relations and bring qualitative reasoning closer to the geographic applications. Their relations and certain operations defined in qualitative calculi use to infer new knowledge on different aspects of space. Today GIS does not support common-sense reasoning due to limitation for how to formalize spatial inferences. It is important to focus on common sense geographic reasoning, reasoning as it is performed by human. Human perceive and represents geographic information qualitatively, the integration of reasoner with spatial application enables GIS users to represent and extract geographic information qualitatively using human understandable query language. In this thesis, I designed and developed common API framework using platform independent software like XML and JAVA that used to integrate qualitative spatial reasoning engines (SparQ) with GIS application. SparQ is set of modules that structured to provides different reasoning services. SparQ supports command line instructions and it has a specific syntax as set of commands. The developed API provides interface between GIS application and reasoning engine. It establishes connection with reasoner over TCP/IP, takes XML format queries as input from GIS application and converts into SparQ module specific syntax. Similarly it extracts given result, converts it into defined XML format and passes it to GIS application over the same TCP/IP connection. The most challenging part of thesis was SparQ syntax analysis for inputs and their outputs. Each module in Sparq takes module specific query syntax and generates results in multiple syntaxes like; error, simple result and result with comments. Reasoner supports both binary and ternary calculi. The input query syntax for binary-calculi is different for ternary-calculi in the terms of constraint-networks. Based on analysis I, identified commonalities between input query syntaxes for both binary and ternary calculi and designed XML structures for them. Similarly I generalized SparQ results into five major categories and designed XML structures. For ternary-calculi, I considered constraint-reasoning module and their specific operations and designed XML structure for both of their inputs and outputs

Crystal: Introspective Reasoners Reinforced with Self-Feedback

Extensive work has shown that the performance and interpretability of commonsense reasoning can be improved via knowledge-augmented reasoning methods, where the knowledge that underpins the reasoning process is explicitly verbalized and utilized. However, existing implementations, including "chain-of-thought" and its variants, fall short in capturing the introspective nature of knowledge required in commonsense reasoning, and in accounting for the mutual adaptation between the generation and utilization of knowledge. We propose a novel method to develop an introspective commonsense reasoner, Crystal. To tackle commonsense problems, it first introspects for knowledge statements related to the given question, and subsequently makes an informed prediction that is grounded in the previously introspected knowledge. The knowledge introspection and knowledge-grounded reasoning modes of the model are tuned via reinforcement learning to mutually adapt, where the reward derives from the feedback given by the model itself. Experiments show that Crystal significantly outperforms both the standard supervised finetuning and chain-of-thought distilled methods, and enhances the transparency of the commonsense reasoning process. Our work ultimately validates the feasibility and potential of reinforcing a neural model with self-feedback.Comment: EMNLP 2023 main conferenc

Logical Reasoning over Natural Language as Knowledge Representation: A Survey

Logical reasoning is central to human cognition and intelligence. It includes deductive, inductive, and abductive reasoning. Past research of logical reasoning within AI uses formal language as knowledge representation and symbolic reasoners. However, reasoning with formal language has proved challenging (e.g., brittleness and knowledge-acquisition bottleneck). This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning, advantages of the new paradigm, benchmarks and methods, challenges of the new paradigm, possible future directions, and relation to related NLP fields. This new paradigm is promising since it not only alleviates many challenges of formal representation but also has advantages over end-to-end neural methods. This survey focus on transformer-based LLMs explicitly working on deductive, inductive, and abductive reasoning over English representation

Case Frames as Contextual Mappings to Case Law in BestPortal

This paper introduces case frames as a way to provide a more meaningful structure to vocabulary mappings used to bridge the gap between laymen and legal descriptions of court proceedings. Case frames both reduce the ambiguity of queries, and improve the ability of users to formulate good quality queries. We extend the BestMap ontology with a formalisation of case frame based mappings in OWL 2, present a new version of BestPortal, and show how case frames impact retrieval results compared to simple contextual mappings and a direct fulltext search

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents

Human-like chatbots necessitate the use of commonsense reasoning in order to effectively comprehend and respond to implicit information present within conversations. Achieving such coherence and informativeness in responses, however, is a non-trivial task. Even for large language models (LLMs), the task of identifying and aggregating key evidence within a single hop presents a substantial challenge. This complexity arises because such evidence is scattered across multiple turns in a conversation, thus necessitating integration over multiple hops. Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning. To this end, we propose a knowledge distillation framework that leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. We further present DOCTOR, a DialOgue Chain-of-ThOught Reasoner that provides reliable CoT rationales for response generation. We conduct extensive experiments to show that enhancing dialogue agents with high-quality rationales from DOCTOR significantly improves the quality of their responses.Comment: 25 pages, 8 figures, Accepted to EMNLP 202