Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 230-235). Available also in a digital version from Dissertation Abstracts.

Bibliography: p. 121-125. / xiv, 125 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / One of the central issues in Artificial Intelligence (AI) is common sense reasoning. This includes logics of knowledge and belief, non-monotonic reasoning, truth-maintenance and belief revision. Within these fields the notion of a consistent belief state is the crucial one. The issues of inconsistency and partiality of information are central to this thesis which proposes a logical knowledge representation formalism employing partial objects and partial worlds on its semantic side. The syntax includes a language, formulae, and partial theories. Partial worlds and theories are consistent, and contradictory information is assumed to arise in multiple agent situations. Relevant mathematical structures are discussed, in particular partial theories are related to partial worlds. A multiple agent case is considered. Partial theories can be partially ordered by an information ordering and the obtained lattice structure facilitates the theory selection process based on information value and truthness of theories. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998

Bibliography: p. 121-125. / xiv, 125 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / One of the central issues in Artificial Intelligence (AI) is common sense reasoning. This includes logics of knowledge and belief, non-monotonic reasoning, truth-maintenance and belief revision. Within these fields the notion of a consistent belief state is the crucial one. The issues of inconsistency and partiality of information are central to this thesis which proposes a logical knowledge representation formalism employing partial objects and partial worlds on its semantic side. The syntax includes a language, formulae, and partial theories. Partial worlds and theories are consistent, and contradictory information is assumed to arise in multiple agent situations. Relevant mathematical structures are discussed, in particular partial theories are related to partial worlds. A multiple agent case is considered. Partial theories can be partially ordered by an information ordering and the obtained lattice structure facilitates the theory selection process based on information value and truthness of theories. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998

Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998. / Bibliography: p. 121-125.

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (p. 105-112).

Thesis (Ph.D.)--Duquesne University, 2005. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 196-204) and index.

Artificial Intelligence (AI) technologies are increasingly pervading aspects of our lives. Because people use natural language to communicate with each other, computers should also use natural language to communicate with us. One of the principal obstacles to achieving this is the ambiguity of natural language, evidenced in problems such as prepositional phrase attachment and pronoun coreference. Current methods rely on the statistical frequency of word patterns, but this is often brittle and opaque to people. In this thesis, I explore the idea of using commonsense knowledge to resolve linguistic ambiguities. I introduce PatchComm, which invokes explicit commonsense assertions to solve context-independent ambiguities. When commonsense assertions are missing, I invoke RetroGAN-DRD, which leverages state-of-the-art inference techniques such as retrofitting and generative adversarial networks (GAN) to infer commonsense assertions. I build upon that with ProGeneXP, which brings state-of-the-art language models to the task of describing its inputs and implicit knowledge in natural language while providing meaningful descriptions for PatchComm to align to further resolve linguistic ambiguities. Finally, I introduce DialComm to lay the groundwork for moving from single-sentence disambiguation to discourse. Specifically, DialComm builds upon PatchComm to obtain information from single sentences and integrates such information with additional commonsense assertions to build integral frame representations for discourses. I illustrate DialComm’s ability with an application to end-user programming in natural language. The contributions of this dissertation lie in showing how commonsense inference can be integrated with parsing to resolve ambiguities in natural language, in a transparent manner. I have implemented three candidate systems, with increasingly sophisticated approaches. I verified that they perform well on some standard tests, and they operate in such a way that is understandable to people. This obviates the mythical inevitability of an interpretability-performance tradeoff. I have shown how my techniques can be used in a candidate application, programming in natural language. My work leaves us in a good position to exploit further advances in natural language understanding and commonsense inference. I am optimistic that natural, transparent communication with computers will help make the world a better place.

<div> <div> <div> <div> <p>A recent surge of research on commonsense knowledge has given the AI community new opportunities and challenges. Many studies focus on constructing commonsense knowledge representations from natural language data. However, how to learn such representations from large-scale text data is still an open question. This thesis addresses the problem through statistical script learning, which learns event representations from stereotypical event relationships using weak supervision. These event representations serve as an abundant source of commonsense knowledge to be applied in downstream language tasks. We propose three script learning models that generalize previous works with new insight. A feature-enriched model characterizes fine-grained and entity-based event properties to address specific semantics. A multi-relational model generalizes traditional script learning models which rely on one type of event relationship—co-occurrence—to a multi-relational model that considers typed event relationships, going beyond simple event similarities. A narrative graph model leverages a narrative graph to inform an event with a grounded situation to maintain a global consistency of event states. Also, pretrained language models such as BERT are used to further improve event semantics.</p><p>Our three script learning models do not rely on annotated datasets, as the cost of creating these at large scales is unreasonable. Based on weak supervision, we extract events from large collections of textual data. Although noisy, the learned event representations carry profound commonsense information, enhancing performance in downstream language tasks.</p> <p>We evaluate their performance with various intrinsic and extrinsic evaluations. In the intrinsic evaluations, although the three models are evaluated in terms of various aspects, the shared core task is Multiple Choice Narrative Cloze (MCNC), which measures the model’s ability to predict what happens next, out of five candidate events, in a given situation. This task facilitates fair comparisons between script learning models for commonsense inference. The three models were proposed in three consecutive years, from 2018 to 2020, each outperforming the previous year’s model as well as the competitors’ baselines. Our best model outperforms EventComp, a widely recognized baseline, by a large margin in MCNC: i.e., absolute accuracy improvements of 9.73% (53.86% → 63.59%). In the extrinsic evaluations, we use our models for implicit discourse sense classification (IDSC), a challenging task in which two argument spans are annotated with an implicit discourse sense; the task is to predict the sense type, which requires a deep understanding of common sense between discourse arguments. Moreover, in an additional work we touch on a more interesting group of tasks about psychological commonsense reasoning. Solving these requires reasoning about and understanding human mental states such as motivation, emotion, and desire. Our best model, an enhancement of the narrative graph model, combines the advantages of the above three works to address entity-based features, typed event relationships, and grounded context in one model. The model successfully captures the context in which events appear and interactions between characters’ mental states, outperforming previous works.</p> <div> <div> <div> <p>The main contributions of this thesis are as follows: (1) We identify the importance of entity-based features for representing commonsense knowledge with script learning. (2) We create one of the first, if not the first, script learning models that addresses the multi-relational nature between events. (3) We publicly release contextualized event representations (models) trained on large-scale newswire data. (4) We develop a script learning model that combines entity-based features, typed event relationships, and grounded context in one model, and show that it is a good fit for modeling psychological common sense.</p><p>To conclude, this thesis presents an in-depth exploration of statistical script learning, enhancing existing models with new insight. Our experimental results show that models informed with the new knowledge aspects significantly outperform previous works in both intrinsic and extrinsic evaluations. </p> </div> </div> </div> </div> </div> </div> </div>

A sociologia da modernidade líquida de Zygmunt Bauman (1925 ) é, segundo o autor, um modo possível de articular o conhecimento científico sobre a sociedade com o conhecimento comum da vida cotidiana. Em virtude de sua natureza, seus textos têm despertado grande interesse em um público de leitores não habituados a esse campo disciplinar, a ponto de ser apresentado, por suas casas publicadoras, como um verdadeiro best-seller. Este estudo, situado no âmbito da sociologia da cultura, visa compreender, por meio da análise da sociologia de Bauman, o estado atual do campo sociológico em suas relações com a cultura de massa, tendo por pressuposto a lógica cultural contemporânea em que a distinção tradicional entre alta e baixa cultura ou entre ciência e senso comum parece perder legitimidade. Como resultado, a sociologia da modernidade líquida, a despeito de sua pretensão científica, aproximar-se-ia das práticas de vulgarização da ciência, fenômeno mais amplo e difuso nos diversos domínios disciplinares e que encontraria no esquema teórico de Bauman sua expressão no campo sociológico. / The liquid modernity sociology of Zygmunt Bauman (1925 -) is, according to the author, a possible way to linking scientific knowledge about society with common knowledge of everyday life. Due to their nature, his texts have aroused great interest in an audience of readers not familiar with this disciplinary field, about to be presented, by his publishers, like a true best-seller. This study, situated within the sociology of culture, aims to understand, through the analysis of the Baumans sociology, the current state of the sociological field in its relations to the mass culture, admitting a cultural logic contemporary that the traditional distinction between high and low culture - or between science and common sense - seems to lose legitimacy. As a result, the sociology of liquid modernity, despite its scientific pretensions, would bring the practices of scientific literacy, broader phenomenon and diffuse in different disciplinary domains and find that thetheoretical scheme of Bauman expression in sociological field.

Chiu, Yiu Man Edmund. / Thesis submitted in: December 1999. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 269-248). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Beginning Story --- p.2 / Chapter 1.2 --- Background --- p.3 / Chapter 1.2.1 --- History of Nonmonotonic Reasoning --- p.3 / Chapter 1.2.2 --- Formalizations of Nonmonotonic Reasoning --- p.6 / Chapter 1.2.3 --- Belief Revision --- p.13 / Chapter 1.2.4 --- Network Representation of Knowledge --- p.17 / Chapter 1.2.5 --- Reference from Logic Programming --- p.21 / Chapter 1.2.6 --- Recent Work on Network-type Automatic Reasoning Sys- tems --- p.22 / Chapter 1.3 --- A Novel Inference Network Approach --- p.23 / Chapter 1.4 --- Objectives --- p.23 / Chapter 1.5 --- Organization of the Thesis --- p.24 / Chapter 2 --- The Predicate Inference Network PIN --- p.25 / Chapter 2.1 --- Preliminary Terms --- p.26 / Chapter 2.2 --- Overall Structure --- p.27 / Chapter 2.3 --- Object Layer --- p.29 / Chapter 2.3.1 --- Virtual Object --- p.31 / Chapter 2.4 --- Predicate Layer --- p.33 / Chapter 2.4.1 --- Node Values --- p.34 / Chapter 2.4.2 --- Information Source --- p.35 / Chapter 2.4.3 --- Belief State --- p.36 / Chapter 2.4.4 --- Predicates --- p.37 / Chapter 2.4.5 --- Prototypical Predicates --- p.37 / Chapter 2.4.6 --- Multiple Inputs for a Single Belief --- p.39 / Chapter 2.4.7 --- External Program Call --- p.39 / Chapter 2.5 --- Variable Layer --- p.40 / Chapter 2.6 --- Inter-Layer Links --- p.42 / Chapter 2.7 --- Chapter Summary --- p.43 / Chapter 3 --- Computation for PIN --- p.44 / Chapter 3.1 --- Computation Functions for Propagation --- p.45 / Chapter 3.1.1 --- Computational Functions for Combinative Links --- p.45 / Chapter 3.1.2 --- Computational Functions for Alternative Links --- p.49 / Chapter 3.2 --- Applying the Computation Functions --- p.52 / Chapter 3.3 --- Relations Represented in PIN --- p.55 / Chapter 3.3.1 --- Relations Represented by Combinative Links --- p.56 / Chapter 3.3.2 --- Relations Represented by Alternative Links --- p.59 / Chapter 3.4 --- Chapter Summary --- p.61 / Chapter 4 --- Dynamic Knowledge Update --- p.62 / Chapter 4.1 --- Operations for Knowledge Update --- p.63 / Chapter 4.2 --- Logical Expression --- p.63 / Chapter 4.3 --- Applicability of Operators --- p.64 / Chapter 4.4 --- Add Operation --- p.65 / Chapter 4.4.1 --- Add a fully instantiated single predicate proposition with no virtual object --- p.66 / Chapter 4.4.2 --- Add a fully instantiated pure disjunction --- p.68 / Chapter 4.4.3 --- Add a fully instantiated expression which is a conjunction --- p.71 / Chapter 4.4.4 --- Add a human biased relation --- p.74 / Chapter 4.4.5 --- Add a single predicate expression with virtual objects --- p.76 / Chapter 4.4.6 --- Add a IF-THEN rule --- p.80 / Chapter 4.5 --- Remove Operation --- p.88 / Chapter 4.5.1 --- Remove a Belief --- p.88 / Chapter 4.5.2 --- Remove a Rule --- p.91 / Chapter 4.6 --- Revise Operation --- p.94 / Chapter 4.6.1 --- Revise a Belief --- p.94 / Chapter 4.6.2 --- Revise a Rule --- p.96 / Chapter 4.7 --- Consistency Maintenance --- p.97 / Chapter 4.7.1 --- Logical Suppression --- p.98 / Chapter 4.7.2 --- Example on Handling Inconsistent Information --- p.99 / Chapter 4.8 --- Chapter Summary --- p.102 / Chapter 5 --- Knowledge Query --- p.103 / Chapter 5.1 --- Domains of Quantification --- p.104 / Chapter 5.2 --- Reasoning through Recursive Rules --- p.109 / Chapter 5.2.1 --- Infinite Looping Control --- p.110 / Chapter 5.2.2 --- Proof of the finite termination of recursive rules --- p.111 / Chapter 5.3 --- Query Functions --- p.117 / Chapter 5.4 --- Type I Queries --- p.119 / Chapter 5.4.1 --- Querying a Simple Single Predicate Proposition (Type I) --- p.122 / Chapter 5.4.2 --- Querying a Belief with Logical Connective(s) (Type I) --- p.128 / Chapter 5.5 --- Type II Queries --- p.132 / Chapter 5.5.1 --- Querying Single Predicate Expressions (Type II) --- p.134 / Chapter 5.5.2 --- Querying an Expression with Logical Connectives (Type II) --- p.143 / Chapter 5.6 --- Querying an Expression with Virtual Objects --- p.152 / Chapter 5.6.1 --- Type I Queries Involving Virtual Object --- p.152 / Chapter 5.6.2 --- Type II Queries involving Virtual Objects --- p.156 / Chapter 5.7 --- Chapter Summary --- p.157 / Chapter 6 --- Uniqueness and Finite Termination --- p.159 / Chapter 6.1 --- Proof Structure --- p.160 / Chapter 6.2 --- Proof for Completeness and Finite Termination of Domain Search- ing Procedure --- p.161 / Chapter 6.3 --- Proofs for Type I Queries --- p.167 / Chapter 6.3.1 --- Proof for Single Predicate Expressions --- p.167 / Chapter 6.3.2 --- Proof of Type I Queries on Expressions with Logical Con- nectives --- p.172 / Chapter 6.3.3 --- General Proof for Type I Queries --- p.174 / Chapter 6.4 --- Proofs for Type II Queries --- p.175 / Chapter 6.4.1 --- Proof for Type II Queries on Single Predicate Expressions --- p.176 / Chapter 6.4.2 --- Proof for Type II Queries on Disjunctions --- p.178 / Chapter 6.4.3 --- Proof for Type II Queries on Conjunctions --- p.179 / Chapter 6.4.4 --- General Proof for Type II Queries --- p.181 / Chapter 6.5 --- Proof for Queries Involving Virtual Objects --- p.182 / Chapter 6.6 --- Uniqueness and Finite Termination of PIN Queries --- p.183 / Chapter 6.7 --- Chapter Summary --- p.184 / Chapter 7 --- Lifschitz's Benchmark Problems --- p.185 / Chapter 7.1 --- Structure --- p.186 / Chapter 7.2 --- Default Reasoning --- p.186 / Chapter 7.2.1 --- Basic Default Reasoning --- p.186 / Chapter 7.2.2 --- Default Reasoning with Irrelevant Information --- p.187 / Chapter 7.2.3 --- Default Reasoning with Several Defaults --- p.188 / Chapter 7.2.4 --- Default Reasoning with a Disabled Default --- p.190 / Chapter 7.2.5 --- Default Reasoning in Open Domain --- p.191 / Chapter 7.2.6 --- Reasoning about Unknown Exceptions I --- p.193 / Chapter 7.2.7 --- Reasoning about Unknown Exceptions II --- p.194 / Chapter 7.2.8 --- Reasoning about Unknown Exceptions III --- p.196 / Chapter 7.2.9 --- Priorities between Defaults --- p.198 / Chapter 7.2.10 --- Priorities between Instances of a Default --- p.199 / Chapter 7.2.11 --- Reasoning about Priorities --- p.199 / Chapter 7.3 --- Inheritance --- p.200 / Chapter 7.3.1 --- Linear Inheritance --- p.200 / Chapter 7.3.2 --- Tree-Structured Inheritance --- p.202 / Chapter 7.3.3 --- One-Step Multiple Inheritance --- p.203 / Chapter 7.3.4 --- Multiple Inheritance --- p.204 / Chapter 7.4 --- Uniqueness of Names --- p.205 / Chapter 7.4.1 --- Unique Names Hypothesis for Objects --- p.205 / Chapter 7.4.2 --- Unique Names Hypothesis for Functions --- p.206 / Chapter 7.5 --- Reasoning about Action --- p.206 / Chapter 7.6 --- Autoepistemic Reasoning --- p.206 / Chapter 7.6.1 --- Basic Autoepistemic Reasoning --- p.206 / Chapter 7.6.2 --- Autoepistemic Reasoning with Incomplete Information --- p.207 / Chapter 7.6.3 --- Autoepistemic Reasoning with Open Domain --- p.207 / Chapter 7.6.4 --- Autoepistemic Default Reasoning --- p.208 / Chapter 8 --- Comparison with PROLOG --- p.214 / Chapter 8.1 --- Introduction of PROLOG --- p.215 / Chapter 8.1.1 --- Brief History --- p.215 / Chapter 8.1.2 --- Structure and Inference --- p.215 / Chapter 8.1.3 --- Why Compare PIN with Prolog --- p.216 / Chapter 8.2 --- Representation Power --- p.216 / Chapter 8.2.1 --- Close World Assumption and Negation as Failure --- p.216 / Chapter 8.2.2 --- Horn Clauses --- p.217 / Chapter 8.2.3 --- Quantification --- p.218 / Chapter 8.2.4 --- Build-in Functions --- p.219 / Chapter 8.2.5 --- Other Representation Issues --- p.220 / Chapter 8.3 --- Inference and Query Processing --- p.220 / Chapter 8.3.1 --- Unification --- p.221 / Chapter 8.3.2 --- Resolution --- p.222 / Chapter 8.3.3 --- Computation Efficiency --- p.225 / Chapter 8.4 --- Knowledge Updating and Consistency Issues --- p.227 / Chapter 8.4.1 --- PIN and AGM Logic --- p.228 / Chapter 8.4.2 --- Knowledge Merging --- p.229 / Chapter 8.5 --- Chapter Summary --- p.229 / Chapter 9 --- Conclusion and Discussion --- p.230 / Chapter 9.1 --- Conclusion --- p.231 / Chapter 9.1.1 --- General Structure --- p.231 / Chapter 9.1.2 --- Representation Power --- p.231 / Chapter 9.1.3 --- Inference --- p.232 / Chapter 9.1.4 --- Dynamic Update and Consistency --- p.233 / Chapter 9.1.5 --- Soundness and Completeness Versus Efficiency --- p.233 / Chapter 9.2 --- Discussion --- p.234 / Chapter 9.2.1 --- Different Selection Criteria --- p.234 / Chapter 9.2.2 --- Link Order --- p.235 / Chapter 9.2.3 --- Inheritance Reasoning --- p.236 / Chapter 9.3 --- Future Work --- p.237 / Chapter 9.3.1 --- Implementation --- p.237 / Chapter 9.3.2 --- Application --- p.237 / Chapter 9.3.3 --- Probabilistic and Fuzzy PIN --- p.238 / Chapter 9.3.4 --- Temporal Reasoning --- p.238 / Bibliography --- p.239