Concepts and Ontologies

In this paper, we would like to describe two logical systems — Tichy's transparent intensional logic and Girard's linear logic. Transparent intensional logic is a method of logical analysis of natural language, which represents expressive system, independent of chosen source language. Our research vision is to connect this method with linear logic. In future research, this connection would allow us to apply Ludics theory principles (which is the extension of the linear logic) to natural language sentences, which puts them in a logical time and space.

This paper addresses the mereological problem of the unity of structured propositions. The problem is how to make multiple parts interact such that they form a whole that is ultimately related to truth and falsity. The solution I propose is based on a Platonist variant of procedural semantics. I think of procedures as abstract entities that detail a logical path from input to output. Procedures are modeled on a function/argument logic, but are not functions (mappings). Instead they are higher-order, fine-grained structures. I identify propositions with particular kinds of molecular procedures containing multiple sub-procedures as parts. Procedures are among the basic entities of my ontology, while propositions are derived entities. The core of a structured proposition is the procedure of predication, which is an instance of the procedure of functional application. The main thesis I defend is that procedurally conceived propositions are their own unifiers detailing how their parts interact so as to form a unit. They are not unified by one of their constituents, e.g., a relation or a sub-procedure, on pain of regress. The relevant procedural semantics is Transparent Intensional Logic, a hyperintensional, typed $$\lambda $$ -calculus, whose $$\lambda $$ -terms express four different kinds of procedures. While demonstrating how the theory works, I place my solution in a wider historical and systematic context.

In the paper we show that sentences differing only in topic-focus articulation have different logical structures, and thus they also have different truth-conditions. Our analysis is based on the procedural semantics of Transparent Intensional Logic (TIL) assigning to sentences hyperpropositions as their structured meanings. We analyse the phenomena of presupposition connected with a topic and allegation triggered by a focus of a sentence so that relevant consequences can be formally derived.

This paper describes the design of a knowledge representation and reasoning system, named Dolphin, which is based on higher-order temporal Transparent Intensional Logic (TIL). An intelligent agent (NAM), that is able to read newspaper headlines from specialized internet server and allows users to ask questions about various world situations is chosen to demonstrate Dolphin features. Temporal aspects play an essential role in natural language therefore we present how this phenomenon is handled in the system. Reasoning capabilities of the agent are divided into three individual strategies and described in the text. As a result we compare NAM answers to one of the most used search engines nowadays.

Reasoning with quantifier expressions in natural language combines logical and arithmetical features, transcending strict divides between qualitative and quantitative. Our topic is this cooperation of styles as it occurs in common linguistic usage and its extension into the broader practice of natural language plus ‘grassroots mathematics’.We begin with a brief review of $\mathsf {FO}(\#)$ , first-order logic with counting operators and cardinality comparisons. This system is known to be of very high complexity, and drowns out finer aspects of the combination of logic and counting. We therefore move to a small fragment that can represent numerical syllogisms and basic reasoning about comparative size: monadic first-order logic with counting, $\mathsf {MFO}(\#)$ . We provide normal forms that allow for axiomatization, determine which arithmetical notions can be defined on finite and on infinite models, and conversely, we discuss which logical notions can be defined out of purely arithmetical ones, and what sort of (non-)classical logics can be induced.Next, we investigate a series of strengthenings of $\mathsf {MFO}(\#)$ , again using normal form methods. The monadic second-order version is close, in a precise sense, to additive Presburger Arithmetic, while versions with the natural device of tuple counting take us to Diophantine equations, making the logic undecidable. We also define a system $\mathsf {ML}(\#)$ that combines basic modal logic over binary accessibility relations with counting, needed to formulate ubiquitous reasoning patterns such as the Pigeonhole Principle. We prove decidability of $\mathsf {ML}(\#)$ , and provide a new kind of bisimulation matching the expressive power of the language.As a complement to the fragment approach pursued here, we also discuss two other ways of lowering the complexity of $\mathsf {FO}(\#)$ by changing the semantics of counting in natural ways. A first approach replaces cardinalities by abstract but well-motivated values of ‘mass’ or other mereological aggregating notions. A second approach keeps the cardinalities but generalizes the meaning of counting to work in models that allow dependencies between variables.Finally, we return to our starting point in natural language, confronting the architecture of our formal systems with linguistic quantifier vocabulary and syntax, as well as with natural reasoning modules such as the monotonicity calculus. In addition to these encounters with formal semantics, we discuss the role of counting in semantic evaluation procedures for quantifier expressions and determine, for instance, which binary quantifiers are computable by finite ‘semantic automata’. We conclude with some general thoughts on yet further entanglements of logic and counting in formal systems, on rethinking the qualitative/quantitative divide, and on connecting our analysis to empirical findings in cognitive science.

We present in this paper the design and implementation of the OntoNL Framework, a natural language interface generator for knowledge repositories, as well as a natural language system for interactions with multimedia repositories which was built using the OntoNL Framework. The system allows the users to specify natural language requests about the multimedia content with rich semantics that result to digital content delivery. We propose and evaluate a semantic relatedness measure for OWL domain ontologies that concludes to the semantic ranking of ontological, grammatically-related structures. This procedure is used to disambiguate in a particular domain of context and represent in an ontology query language, natural language expressions. The ontology query language that we use is the SPARQL. The construction of the queries is automated and also dependent on the semantic relatedness measurement of ontology concepts. We also present the results of experimentation with the system.

The STI4-RT takes care of an IACVIRTUAL (Artificial Intelligence Applied at a Virtual Doctor’s Office Modeling and Implementation) line of work which is responsible for the development of a virtual environment for giving support to the learning process of cardiology and masthology students. To accomplish this goal, it was developed an Intelligent Tutoring System (ITS), which is being the responsible for provide an interactive environment for this learning process. The construction of this ITS was based on some of theory fundamentals, like intelligent agents (under a multi-agents system approach), ontologies (for modeling the knowledge domain and pedagogical approaches), a proposal of system architecture called MATHEMA. Besides these technologies, it was used a Web layer to access the environment and a persistence layer for saving students learning information. The multi-agents system structure is used to map the architectural proposal into intelligent agents. Those agents interacts with the ontology modeling for using the most appropriate pedagogical approach to teach the domain concepts and use the persistence layer to register information about the student learning process. Through the Web Interface, the students send request solicitations for the agent container, and receive the response back from this container at the Web tear. The domain expert uses the agents to make maintenance at the domain modeling. The expert interference allows correction and evolutions to be done at the ontology content. At moment, the STI4-RT was tested for concepts related to the Thoracic Radiology medical specialty. Satisfactory results were obtained.

The coordination of distributed processing is of great interest to a number of research communities. However these research communities, such as those involved in e-science GRIDs and multi-agent systems, view this problem from disparate viewpoints. This paper aims to contribute to the necessary reconciliation of these perspectives. By demonstrating the coordination of processes whether reactive (such as web services) or proactive (such as autonomous agents) can be done with a single representation using a protocol language, RASA The multi-agent paradigm introduces, into any model of distributed systems, flexibility and autonomy that can be daunting and intimidating to scientists accustomed to more orthodox approaches. It is for this reason that it is important that the model for coordination of this system is reliable, verifiable, inspectable, referable, composable and executable. The language RASA provides this functionality, which we extend its use for not only agent interaction protocols but also to express workflows. The language for expression then becomes the domain of discourse as well as potentially the language for the workflow's execution.

PREFACE, ABBREVIATIONS, 1. THE NEED FOR A LOGICAL CALCULUS FOR THE THEORY OF SINN AND BEDEUTUNG, Introduction, Frege's Project: Logicism and the Notion of Begriffsschrift, The Theory of Sinn and Bedeutung, The Limitations of the Begriffsschrift, Filling the Gap, 2. THE LOGIC OF THE GRUNDGESETZE, Logical Language and the Content of Logic, Functionality and Predication, Quantifiers and Gothic Letters, Roman Letters: An Alternative Notation for Generality, Value-Ranges and Extensions of Concepts, The Syntactic Rules of the Begriffsschrift, The Axiomatization of Frege's System, Responses to the Paradox, 3. SINNE AND GEDANKEN, The Nature of Sinne Generally, The Sinne of Incomplete Expressions and the Composition of Gedanken, Context Principle, Priority Thesis, and Multiple Analyses: Challenges to Compositionality, The Identity Conditions of Sinne and Gedanken, 4. CHURCH'S LOGIC OF SENSE AND DENOTATION, Overview, The Method of Transparent Intensional Logic, Alternatives (0), (1) and (2) and Synonymous Isomorphism, The Formulation of the Systems, Problems in Church's Logic of Sense and Denotation, The UnFregean Elements of Church's Logic of Sense and Denotation, 5. A LOGICAL CALCULUS FOR THE THEORY OF SINN AND BEDEUTUNG, Basic Features, Names and Variables for Sinne, The New Constants of the Expanded Language, Syntactic Rules of the Expanded Language, Axioms and Inference Rules of the Expanded Calculi, Inferences Involving Propositional Attitudes and Quantifying In, Difficulties with the Expanded Calculi, 6. COMPARISON WITH RUSSELL AND OTHER THINKERS, Solatium miseris, socios habuisse malorum., Russell's Ontology of Propositions, Russell and the Principles of Mathematics Appendix B Paradox, Frege and the Principles of Mathematics Appendix B Paradox, Lessons Learned from Russell, Other Systems of Intensional Logic, 7. POSSIBLE REVISIONS TO FREGE'S PHILOSOPHY, Adequate versus Ad Hoc Solutions, A Cantorian Analysis of the Difficulties, Dropping Classes and/or Concepts, Reducing the Number of Sinne and Gedanken, Ramification as a Solution to the Paradoxes, The Justification of Ramification, Conclusion, APPENDIX: SUMMARY OF DEFINITIONS, AXIOMS AND INFERENCE RULES, BIBLIOGRAPHY, INDEX

Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.

In this paper, we offer one possible way to bridge the gap existing between the lexical and logical semantic analysis of natural language expressions. We look for a link that would allow us to perform a more adequate and integrated semantic analysis of natural language expressions. The solution is to combine the descriptions of lexical units as they are developed within the area of lexical semantics (e.g. WordNet) with logical analysis of sentence meanings worked out within the Transparent Intensional Logic framework.

One of the main goals of Distributed Artificial Intelligence is to devise methods to join a community of Computational Agents into a Multi-Agent System, where these agents can cooperate to reach common goals. Cooperation in a Cognitive Agent community is usually supported by an Agent Communication Language (ACL) which allows the agents to exchange knowledge and information through a computer network. In this paper, we propose Parla, a high level agent communication language to cognitive multi-agent systems. This language is based on a standard message format, that contains the necessary information for the message integrity, network security and groupware services to be implemented. These services can either be performed by the lower layers of the system or be included in the high level agent communication support. This message format has a specific slot to store the cooperation language expressions. These expressions consist of a primitive name and an argument, which should be a valid expression of a knowledge representation formalism supported by the cognitive agent. The following aspects of the Parla language are presented: language layers, agent communication support requirements, message format, primitive set and primitive semantics. To demonstrate the language use, a cooperation example among four agents is presented. In this example, each agent has its own specific domain of knowledge but, because of global interdependences, cooperation is necessary. This example refers to the recomposition of part of the South Brazil's electrical network.

In this paper we approach the novel problem of segmenting an image based on a natural language expression. This is different from traditional semantic segmentation over a predefined set of semantic classes, as e.g., the phrase “two men sitting on the right bench” requires segmenting only the two people on the right bench and no one standing or sitting on another bench. Previous approaches suitable for this task were limited to a fixed set of categories and/or rectangular regions. To produce pixelwise segmentation for the language expression, we propose an end-to-end trainable recurrent and convolutional network model that jointly learns to process visual and linguistic information. In our model, a recurrent neural network is used to encode the referential expression into a vector representation, and a fully convolutional network is used to a extract a spatial feature map from the image and output a spatial response map for the target object. We demonstrate on a benchmark dataset that our model can produce quality segmentation output from the natural language expression, and outperforms baseline methods by a large margin.

Transparent Intensional Logic is a logical theory developed with a view to logical analysis of sizeable fragments of primarily natural language.

Referring Expression Comprehension (REC) is a task that requires to indicate particular objects within an image by natural language expressions. Previous studies on this task have assumed that the language expression and the image are one-to-one correspondence, that is, the language refers to the target region must exist in the current image and then the region with the highest score will be located, no matter whether they match or not. However, in practical applications, REC is required to locate the reference target region from a series of matched, semi-matched and mismatched scene image sequences. It is the 3D version of this challenge that refers to as Scenario Referring Expression Comprehension (SREC) in this paper. To accomplish such a task, we made a testset based on the existing real-scenario dataset enhancement, constructed a Dual Attributes Recursive Retrieve Reasoning Model (DA3R) for the first time with the Attributes of both images and expressions, and finally verified the feasibility of the method on the testset by assess with three different types of enhanced expression.