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24
A Novel Architecture for Situation Awareness Systems
"... Abstract. Situation Awareness (SA) is the problem of comprehending elements of an environment within a volume of time and space. It is a crucial factor in decisionmaking in dynamic environments. Current SA systems support the collection, filtering and presentation of data from different sources ver ..."
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Abstract. Situation Awareness (SA) is the problem of comprehending elements of an environment within a volume of time and space. It is a crucial factor in decisionmaking in dynamic environments. Current SA systems support the collection, filtering and presentation of data from different sources very well, and typically also some form of lowlevel data fusion and analysis, e.g., recognizing patterns over time. However, a still open research challenge is to build systems that support higherlevel information fusion, viz., to integrate domain specific knowledge and automatically draw conclusions that would otherwise remain hidden or would have to be drawn by a human operator. To address this challenge, we have developed a novel system architecture that emphasizes the rôle of formal logic and automated theorem provers in its main components. Additionally, it features controlled natural language for operator I/O. It offers three logical languages to adequately model different aspects of the domain. This allows to build SA systems in a more declarative way than is possible with current approaches. From an automated reasoning perspective, the main challenges lay in combining (existing) automated reasoning techniques, from lowlevel data fusion of timestamped data to semantic analysis and alert generation that is based on linear temporal logic. The system has been implemented and interfaces with GoogleEarth to visualize the dynamics of situations and system output. It has been successfully tested on realistic data, but in this paper we focus on the system architecture and in particular on the interplay of the different reasoning components. 1
LogAnswer  A DeductionBased Question Answering System
 In Automated Reasoning (IJCAR 2008), Lecture Notes in Computer Science
, 2008
"... Abstract. LogAnswer is an open domain question answering system which employs an automated theorem prover to infer correct replies to natural language questions. For this purpose LogAnswer operates on a large axiom set in firstorder logic, representing a formalized semantic network acquired from ex ..."
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Abstract. LogAnswer is an open domain question answering system which employs an automated theorem prover to infer correct replies to natural language questions. For this purpose LogAnswer operates on a large axiom set in firstorder logic, representing a formalized semantic network acquired from extensive textual knowledge bases. The logicbased approach allows the formalization of semantics and background knowledge, which play a vital role in deriving answers. We present the functional LogAnswer prototype, which consists of automated theorem provers for logical answer derivation as well as an environment for deep linguistic processing.3 1
An Update on PENG Light
"... This paper presents an update on PENG Light, a lightweight and portable controlled natural language processor that can be used to translate a welldefined subset of English unambiguously into a formal target language. We illustrate by example of a Firefox extension that provides a simple interface t ..."
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This paper presents an update on PENG Light, a lightweight and portable controlled natural language processor that can be used to translate a welldefined subset of English unambiguously into a formal target language. We illustrate by example of a Firefox extension that provides a simple interface to the controlled natural language processor how web pages can be annotated with textual information written in controlled natural language and how these annotations can be translated incrementally into firstorder logic. We focus in particular on technical aspects of the controlled language processor and show in detail how lookahead information that can be used to guide the writing process of the author is generated during the parsing process. Additionally, we discuss what kind of user interaction is required for processing unknown content words. 1
The LogAnswer Project at ResPubliQA 2010
"... Abstract. The LogAnswer project investigates the potential of deep linguistic processing and logical reasoning for question answering. The paragraph selection task of ResPubliQA 2010 offered the opportunity to validate improvements of the LogAnswer QA system that reflect our experience from ResPubli ..."
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Abstract. The LogAnswer project investigates the potential of deep linguistic processing and logical reasoning for question answering. The paragraph selection task of ResPubliQA 2010 offered the opportunity to validate improvements of the LogAnswer QA system that reflect our experience from ResPubliQA 2009. Another objective was to demonstrate the benefit of QA technologies over a pure IR approach. Two runs were produced for ResPubliQA 2010: The first run corresponds to LogAnswer with standard configuration. The accuracy of 0.52 and c@1 score of 0.59 witness that LogAnswer has matured (in 2009, accuracy was 0.40 and
CASCJ4 the 4th IJCAR ATP system competition
 In: Proc. IJCAR ’08
, 2008
"... The CADE ATP System Computer (CASC) evaluates the performance of sound, fully automatic, classical firstorder logic, ATP systems. The evaluation is in terms of the number of problems solved, the number of acceptable proofs and models produced, and the average runtime for problems solved, in the con ..."
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The CADE ATP System Computer (CASC) evaluates the performance of sound, fully automatic, classical firstorder logic, ATP systems. The evaluation is in terms of the number of problems solved, the number of acceptable proofs and models produced, and the average runtime for problems solved, in the context of a bounded number of eligible problems chosen from the TPTP problem library, and a specified time limit for each solution attempt. The 4th IJCAR ATP System Competition (CASCJ4) was held on 13th August 2008. The design of the competition and it’s rules, and information regarding the competing systems, are provided in this report. 1
The Hyper Tableaux Calculus with Equality and an Application to Finite Model Computation
, 2008
"... In most theorem proving applications, a proper treatment of equational theories or equality is mandatory. In this paper we show how to integrate a modern treatment of equality in the hyper tableau calculus. It is based on splitting of positive clauses and an adapted version of the superposition infe ..."
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In most theorem proving applications, a proper treatment of equational theories or equality is mandatory. In this paper we show how to integrate a modern treatment of equality in the hyper tableau calculus. It is based on splitting of positive clauses and an adapted version of the superposition inference rule, where equations used for superposition are drawn (only) from a set of positive unit clauses, and superposition inferences into positive literals is restricted into (positive) unit clauses only. The calculus also features a generic, semantically justified simplification rule which covers many redundancy elimination techniques known from superposition theorem proving. Our main results are soundness and completeness of the calculus, but we also show how to apply the calculus for finite model computation, and we briefly describe the implementation.
Combining Theorem Proving with Natural Language Processing
"... Abstract. The LogAnswer system is an application of automated reasoning to the field of open domain question answering, which aims at finding answers to natural language questions regarding arbitrary topics. In our system we have integrated an automated theorem prover in a framework of natural langu ..."
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Abstract. The LogAnswer system is an application of automated reasoning to the field of open domain question answering, which aims at finding answers to natural language questions regarding arbitrary topics. In our system we have integrated an automated theorem prover in a framework of natural language processing tools to allow for deductive reasoning over an extensive knowledge base derived from textual sources. For this purpose we had to intertwine two opposing approaches: on the one hand formal logic with its precision but brittleness, and on the other hand, machine learning applied to shallow linguistic features, which are robust but less precise. In the paper we present implementation details and discuss obstacles and their proposed solutions. 1
Deontic logic for human reasoning
 Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation  Essays Dedicated to Gerhard Brewka on the Occasion of His 60th Birthday, volume 9060 of Lecture Notes in Computer Science
, 2014
"... Deontic logic is shown to be applicable for modelling human reasoning. For this the Wason selection task and the suppresion task are discussed in detail. Different versions of modelling norms with deontic logic are introduced and in the case of the Wason selection task it is demonstrated how differe ..."
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Deontic logic is shown to be applicable for modelling human reasoning. For this the Wason selection task and the suppresion task are discussed in detail. Different versions of modelling norms with deontic logic are introduced and in the case of the Wason selection task it is demonstrated how differences in the preformance of humans in the abstract and in the social contract case can be explained. Furthermore it is shown that an automated theorem prover can be used as a reasoning tool for deontic logic. Human reasoning and in particular conditional reasoning has been researched in various disciplines. In cognitive psychology a lot of experimental data is collected and there are numerous different modelling approaches. In philosophy, rationality and normative reasoning is a topic with increasing interest. In artificial intelligence research the aim is to model human rational reasoning within artificial systems. Recently there are some papers from automated reasoning which try to model experiments from cognitive psychology; in particular the experiments involving the Wason selection and the suppression tasks are discussed in the literature ([HR09, HPW11]). In this paper we want to contribute to this discussion by advocating deontic logic to this end. However, our aim is not only to use this logic to model the settings and the result of these experiments, moreover, we want to use an automated reasoning system to solve the tasks. We are well aware that this is not the first paper proposing deontic logic for conditional reasoning (this will be discussed in a separate section), but to the best of our knowledge we are the first to offer a high performance theorem prover, which is able to handle (and decide) deontic logic. We will use the first order predicate logic prover Hyper for deontic logic, which is possible because we translate the latter into the description logic ALC. This again can be translated into DLclauses, for which Hyper is a decision procedure. In the following section we introduce the Wason Selection Task, which will be used to discuss several logical approaches in Section 2 and to introduce our approach using deontic ∗Work supported by DFG FU 263/151 ’Ratiolog’ 1 ar
Logical ontology validation using an automatic theorem prover
 In Proceedings of the 19th European Conference on Arti cial Intelligence (ECAI
, 2010
"... Abstract. Ontologies are utilized for a wide range of tasks, like information retrieval/extraction or text generation, and in a multitude of domains, such as biology, medicine or business and commerce. To be actually usable in such realworld scenarios, ontologies usually have to encompass a large ..."
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Abstract. Ontologies are utilized for a wide range of tasks, like information retrieval/extraction or text generation, and in a multitude of domains, such as biology, medicine or business and commerce. To be actually usable in such realworld scenarios, ontologies usually have to encompass a large number of factual statements. However, with increasing size, it becomes very difficult to ensure their complete correctness. This is particularly true in the case when an ontology is not handcrafted but constructed (semi)automatically through text mining, for example. As a consequence, when inference mechanisms are applied on these ontologies, even minimal inconsistencies oftentimes lead to serious errors and are hard to trace back and find. This paper addresses this issue and describes a method to validate ontologies using an automatic theorem prover and MultiNet axioms. This logicbased approach allows to detect many inconsistencies, which are difficult or even impossible to identify through statistical methods or by manual investigation in reasonable time. To make this approach accessible for ontology developers, a graphical user interface is provided that highlights erroneous axioms directly in the ontology for quicker fixing.
Proceedings of the CADE23 ATP System Competition CASC23
"... The CADE ATP System Competition (CASC) evaluates the performance of sound, fully automatic, classical logic, ATP systems. The evaluation is in terms of the number of problems solved, the number of acceptable proofs and models produced, and the average runtime for problems solved, in the context of a ..."
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The CADE ATP System Competition (CASC) evaluates the performance of sound, fully automatic, classical logic, ATP systems. The evaluation is in terms of the number of problems solved, the number of acceptable proofs and models produced, and the average runtime for problems solved, in the context of a bounded number of eligible problems chosen from the TPTP problem library, and specified time limits on solution attempts. The CADE23 ATP System Competition (CASC23) was held on 3rd August 2011. The design of the competition and its rules, and information regarding the competing systems, are provided in this report. 1