On Repairing Reasoning Reversals via Representational Refinements

Representation is a fluent. A mismatch between the real world and an agent’s representation of it can be signalled by unexpected failures (or successes) of the agent’s reasoning. The ‘real world ’ may include the ontologies of other agents. Such mismatches can be repaired by refining or abstracting an agent’s ontology. These refinements or abstractions may not be limited to changes of belief, but may also change the signature of the agent’s ontology. We describe the implementation and successful evaluation of these ideas in the ORS system. ORS diagnoses failures in plan execution and then repairs the faulty ontologies. Our automated approach to dynamic ontology repair has been designed specifically to address real issues in multi-agent systems, for instance, as envisaged in the Semantic Web

at the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011)

Technical Report TR-2011/1, Department of Languages and Computation. University of Almeria November 2011. Joaquín Cañadas, Grzegorz J. Nalepa, Joachim Baumeister (Editors)The seventh workshop on Knowledge Engineering and Software Engineering (KESE7) was held at the Conference of the Spanish Association for Artificial Intelligence (CAEPIA-2011) in La Laguna (Tenerife), Spain, and brought together researchers and practitioners from both fields of software engineering and artificial intelligence. The intention was to give ample space for exchanging latest research results as well as knowledge about practical experience.University of Almería, Almería, Spain. AGH University of Science and Technology, Kraków, Poland. University of Würzburg, Würzburg, Germany

Loki : the semantic wiki for collaborative knowledge engineering

We present Loki, a semantic wiki designed to support the collaborative knowledge engineering process with the use of software engineering methods. Designed as a set of DokuWiki plug-ins, it provides a variety of knowledge representation methods, including semantic annotations, Prolog clauses, and business processes and rules oriented to specific tasks. Knowledge stored in Loki can be retrieved via SPARQL queries, in-line Semantic MediaWiki-like queries, or Prolog goals. Loki includes a number of useful features for a group of experts and knowledge engineers developing the wiki, such as knowledge visualization, ontology storage, or code hint and completion mechanism. Reasoning unit tests are also introduced to validate knowledge quality. The paper is complemented by the formulation of the collaborative knowledge engineering process and the description of experiments performed during Loki development to evaluate its functionality. Loki is available as free software at https://loki.re

Discrete Structures (KSU)

This Grants Collection for Discrete Structures was created under a Round Eleven ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/compsci-collections/1025/thumbnail.jp

Codeco: A Grammar Notation for Controlled Natural Language in Predictive Editors

Existing grammar frameworks do not work out particularly well for controlled natural languages (CNL), especially if they are to be used in predictive editors. I introduce in this paper a new grammar notation, called Codeco, which is designed specifically for CNLs and predictive editors. Two different parsers have been implemented and a large subset of Attempto Controlled English (ACE) has been represented in Codeco. The results show that Codeco is practical, adequate and efficient

A Framework for Automating Security Assessments with Deductive Reasoning

Proper testing of hardware and software infrastructure and applications has become mandatory. To this purpose, security researchers and software companies have released a plethora of domain specific tools, libraries and frameworks that assist human operators (penetration testers, red teamers, bug hunters) in finding and exploiting specific vulnerabilities, and orchestrating the activities of a security assessment. Most tools also require minor reconfigurations in order to operate properly with isomorphic systems, characterized by the same exploitation path even in presence of different configurations. In this paper we present a human-assisted framework that tries to overcome the aforementioned limitations. Our proposal is based on a Prolog-based expert system with facts and deductive rules that allow to infer new facts from existing ones. Rules are bound to actions whose results are fed back into the knowledge base as further facts. In this way, a security assessment is treated like a theorem that has to be proven. We have built an initial prototype and evaluated it in different security assessments of increasing complexity (jeopardy and boot-to-root machines). Our preliminary results show that the proposed approach can address the following challenges; (a) reaching non-standard goals (which would be missed by most tools and frameworks); (b) solving isomorphic systems without the need for reconfiguration; (c) identifying vulnerabilities from chained weaknesses and exposures

R-SQL: An SQL Database System with Extended Recursion

The relational database language SQL:1999 standard supports recursion, but thisapproach is limited to the linear case. Moreover, mutual recursion is not supported,and negation cannot be combined with recursion. We designed the language R-SQLto overcome these limitations in [ANSS13], improving termination properties in re-cursive definitions. In addition we developed a proof of concept implementation ofan R-SQL system. In this paper we describe in detail an improved system enhanc-ing performance. It can be integrated into existing RDBMS’s, extending them withthe aforementioned benefits of R-SQL. The system processes an R-SQL databasedefinition obtaining its extension in tables of an RDBMS (such as PostgreSQL andDB2). It is implemented in SWI-Prolog and it produces a Python script that, uponexecution, computes the result of the R-SQL relations. We provide some perfor-mance results showing the efficiency gains w.r.t. the previous version. We alsoinclude a comparative analysis including some representative relational a deductive systems