Homo Sapiens Sapiens Meets Homo Strategicus at the Laboratory

Homo Strategicus populates the vast plains of Game Theory. He knows all logical implications of his knowledge (logical omniscience) and chooses optimal strategies given his knowledge and beliefs (rationality). This paper investigates the extent to which the logical capabilities of Homo Sapiens Sapiens resemble those possessed by Homo Strategicus. Controlling for other-regarding preferences and beliefs about the rationality of others, we show, in the laboratory, that the ability of Homo Sapiens Sapiens to perform complex chains of iterative reasoning is much better than previously thought. Subjects were able to perform about two to three iterations of reasoning on average.iterative reasoning; depth of reasoning; logical omniscience; rationality; experiments; other-regarding preferences

Relationships Between Middle School Students\u27 Adaptive Reasoning When Creating Learner-Generated Drawings and Partner Talk During Inquiry-Based Mathematical Tasks

Adaptive reasoning is one of five components students use to develop mathematical expertise and become mathematically proficient. When students adapt their reasoning they are logically thinking about the mathematical relationships between concepts and adapting their thinking to solve problems. Three Act Math Tasks are popular math problems used in schools in which students engage in adaptive reasoning. These types of problems are beneficial to students because they engage students in inquiry-based learning, a kind of learning where students work to pose questions, interpret data, design ways to solve the problem and present their solutions. Little is known about how students adapt their reasoning as they partake in these types of tasks. The objective of this study is to better understand what adaptive reasoning strategies seventh graders used and how they used these strategies when engaged in inquiry-based mathematical tasks. To accomplish this, the study observed 18 seventh grade students as they worked through three mathematical tasks. The researcher observed student discussions and their drawings to see what adaptive reasoning strategies were being used by students and how the strategies were used throughout different stages of the tasks. In this way a more complete picture of how students adapted their reasoning was obtained. The researcher analyzed student use of six different adaptive reasoning indicators, including: 1) relationships and connections, 2) justifications, 3) alternates pursued, 4) prior knowledge, 5) legitimacy determined, and 6) pattern recognition. Results indicate that students used all six adaptive reasoning strategies. Students primarily adapted their reasoning by finding relationships and connections and making justifications. Additionally, each student demonstrated a unique pattern of adaptive reasoning strategies which was mediated by their partner. Use of the other four indicators, alternates pursued, prior knowledge, legitimacy determined and pattern recognition were used in conjunction with the two primary indicators. Additionally, different patterns of use were identified within the separate modalities of student drawings and discussions. This study is beneficial because it helps teachers and researchers better understand what adaptive reasoning strategies students are utilizing and the relationship between these strategies in a classroom setting. This affords teachers and researchers opportunities to develop better learning experiences and understand how students reason in light of mathematical proficiency

Argumentation-based fault diagnosis for home networks

Home networks are a fast growing market but managing them is a difficult task, and diagnosing faults is even more challenging. Current fault management tools provide comprehensive information about the network and the devices but it is left to the user to interpret and reason about the data and experiment in order to find the cause of a problem. Home users may not have motivation or time to learn the required skills. Furthermore current tools adopt a closed approach which hardcodes a knowledge base, making them hard to update and extend. This paper proposes an open fault management framework for home networks, whose goal is to simplify network troubleshooting for non-expert users. The framework is based on assumption-based argumentation that is an AI technique for knowledge representation and reasoning. With the underlying argumentation theory, we can easily capture and model the diagnosis procedures of network administrators. The framework is rule-based and extensible, allowing new rules to be added into the knowledge base and diagnostic strategies to be updated on the fly.The framework can also utilise external knowledge and make distributed diagnosi

Named Graphs as a Mechanism for Reasoning About Provenance

Named Graphs is a simple, compatible extension to the RDF abstract syntax that enables statements to be made about RDF graphs. This approach is in contrast to earlier attempts such as RDF reification, or knowledge-base specific extensions including quads and contexts. In this paper we demonstrate the use of Named Graphs and our experiences developing new kinds of semantic web application that build on Named Graphs for digital signatures, provenance, and semantic reasoning. We present a working example based on the Named Graphs for Jena (NG4J) API, from which we developed a semantic version control system for Software Engineering capable of reasoning about Named Graph-based provenance. We go on to discuss the implications of Named Graphs for Description Logics and semantic inference strategies

Functional Modelling for Fault Diagnosis and its application for NPP.

The paper presents functional modelling and its application for diagnosis in nuclear power plants. Functional modelling is defined and its relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed and it is demonstrated that the levels of abstraction in models for diagnosis must reflect plant knowledge about goals and functions which is represented in functional modelling. Multilevel flow modelling (MFM), which is a method for functional modelling, is introduced briefly and illustrated with a cooling system example. The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool, the MFMSuite. MFM applications in nuclear power systems are described by two examples: a PWR; and an FBR reactor. The PWR example show how MFM can be used to model and reason about operating modes. The FBR example illustrates how the modelling development effort can be managed by proper strategies including decomposition and reuse