Dual Processing theory proposes that the ability to over ride associative (System 1) in favour of analytical (System 2) processed in deductive reasoning may depend on inhibitory control. The present study applies this association to a spatial reasoning task by adapting a mental rotation task to a multichoice format including System 1 (mirror) and System 2 (rotated image) responses. Fifty undergraduate volunteers from the University of Canterbury responded to a Stroop task as a measure of inhibitory control that was compared with System 1 and System 2 responding from a spatial and a deductive reasoning task. It was expected that people with weaker inhibitory potential would make more System 1 and fewer System 2 responses in both deductive and visual-spatial reasoning tasks. Contrary to expectation System 2 responding dominated for both tasks and correlations between both reasoning tasks and measures of inhibitory control were non-significant. The differing idiosyncratic demands of each task may have obscured any common variables associated with inhibitory control. This research initiated a test for the presence of System 1 and System 2 in spatial reasoning.

abstract: Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former. / Dissertation/Thesis / Ph.D. Computer Science 2012

abstract: Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond factual recall of the recognized components and includes reasoning and thinking beyond what can be seen (or perceived). Understanding is often evaluated by asking questions of increasing difficulty. Thus, the expected functionalities of an intelligent Image Understanding system can be expressed in terms of the functionalities that are required to answer questions about an image. Answering questions about images require primarily three components: Image Understanding, question (natural language) understanding, and reasoning based on knowledge. Any question, asking beyond what can be directly seen, requires modeling of commonsense (or background/ontological/factual) knowledge and reasoning. Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2018

A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system – by combining reasoning over and reasoning about streams – can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes. / <p>The series name <em>Linköping Studies in Science and Technology Licentiate Thesis</em> is inocorrect. The correct series name is <em>Linköping Studies in Science and Technology Thesis</em>.</p> / NFFP6 / CENIIT

This work includes an attempt to enhance the autonomy of intelligent agents via real-time learning.In nature, the ability to learn at runtime gives species which can do so key advantages over others. While most AI systems do not need to have this ability but can be trained before deployment, it allows agents to adapt, at runtime, to changing and generally unknown circumstances, and then to exploit their environment for their own purposes. To reach this goal, in this thesis a pragmatic design (ONA) for a general-purpose reasoner incorporating Non-Axiomatic Reasoning System (NARS) theory is explored. The design and implementation is presented in detail, in addition to the theoretical foundation. Then, experiments related to various system capabilities are carried out and summarized, together with application projects where ONA is utilized: a traffic surveillance application in the Smart City domain to identify traffic anomalies through real-time reasoning and learning, and a system to help first responders by providing driving assistance and presenting of mission-critical information. Also it is shown how reliable real-time learning can help to increase autonomy of intelligent agents beyond the current state-of-the-art. Here, theoretical and practical comparisons with established frameworks and specific techniques such as Q-Learning are made, and it is shown that ONA does also work in non-Markovian environments where Q-Learning cannot be applied. Some of the reasoner's capabilities are also demonstrated on real robotic hardware. The experiments there show combining learning knowledge at runtime with the utilization of only partly complete mission-related background knowledge given by the designer, allowing the agent to perform a complex task from an only minimal mission specification which does not include learnable details. Overall, ONA is suitable for autonomous agents as it combines, in a single technique, the strengths of behavior learning, which is usually captured by Reinforcement Learning, and means-end reasoning (such as Belief-Desire-Intention models with planner) to effectively utilize knowledge expressed by a designer. / Computer and Information Science

Abstrakt Syftet med denna studie är att undersöka faktorer som kan påverka elevers matematiska resonemang. Detta gjordes genom att undersöka vilken typ av resonemang elever i årskurs 9 använde när de löste olika uppgifter och även elevernas uppfattningar om matematik. Teoretiska perspektiv för studien är baserad på Lithners teoretiska ramverk som skiljer på två huvudtyper av matematiska resonemang: den första är imitativt resonemang, där eleven imiterar lösningsalgoritmer som hen känner till, och kreativt matematiskt resonemang, där eleven skapar en lösning för att lösa en uppgift. Metoderna som användes i studien var observation och ostrukturerade intervjuer. Resultaten visar att elever använde imitativa resonemang för att lösa uppgifter och sällan använde kreativa matematiska resonemang. Det är rimligt att anta att arbetssättet hade en roll för vilken typ av matematiska resonemang elever använde för att lösa olika övningsuppgifter. Resultatet tyder även på att elevens träning på att lösa uppgifter som krävde användning av kreativt matematiskt resonemang påverkade betyget eleven fick i det skriftliga provet. Eleverna indikerade uppfattningar om att uppgifter som krävde användning av kreativa matematiska resonemang för att lösas var svåra, särskilt svåra att förstå. De slutsatser som dras är att antalet uppgifter som kräver användning av kreativt matematiskt resonemang för att lösas bör utökas och eleverna behöver utveckla sitt matematiska språk.

No description available.

This is a qualitative research that draws Gee‟s Discourse analysis to understand how learners communicate their mathematical reasoning in a multilingual classroom in South Africa. The study involved a Grade 11 class of 25 learners in a township school East of Johannesburg. The research method used was a case study. Data was collected using classroom observations, and document analysis. The study has shown that learners communicate their mathematics reasoning up to a certain level. The way learners communicated their mathematical reasoning depended on the activities that were given by the textbook being used in the classroom, and the questions which the teacher asked during the lessons. From the findings of the study, recommendations were made: the assessment of how learners communicate their mathematical reasoning should have a basis, say the curriculum. If the curriculum states the level of mathematical reasoning which the learners at Grade 11 must reach, then the teacher will have to probe the learners for higher reasoning; mathematics classroom textbooks should be designed to enable learners communicate their mathematical reasoning. The teacher should ask learners questions that require learners to communicate their mathematical reasoning.

Ever since artifacts have been produced, improving them has been a common human activity. Improving an artifact refers to modifying it such that it will be either easier to produce, or easier to use, or easier to fix, or easier to maintain, and so on. In all of these cases, "easier" means fewer resources are required for those processes. While 'resources' is a general measure, which can ultimately be expressed by some measure of cost (such as time or money), we believe that at the core of many improvements is the notion of reduction of complexity, or in other words, simplification. This talk presents our research on performing design simplification using analogical reasoning. We first define the simplification problem as the problem of reducing the complexity of an artefact from a given point of view. We propose that a point of view from which the complexity of an artefact can be measured consists of a context, an aspect and a measure. Next, we describe an approach to solving simplification problems by goal-directed analogical reasoning, as our implementation of this approach. Finally, we present some experimental results obtained with the system. The research presented in this dissertation is significant as it focuses on the intersection of a number of important, active research areas - analogical reasoning, functional representation, functional reasoning, simplification, and the general area of AI in Design.

Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS. / Collaborative Unmanned Aircraft Systems (CUAS)