International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / We report on the initial design and development of a prototype computer-mediated response system, the Fault Induced Document Officer (FIDO), at the UC Berkeley Center for EUV Astrophysics (CEA) Extreme Ultraviolet Explorer project (EUVE). Typical 24x7 staffed spacecraft operations use highly skilled expert teams to monitor current ground systems and spacecraft state for responding to anomalous ground system and spacecraft conditions. Response to ground system error messages and spacecraft anomalies is based on knowledge of nominal component behavior and the evaluation of relevant telemetry by the team. This type of human-mediated operation is being replaced by an intelligent software system to reduce costs and to increase performance and reliability. FIDO is a prototype software application that will provide automated retrieval and display of documentation for operations staff. Initially, FIDO will be applied for ground systems. Later implementations of FIDO will target spacecraft systems. FIDO is intended to provide system state summary, links to relevant documentation, and suggestions for operator responses to error messages. FIDO will provide the operator with near realtime expert assistance and access to necessary information. This configuration should allow the resolution of many anomalies without the need for on-site intervention by a skilled controller or expert.

Causal reasoning is an important part of scientific thinking, and even young children can use causes to explain what they observe and to make predictions. Weight is an interesting type of cause because it is a nonobvious property, and thus is not readily observable. The first research question of my dissertation examines when children use this property as a cause. In Study 1, 2- to 5-year-old children completed three different tasks in which they had to use weight to produce effects; an object displacement task, a balance-scale task, and a tower building task. The children’s use of weight improved with age, with 4- and 5-year-olds showing above-chance performance on all tasks. The younger children’s performance was more variable across tasks, suggesting that the complexity of the problem may influence their use of weight. The second research question is whether children’s use of weight as a cause can be improved. To examine this question, I varied the pedagogical cues that children received on the balance scale task from Study 1. The results of Study 2, indicate that highlighting the different effects of the heavy and light objects improves 3- to 4-year-olds’ performance. However, the results of Study 3 indicate that 2-year-olds did not benefit from even multiple pedagogical cues (contrasting the different effects and providing a verbal description to highlight the weight difference). To sum up, children at age 4 and above showed a general ability to use weight in across causal reasoning tasks. Whether children’s understanding of weight could be improved depended on their age and the cues given.

In my dissertation, I argue that rationality, for real humans, is best understood as a strategy for communication and interacting in a social environment. In particular, I argue that humans are rational to the extent that they are able to understand and be understood by others, to the extent that they can give and accept reasons and explanations. This raises a pair of questions. The first concerns the source of the norms for giving and accepting reasons. The second is why we should accept and follow these norms if they are not guaranteed to preserve truth or optimize outcomes. I address the first question by arguing that these norms function as constraints on our imaginations, on the ways in which we can think about or understand the world. This goes beyond the traditional view that these norms govern acceptable inferences. Rather, I argue, the more significant function of these norms is to govern the structure of our reasoning in the sense of guiding considerations about the relevance and form of our understandings of situations. This suggests an answer to the second question. We ought to accept these norms because they are self-confirming. Following them allows us to communicate and interact with others who follow these same norms. In those endeavors that require interaction and coordination in a social group, being understood is frequently more important than being right. / text

What makes one more or less likely to project a novel property from an item to that item’s broader category? Research on category-based induction has documented a consistent typicality effect: typical exemplars promote stronger inferences than atypical exemplars. This work has been largely confined to categories whose central tendencies are the most typical members of the category. Experiments 1 and 2, using natural and artificial categories, showed that central tendencies have greatest induction strength even for categories that are best represented by ideal exemplars. Experiments 3-7 investigate the role of familiarity in induction. Experiments 3 and 4 directly contrast statistical averageness against familiarity through category learning procedures. Experiment 5 creates this contrast through frequency differences across stimuli. Experiments 6 and 7 investigate how the familiarity advantage found in Experiments 3-5 can be modified through fluency manipulations, independent of actual experience. Taken together, these studies suggest that category-based induction is driven largely by a familiarity heuristic. / text

The research described in this thesis stems from ROBOCARE1, a three year research project aimed at developing software and robotic technology for providing intelligent support for elderly people. This thesis deals with two problems which have emerged in the course of the project’s development: Multi-agent coordination with scarce resources. Multi-agent planning is concerned with automatically devising plans or strategies for the coordinated enactment of concurrently executing agents. A common realistic constraint in applications which require the coordination of multiple agents is the scarcity of resources for execution. In these cases, concurrency is affected by limited capacity resources, the presence of which modifies the structure of the planning/coordination problem. Specifically, the first part of this thesis tackles this problem in two contexts, namely when planning is carried out centrally (planning from first principles), and in the context of distributed multi-agent coordination. Domain modeling for scheduling applications. It is often the case that the products of research in AI problem solving are employed to develop applications for supporting human decision processes. Our experience in ROBOCARE as well as other domains has often called for the customization of prototypical software for real applications. Yet the gap between what is often a research prototype and a complete decision support system is seldom easy to bridge.The second part of the thesis focuses on this issue from the point of view of scheduling software deployment.Overall, this thesis presents three contributions within the two problems mentioned above. First, we address the issue of planning in concurrent domains in which the complexity of coordination is dominated by resource constraints. To this end, an integrated planning and scheduling architecture is presented and employed to explore the structural trademarks of multi-agent coordination problems in function of their resource-related characteristics. Theoretical and experimental analyses are carried out revealing which planning strategies are most fit for achieving plans which prescribe efficient coordination subject to scarce resources.We then turn our attention to distributed multi-agent coordination techniques (specifically, a distributed constraint optimization (DCOP) reduction of the coordination problem). Again, we consider the issue of achieving coordinated action in the presence of limited resources. Specifically, resource constraints impose n-ary relations among tasks. In addition, as the number of n-ary relations due to resource contention are exponential in the size of the problem, they cannot be extensionally represented in the DCOP representation of the coordination problem. Thus, we propose an algorithm for DCOP which retains the capability to dynamically post n-ary constraints during problem resolution in order to guarantee resource-feasible solutions. Although the approach is motivated by the multi-agent coordination problem, the algorithm is employed to realize a general architecture for n-ary constraint reasoning and posting.Third, we focus on a somewhat separate issue stemming from ROBOCARE, namely a software engineering methodology for facilitating the process of customizing scheduling components in real-world applications. This work is motivated by the strong applicative requirements of ROBOCARE. We propose a software engineering methodology specific to scheduling technology development. Our experience in ROBOCARE as well as other application scenarios has fostered the development of a modeling framework which subsumes the process of component customization for scheduling applications. The framework aims to minimize the effort involved in deploying automated reasoning technology in practise, and is grounded on the use of a modeling language for defining how domain-level concepts are grounded into elements of a technology-specific scheduling ontology.

Contemporary American Criminal Law, as represented by the American Law Institute's Model Penal Code, defines the structure of criminal offenses in a manner that establishes certain psychological processes of the defendant as necessary conditions for criminal liability. In order to convict a defendant, the state must prove all offense elements including the voluntary act and culpability requirements. These provisions involve the actor's psychological processes, but neither the exact nature of these requirements nor the relationship between them is clearly understood. Certain general defenses, such as automatism and insanity, also address the defendant's psychological processes. It has been notoriously difficult, however, to develop a satisfactory formulation of either of these defenses or of the relationship between them and the system of offense elements. This dissertation presents a conceptual framework that grounds the Model Penal Code's structure of offense elements in philosophical action theory. On this interpretation, the offense requirements that involve the defendant's psychological processes can be understood as part of an integrated attempt to establish the criminal law as a behavior guiding institution that is uniquely appropriate to those who have the capacity to direct their conduct through a process of practical reasoning. The key offense requirements are designed to limit criminal liability to those behaviors that are appropriately attributed to the offender as a practical reasoner. Certain general defenses, including insanity, exculpate defendants when their behavior is not attributable to them as practical reasoners as a result of certain types of impairment that are not addressed by the offense elements. This conceptual framework provides a consistent interpretation of the relevant offense elements and defenses as part of an integrated system that limits criminal liability to those acts that are appropriately attributable to the defendant in his capacity as a practical reasoner. In addition, this dissertation contends that this system reflects a defensible conception of personal responsibility.

The purpose of this study was to determine whether fourth-, sixth-, and eighth-grade students used rational number reasoning to solve comparison of area tasks, and whether the tendency to use such reasoning increased with grade level. The areas to be compared were not similar and therefore, could not directly be compared in a straightforward manner. The most viable solution involved comparing the part-whole relationships inherent in the tasks. Rational numbers in the form of fractional terms could be used to express the part-whole relationships. The use of fractional terms provided a means for students to express the areas to be compared in an abstract manner and thus free themselves from the perceptual aspects of the tasks. The study examined how students solve unique problems in a familiar context where rational number knowledge could be applied. It also noted the effect of introducing fraction symbols into the tasks after students had indicated how they would solve the problems without any reference to fractions. Data were gathered through individual task-based interviews which consisted of 21 tasks, conducted with 36 elementary and junior high school students (12 students each in the fourth, sixth, and eighth grades). Each interview was video and audio taped to provide a record of the students' behavioral and verbal responses. The student responses were analyzed to determine the strategies the students used to solve the comparison of area tasks. The student responses were classified into 11 categories of strategies. There were four Part-Whole Categories, one Part-Whole/Direct Comparison Combination category and six Direct Comparison categories. The results of the study indicate that the development of rational number instruction should include: learning sequences which take students beyond the learning of a set of fraction concepts and skills, attention to the interaction of learning and the visual aspects of instructional models, and the careful inclusion of different types of fractions and other rational number task variables. This study supports the current national developments in curriculum and evaluation standards for mathematics instruction which stress the ability of students to problem solve, communicate, and reason.

When evaluating a situation that results in harm, it is critical to consider how a person’s prior intention may have been causally responsible for the action that resulted in the harmful outcome. This thesis examined children’s developing understanding of intentional causation in reasoning about harmful outcomes, and the relation between this understanding and mental-state reasoning. Four-, 6-, and 8-year-old children, and adults, were told eight stories in which characters’ actions resulted in harmful outcomes. Story types differed in how the actions that resulted in harm were causally linked to their prior intentions such that: (1) characters wanted to, intended to, and did perform a harmful act; (2) they wanted and intended to perform a harmful act, but instead, accidentally brought about the harmful outcome; (3) they wanted and intended to perform a harmful act, then changed their mind, but accidentally brought about the harmful outcome; (4) they did not want or intend to harm, but accidentally brought about a harmful outcome. Participants were asked to judge the characters’ intentions, make punishment judgments, and justify their responses. Additionally, children were given first- and second-order false-belief tasks, commonly used to assess mental-state reasoning. The results indicated that intention judgment accuracy improved with age. However, all age groups had difficulty evaluating the intention in the deviant causal chain scenario (Searle, 1983), in which the causal link between intention and action was broken but a harmful intention was maintained. Further, the results showed a developmental pattern in children’s punishment judgments based on their understanding of intentional causation, although the adults’ performance did not follow the same pattern. Also, younger children referred to the characters’ intentions less frequently in their justifications of their punishment judgments. The results also revealed a relation between belief-state reasoning and intentional-causation reasoning in scenarios that did not involve, or no longer involved, an intention to harm. Further, reasoning about intentional causation was related to higher-level understanding of mental states. The implications of these findings in clarifying and adding to previous research on the development of understanding of intentional causation and intentions in moral reasoning are discussed.

The nature and competitiveness of the modern software development industry demands that software engineers be able to make accurate and consistent software cost estimates. Traditionally software cost estimates have been derived with algorithmic cost estimation models such as COCOMO and Function Point Analysis. However, researchers have shown that existing software cost estimation techniques fail to produce accurate and consistent software cost estimates. Improving the reliability of software cost estimates would facilitate cost savings, improved delivery time and better quality software developments. To this end, considerable research has been conducted into finding alternative software cost estimation models that are able produce better quality software cost estimates. Researchers have suggested a number of alternative models to this problem area. One of the most promising alternatives is Case-Based Reasoning (CBR), which is a machine learning paradigm that makes use of past experiences to solve new problems. CBR has been proposed as a solution since it is highly suited to weak theory domains, where the relationships between cause and effect are not well understood. The aim of this research was to determine the applicability of CBR to software cost estimation. This was accomplished in part through the thorough investigation of the theoretical and practical background to CBR, software cost estimation and current research on CBR applied to software cost estimation. This provided a foundation for the development of experimental CBR software cost estimation models with which an empirical evaluation of this technology applied to software cost estimation was performed. In addition, several regression models were developed, against which the effectiveness of the CBR system could be evaluated. The architecture of the CBR models developed, facilitated the investigation of the effects of case granularity on the quality of the results obtained from them. Traditionally researchers into this field have made use of poorly populated datasets, which did not accurately reflect the true nature of the software development industry. However, for the purposes of this research an extensive database of 300 software development projects was obtained on which these experiments were performed. The results obtained through experimentation indicated that the CBR models that were developed, performed similarly and in some cases better than those developed by other researchers. In terms of the quality of the results produced, the best CBR model was able to significantly outperform the estimates produced by the best regression model. Also, the effects of increased case granularity was shown to result in better quality predictions made by the CBR models. These promising results experimentally validated CBR as an applicable software cost estimation technique. In addition, it was shown that CBR has a number of methodological advantages over traditional cost estimation techniques. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2002.

According to National Highway Traffic Safety Administration’s (NHTSA) official reports, many traffic accidents have been caused due to drivers’ drowsiness. Previous work based on computer vision techniques achieved drowsiness detection, usually with special hardware that depended on laboratory environments. To overcome limitations of these approaches, a natural light based surveillance system is proposed. The system achieves drowsiness detection in three stages: face segmentation, drowsiness feature extraction and classification. To segment faces, a simplified skin colour model is developed to compute colour distance maps from original facial images. Candidate faces are located using colour distance maps in conjunction with centres of gravity of individual faces. Gegenbauer features are then applied to capture shape information that is related to drowsiness. The computation of these features is based on moments derived from coefficients of Gegenbauer polynomials. To detect the behaviour of a subject, image sequences of his/her face are classified into drowsy and nondrowsy states by a Hidden Markov Model using Gegenbauer features. A sequence is classified as drowsy if the number of drowsy states in the Hidden Markov Model reaches a pre-defined threshold. To evaluate the proposed system, experiments are conducted using 65 video clips that contained a mixture of 54 drowsy and 11 non-drowsy behaviours. The proposed system detected 47 drowsy behaviours from these video clips successfully, and thus resulting in a detection rate of 87%. This proposed system is independent of infrared illuminators that were found to be unreliable in previous systems. Furthermore, the new system deploys multiple facial features and presents a more accurate description of drowsiness rather than a single facial feature proposed by previous authors.