29133778
Toward a real-time model-based training system
This article describes the development of a real-time model-based training system that provides adaptive ''over-the-shoulder'' (OTS) instructions to trainees as they learn to perform an Anti-Air Warfare Coordinator (AAWC) task. The long-term goal is to develop a system that will provide real-time instructional materials based on learners' actions, so that eventually the initial set of instructions on a task can be strengthened, complemented, or overridden at different stages of training. The training system is based on the ACT-R architecture, which serves as the theoretical background for the cognitive model that monitors the learning process of the trainee. An experiment was designed to study the impact of OTS instructions on learning. Results showed that while OTS instructions facilitated short-term learning, (a) they took time away from the processing of current information, (b) their effects tended to decay rapidly in initial stages of training, and (c) their effects on training diminished when the OTS instructions were proceduralized in later stages of training. A cognitive model that learned from both the upfront and OTS instructions was created and provided good fits to the learning and performance data collected from human participants. Our results suggest that to fully capture the symbiotic performance between humans and intelligent training systems, it is important to closely monitor the learning process of the trainee so that instructional interventions can be delivered effectively at different stages of training. We proposed that such a flexible system can be developed based on an adaptive cognitive model that provides real-time predictions on learning and performance.
12350992
A Rational Analysis of Categorization
Abstract A rational analysis tries to predict the behavior of a cognitive system from the assumption it is optimized to the environment. An iterative categorization algorithm has been developed which attempts to get optimal Bayesian estimates of the probabilities that objects will display various features. A prior probability is estimated that an object comes from a category and combined with conditional probabilities of displaying features if the object comes from the category. Separate Bayesian treatments are offered for the cases of discrete and continuous dimensions. The resulting algorithm is efficient, works well in the case of large data bases, and replicates the full range of empirical literature in human categorization.
6447408
Tuning of Search of the Problem Space for Geometry Proofs
In planning a proof, a student searches through a space of inferences leading forward from the givens of the problem and backward from the to-be-proven statement. One dimension of growth of expertise is that students become more tuned in the search of this problem space. This can be shown to result from the application of various learning operators to production embodiments of the inference rules. Rules are evaluated after the solution of a problem according to whether they led to or led away from the solution. Rules that contributed to a solution are strengthened and an attempt is made to formulate general versions of these rules that will apply in other situations. Rules that led away from the solution are weakened and a discrimination process is evoked to try to add features to the rules that will try to restrict them to the correct circumstances of application. Composition is a learning process that collapses successful sequences of rule operations into single macro-rule productions. There is also a process that converts the backward reasoning rules formed by composition into forward reasoning rules. The effect of these learning processes is to put into production conditions tests for problem features that are heunstically predictive of the rule's success.
8794180
The generality/specificity of expertise in scientific reasoning
Previous research on scientific reasoning has shown that it involves a diverse set of skills. Yet, little is known about generality or domain specificity of those skills, an important issue in theories of expertise and in attempts to automate scientific reasoning skills. We present a study designed to test what kinds of skills psychologists actually use in designing and interpreting experiments and contrast expertise within a particular research area with general expertise at designing and interpreting experiments. The results suggest that psychologists use many domain-general skills in their experimentation and that bright and motivated Carnegie Mellon undergraduates are missing many of these skills. We believe that these results have important implications for psychological and artificial intelligence models of expertise, as well as educational implications in the domain of science instruction.
195901903
Integrating analogical mapping and general problem solving: the path-mapping theory
This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects' incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.
14213624
Achieving Efficient and Cognitively Plausible Learning in Backgammon
Traditionally, computer applications to game domains have taken a brute-force approach, relying on sheer computational power to overcome the complexity of the domain. Although many of these programs have been quite successful, it is interesting to note that humans can still perform extremely well against them. Thus we are compelled to ask, if no human could match the computational power of most of these programs, are there methods for learning and performance in game domains that more closely reflect human cognition? In response to this question, this paper attempts to model how humans learn and play games by developing a Backgammon-playing algorithm based on cognition. Analysis of this algorithm shows that it is efficient and commensurate with human abilities suggesting that it provides a cognitively plausible theory of learning in Backgammon.
2301308
Recovering from Distributable Thread Failures with Assured Timeliness in Real-Time Distributed Systems
We consider the problem of recovering from failures of distributable threads with assured timeliness. When a node hosting a portion of a distributable thread fails, it causes orphans - i.e., thread segments that are disconnected from the thread's root. We consider a termination model for recovering from such failures, where the orphans must be detected and aborted, and failure-exception notification must be delivered to the farthest, contiguous surviving thread segment for resuming thread execution. We present a realtime scheduling algorithm called AUA, and a distributable thread integrity protocol called TP-TR. We show that AUA and TP-TR bound the orphan cleanup and recovery time, thereby bounding thread starvation durations, and maximize the total thread accrued timeliness utility. We implement AUA and TP-TR in a real-time middleware that supports distributable threads. Our experimental studies with the implementation validate the algorithm/protocol's time-bounded recovery property and confirm their effectiveness
529922
Mace: language support for building distributed systems
Building distributed systems is particularly difficult because of the asynchronous, heterogeneous, and failure-prone environment where these systemsmust run. Tools for building distributed systems must strike a compromise between reducing programmer effort and increasing system efficiency. We present Mace, a C++ language extension and source-to-source compiler that translates a concise but expressive distributed system specification into a C++ implementation. Mace overcomes the limitations of low-level languages by providing a unified framework for networking and event handling, and the limitations of high-level languages by allowing programmers to write program components in a controlled and structured manner in C++. By imposing structure and restrictions on how applications can be written, Mace supports debugging at a higher level, including support for efficient model checking and causal-path debugging. Because Mace programs compile to C++, programmers can use existing C++ tools, including optimizers, profilers, and debuggers to analyze their systems.
19285984
Surface Based Hypothesis Verification in Intensity Images Using Geometric and Appearance Data
In this paper we discuss current work concerning Appearance-based and CAD-based vision; two opposing vision strategies. CAD-based vision is geometry based, reliant on having complete object centred models. Appearance-based vision builds view dependent models from training images. Existing CAD-based vision systems that work with intensity images have all used one and zero dimensional features, for example lines, arcs, points and corners. We describe a system we have developed for combining these two strategies. Geometric models are extracted from a commercial CAD library of industry standard parts. Surface appearance characteristics are then learnt automatically by observing actual object instances. This information is combined with geometric information and is used in hypothesis evaluation. This augmented description improves the systems robustness to texture, specularities and other artifacts which are hard to model with geometry alone, whilst maintaining the advantages of a geometric description.
46723192
On the role of metaphor and language in design of third party payments in eBanking: Usability and quality
This paper describes results of a usability study of contrasting user-interface designs for Internet Banking (eBanking). Two specific interface metaphors were compared in the first experiment, linear form filling and array editing interaction modes. Terminology in the interaction dialogue was compared in the second experiment, using typical banking language and a generic, plain language interface. This research aimed to perform usability evaluation and comparison of the alternative interface designs to illuminate the development of new eBanking services. This research involved sixty-one participants (Internet users and customers of the involved Bank) exploring the designs in controlled experiments involving hands-on experience. Banks are interested in ensuring their eBanking services are highly customer-centric and that the interface matches customer expectations in order to drive customers towards this lower cost channel. The results of the first experiment (N=32, where N indicates the number of participants in the cohort) concluded that the simple form-filling metaphor, taken from the traditional paper-based procedure, was generally more usable than a Spreadsheet metaphor. In the second experiment (N=29), it was found that although banking terminology was not completely understood across the cohort, the instructional language changes did not impact significantly on usability.
17909219
Robot Free Will
We introduce the perspex machine which unifies projective geometry and Turing computation and results in a supra-Turing machine. We show two ways in which the perspex machine unifies symbolic and non-symbolic AI. Firstly, we describe concrete geometrical models that map perspexes onto neural networks, some of which perform only symbolic operations. Secondly, we describe an abstract continuum of perspex logics that includes both symbolic logics and a new class of continuous logics. We argue that an axiom in symbolic logic can be the conclusion of a perspex theorem. That is, the atoms of symbolic logic can be the conclusions of sub-atomic theorems. We argue that perspex space can be mapped onto the spacetime of the universe we inhabit. This allows us to discuss how a robot might be conscious, feel, and have free will in a deterministic, or semi-deterministic, universe. We ground the reality of our universe in existence. On a theistic point, we argue that preordination and free will are compatible. On a theological point, we argue that it is not heretical for us to give robots free will. Finally, we give a pragmatic warning as to the double-edged risks of creating robots that do, or alternatively do not, have free will.