Shared resource control between human and computer

The advantages of an AI system of actively monitoring human control of a shared resource (such as a telerobotic manipulator) are presented. A system is described in which a simple AI planning program gains efficiency by monitoring human actions and recognizing when the actions cause a change in the system's assumed state of the world. This enables the planner to recognize when an interaction occurs between human actions and system goals, and allows maintenance of an up-to-date knowledge of the state of the world and thus informs the operator when human action would undo a goal achieved by the system, when an action would render a system goal unachievable, and efficiently replans the establishment of goals after human intervention

Massively-parallel marker-passing in semantic networks

AbstractOne approach to using the information available in a semantic network is the use of marker-passing algorithms, which propagate information through the network to determine relationships between objects. One of the primary arguments in favor of these algorithms are their ability to be implemented in parallel. Despite this, most implementations have been serial and have only sometimes gone so far as to simulate parallelism. In this paper the marker-passing approach is presented. An actual parallel implementation which shows that such programs can be written on commercially available massively parallel machines is also presented

The Syzygy Surfer: Creative technology for the World Wide Web

Conference paper given at WebSci11.This paper discusses our development of a new Web engine, the Syzygy Surfer, which aims to induce a search/browsing experience that is more creative than traditional search. We do this by purposefully combining the ambiguity of natural language with the precision of Semantic Web technologies. Here we set out the framework for our investigation and discuss the context and background ideas that are informing the research. The paper offers some preliminary examples taken from our work in progress on the device and suggests the way ahead for future developments and applications

Massively parallel support for a case-based planning system

Case-based planning (CBP), a kind of case-based reasoning, is a technique in which previously generated plans (cases) are stored in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over generative planning, in which a new plan is produced from scratch. CBP thus offers a potential (heuristic) mechanism for handling intractable problems. One drawback of CBP systems has been the need for a highly structured memory to reduce retrieval times. This approach requires significant domain engineering and complex memory indexing schemes to make these planners efficient. In contrast, our CBP system, CaPER, uses a massively parallel frame-based AI language (PARKA) and can do extremely fast retrieval of complex cases from a large, unindexed memory. The ability to do fast, frequent retrievals has many advantages: indexing is unnecessary; very large case bases can be used; memory can be probed in numerous alternate ways; and queries can be made at several levels, allowing more specific retrieval of stored plans that better fit the target problem with less adaptation. In this paper we describe CaPER's case retrieval techniques and some experimental results showing its good performance, even on large case bases

The Extended Mind and Network-Enabled Cognition

In thinking about the transformative potential of network technologies with respect to human cognition, it is common to see network resources as playing a largely assistive or augmentative role. In this paper we propose a somewhat more radical vision. We suggest that the informational and technological elements of a network system can, at times, constitute part of the material supervenience base for a human agent’s mental states and processes. This thesis (called the thesis of network-enabled cognition) draws its inspiration from the notion of the extended mind that has been propounded in the philosophical and cognitive science literature. Our basic claim is that network systems can do more than just augment cognition; they can also constitute part of the physical machinery that makes mind and cognition mechanistically possible. In evaluating this hypothesis, we identify a number of issues that seem to undermine the extent to which contemporary network systems, most notably the World Wide Web, can legitimately feature as part of an environmentally-extended cognitive system. Specific problems include the reliability and resilience of network-enabled devices, the accessibility of online information content, and the extent to which network-derived information is treated in the same way as information retrieved from biological memory. We argue that these apparent shortfalls do not necessarily merit the wholesale rejection of the network-enabled cognition thesis; rather, they point to the limits of the current state-of-the-art and identify the targets of many ongoing research initiatives in the network and information sciences. In addition to highlighting the importance of current research and technology development efforts, the thesis of network-enabled cognition also suggests a number of areas for future research. These include the formation and maintenance of online trust relationships, the subjective assessment of information credibility and the long-term impact of network access on human psychological and cognitive functioning. The nascent discipline of web science is, we suggest, suitably placed to begin an exploration of these issues

Exploiting Class Learnability in Noisy Data

In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged Web data. Noisy labels occur frequently in data sets harvested via these means, sometimes resulting in entire classes of data on which learned classifiers generalize poorly. For real world applications, we argue that it can be beneficial to avoid training on such classes entirely. In this work, we aim to explore the classes in a given data set, and guide supervised training to spend time on a class proportional to its learnability. By focusing the training process, we aim to improve model generalization on classes with a strong signal. To that end, we develop an online algorithm that works in conjunction with classifier and training algorithm, iteratively selecting training data for the classifier based on how well it appears to generalize on each class. Testing our approach on a variety of data sets, we show our algorithm learns to focus on classes for which the model has low generalization error relative to strong baselines, yielding a classifier with good performance on learnable classes.Comment: Accepted to AAAI 201

Semantic Web Techniques to Support Interoperability in Distributed Networked Environments

We explore two Semantic Web techniques arising from ITA research into semantic alignment and interoperability in distributed networks. The first is POAF (Portable Ontology Aligned Fragments) which addresses issues relating to the portability and usage of ontology alignments. POAF uses an ontology fragmentation strategy to achieve portability, and enables subsequent usage through a form of automated ontology modularization. The second technique, SWEDER (Semantic Wrapping of Existing Data sources with Embedded Rules), is grounded in the creation of lightweight ontologies to semantically wrap existing data sources, to facilitate rapid semantic integration through representational homogeneity. The semantic integration is achieved through the creation of context ontologies which define the integrations and provide a portable definition of the integration rules in the form of embedded SPARQL construct clauses. These two Semantic Web techniques address important practical issues relevant to the potential future adoption of ontologies in distributed network environments

Formalizing behavior-based planning for nonholonomic robots

In this paper we present a formalization of behavior-based planning for nonholonomic robotic systems. This work provides a framework that integrates features of reactive planning models with modern control-theory-based robotic approaches in the area of path-planning for nonholonomic robots. In particular, we introduce a motion description language, MDLe, that provides a formal basis for robot programming using behaviors, and at the same time permits incorporation of kinematic models of robots given in the form of differential equations. The structure of the language MDLe is such as to allow descriptions of triggers (generated by sensors) in the language. Feedback and feedforward control laws are selected and executed by the triggering events. We demonstrate the use of MDLe in the area of motion planning for nonholonomic robots. Such models impose limitations on stabilizability via smooth feedback, i.e. piecing together open loop and closed loop trajectories becomes essential in these circumstances, and MDLe enables one to describe such piecing together in a systematic manner. A reactive planner using the formalism of the paper is described. We demonstrate obstacle avoidance with limited range sensors as a test of this planner.

Semantic Integration Portal

The Semantic Integration Portal is a demonstration of the potential capabilities of Semantic Web applications in a knowledge-rich context. Source data is taken from different online terrorist incident aggregators and marked up according to ontologies specific to those domains. Unlike other semantic web techniques, which scrape the internet for raw data and then mark-up against a standard ontology, the approach here is to allow each data source to have its own domain-specific ontology. This allows the data producers the opportunity to mark up their data in their own way, producing RDF data according to their own ontologies without the need to conform to a standard. A variety of semantic integration techniques can then be applied to these ontologies, both automatic and interactive, allowing data from both sets to be viewed in a suitable application, in this case the mspace browser. Future iterations of the semantic integration portal aim to introduce more automated ontology-mapping techniques, aligning data from a variety of diverse sources with less need for human intervention

Design Index for Deep Neural Networks

AbstractIn this paper, we propose a Deep Neural Networks (DNN) Design Index which would aid a DNN designer during the designing phase of DNNs. We study the designing aspect of DNNs from model-specific and data-specific perspectives with focus on three performance metrics: training time, training error and, validation error. We use a simple example to illustrate the significance of the DNN design index. To validate it, we calculate the design indices for four benchmark problems. This is an elementary work aimed at setting a direction for creating design indices pertaining to deep learning