139,858 research outputs found
The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques
The paper describes VEX-93 as a hybrid environment for developing
knowledge-based and problem solver systems. It integrates methods and
techniques from artificial intelligence, image and signal processing and
data analysis, which can be mixed. Two hierarchical levels of reasoning
contains an intelligent toolbox with one upper strategic inference engine
and four lower ones containing specific reasoning models: truth-functional
(rule-based), probabilistic (causal networks), fuzzy (rule-based) and
case-based (frames). There are image/signal processing-analysis capabilities
in the form of programming languages with more than one hundred primitive
functions.
User-made programs are embeddable within knowledge basis, allowing the
combination of perception and reasoning. The data analyzer toolbox contains
a collection of numerical classification, pattern recognition and ordination
methods, with neural network tools and a data base query language at
inference engines's disposal.
VEX-93 is an open system able to communicate with external computer programs
relevant to a particular application. Metaknowledge can be used for
elaborate conclusions, and man-machine interaction includes, besides windows
and graphical interfaces, acceptance of voice commands and production of
speech output.
The system was conceived for real-world applications in general domains, but
an example of a concrete medical diagnostic support system at present under
completion as a cuban-spanish project is mentioned.
Present version of VEX-93 is a huge system composed by about one and half
millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version
EEG source-space synchrostate transitions and Markov modeling in the math-gifted brain during a long-chain reasoning task
To reveal transition dynamics of global neuronal networks of mathâgifted adolescents in handling longâchain reasoning, this study explores momentary phaseâsynchronized patterns, that is, electroencephalogram (EEG) synchrostates, of intracerebral sources sustained in successive 50âms time windows during a reasoning task and nonâtask idle process. Through agglomerative hierarchical clustering for functional connectivity graphs and nested iterative cosine similarity tests, this study identifies seven general and one reasoningâspecific prototypical functional connectivity patterns from all synchrostates. Markov modeling is performed for the timeâsequential synchrostates of each trial to characterize the interstate transitions. The analysis reveals that default mode network, central executive network (CEN), dorsal attention network, cinguloâopercular network, left/right ventral frontoparietal network, and ventral visual network aperiodically recur over nonâtask or reasoning process, exhibiting high predictability in interactively reachable transitions. Compared to nonâgifted subjects, mathâgifted adolescents show higher fractional occupancy and mean duration in CEN and reasoningâtriggered transient right frontotemporal network (rFTN) in the time course of the reasoning process. Statistical modeling of Markov chains reveals that there are more selfâloops in CEN and rFTN of the mathâgifted brain, suggesting robust state durability in temporally maintaining the topological structures. Besides, mathâgifted subjects show higher probabilities in switching from the other types of synchrostates to CEN and rFTN, which represents more adaptive reconfiguration of connectivity pattern in the largeâscale cortical network for focused taskârelated information processing, which underlies superior executive functions in controlling goalâdirected persistence and high predictability of implementing imagination and creative thinking during longâchain reasoning
An Architectural Approach to Ensuring Consistency in Hierarchical Execution
Hierarchical task decomposition is a method used in many agent systems to
organize agent knowledge. This work shows how the combination of a hierarchy
and persistent assertions of knowledge can lead to difficulty in maintaining
logical consistency in asserted knowledge. We explore the problematic
consequences of persistent assumptions in the reasoning process and introduce
novel potential solutions. Having implemented one of the possible solutions,
Dynamic Hierarchical Justification, its effectiveness is demonstrated with an
empirical analysis
Minimum Energy Information Fusion in Sensor Networks
In this paper we consider how to organize the sharing of information in a
distributed network of sensors and data processors so as to provide
explanations for sensor readings with minimal expenditure of energy. We point
out that the Minimum Description Length principle provides an approach to
information fusion that is more naturally suited to energy minimization than
traditional Bayesian approaches. In addition we show that for networks
consisting of a large number of identical sensors Kohonen self-organization
provides an exact solution to the problem of combining the sensor outputs into
minimal description length explanations.Comment: postscript, 8 pages. Paper 65 in Proceedings of The 2nd International
Conference on Information Fusio
Context guided retrieval
This paper presents a hierarchical case representation that uses a context guided retrieval method The performance of this method is compared to that of a simple flat file representation using standard nearest neighbour retrieval. The data presented in this paper is more extensive than that presented in an earlier paper by the same authors. The estimation of the construction costs of light industrial warehouse buildings is used as the test domain. Each case in the system comprises approximately 400 features. These are structured into a hierarchical case representation that holds more general contextual features at its top and specific building elements at its leaves. A modified nearest neighbour retrieval algorithm is used that is guided by contextual similarity. Problems are decomposed into sub-problems and solutions recomposed into a final solution. The comparative results show that the context guided retrieval method using the hierarchical case representation is significantly more accurate than the simpler flat file representation and standard nearest neighbour retrieval
Embodied Question Answering
We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.Comment: 20 pages, 13 figures, Webpage: https://embodiedqa.org
Aircraft systems architecting: a functional-logical domain perspective
Presented is a novel framework for early systems architecture design. The framework defines data structures and algorithms that enable the systems architect to operate interactively and simultaneously in both the functional and logical domains. A prototype software tool, called AirCADia Architect, was implemented, which allowed the framework to be evaluated by practicing aircraft systems architects. The evaluation confirmed that, on the whole, the approach enables the architects to effectively express their creative ideas when synthesizing new architectures while still retaining control over the process
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