224,558 research outputs found
Confidence-Based Reasoning with Local Temporal Formal Contexts
Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge based system for confidence reasoning.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606
Concurrent Data Structures Linked in Time
Arguments about correctness of a concurrent data structure are typically
carried out by using the notion of linearizability and specifying the
linearization points of the data structure's procedures. Such arguments are
often cumbersome as the linearization points' position in time can be dynamic
(depend on the interference, run-time values and events from the past, or even
future), non-local (appear in procedures other than the one considered), and
whose position in the execution trace may only be determined after the
considered procedure has already terminated.
In this paper we propose a new method, based on a separation-style logic, for
reasoning about concurrent objects with such linearization points. We embrace
the dynamic nature of linearization points, and encode it as part of the data
structure's auxiliary state, so that it can be dynamically modified in place by
auxiliary code, as needed when some appropriate run-time event occurs. We name
the idea linking-in-time, because it reduces temporal reasoning to spatial
reasoning. For example, modifying a temporal position of a linearization point
can be modeled similarly to a pointer update in separation logic. Furthermore,
the auxiliary state provides a convenient way to concisely express the
properties essential for reasoning about clients of such concurrent objects. We
illustrate the method by verifying (mechanically in Coq) an intricate optimal
snapshot algorithm due to Jayanti, as well as some clients
Labelled Tableaux for Distributed Temporal Logic
The distributed temporal logic DTL is a logic for reasoning about temporal properties of discrete distributed systems from the local point of view of the system's agents, which are assumed to execute sequentially and to interact by means of synchronous event sharing. We present a sound and complete labelled tableaux system for full DTL. To achieve this, we first formalize a labelled tableaux system for reasoning locally at each agent and afterwards we combine the local systems into a global one by adding rules that capture the distributed nature of DTL. We also provide examples illustrating the use of DTL and our tableaux syste
High level cognitive information processing in neural networks
Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field
A framework for qualitative reasoning about solid objects
Predicting the behavior of a qualitatively described system of solid objects requires a combination of geometrical, temporal, and physical reasoning. Methods based upon formulating and solving differential equations are not adequate for robust prediction, since the behavior of a system over extended time may be much simpler than its behavior over local time. A first-order logic, in which one can state simple physical problems and derive their solution deductively, without recourse to solving the differential equations, is discussed. This logic is substantially more expressive and powerful than any previous AI representational system in this domain
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