5,891 research outputs found
Complexity over Uncertainty in Generalized Representational\ud Information Theory (GRIT): A Structure-Sensitive General\ud Theory of Information
What is information? Although researchers have used the construct of information liberally to refer to pertinent forms of domain-specific knowledge, relatively few have attempted to generalize and standardize the construct. Shannon and Weaver(1949)offered the best known attempt at a quantitative generalization in terms of the number of discriminable symbols required to communicate the state of an uncertain event. This idea, although useful, does not capture the role that structural context and complexity play in the process of understanding an event as being informative. In what follows, we discuss the limitations and futility of any generalization (and particularly, Shannonās) that is not based on the way that agents extract patterns from their environment. More specifically, we shall argue that agent concept acquisition, and not the communication of\ud
states of uncertainty, lie at the heart of generalized information, and that the best way of characterizing information is via the relative gain or loss in concept complexity that is experienced when a set of known entities (regardless of their nature or domain of origin) changes. We show that Representational Information Theory perfectly captures this crucial aspect of information and conclude with the first generalization of Representational Information Theory (RIT) to continuous domains
Representational information: a new general notion and measure\ud of information
In what follows, we introduce the notion of representational information (information conveyed by sets of dimensionally deļ¬ned objects about their superset of origin) as well as an\ud
original deterministic mathematical framework for its analysis and measurement. The framework, based in part on categorical invariance theory [Vigo, 2009], uniļ¬es three key constructsof universal science ā invariance, complexity, and information. From this uniļ¬cation we deļ¬ne the amount of information that a well-deļ¬ned set of objects R carries about its ļ¬nite superset of origin S, as the rate of change in the structural complexity of S (as determined by its degree of categorical invariance), whenever the objects in R are removed from the set S. The measure captures deterministically the signiļ¬cant role that context and category structure play in determining the relative quantity and quality of subjective information conveyed by particular objects in multi-object stimuli
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Multiscale Fields of Patterns
We describe a framework for defining high-order image models that can be used
in a variety of applications. The approach involves modeling local patterns in
a multiscale representation of an image. Local properties of a coarsened image
reflect non-local properties of the original image. In the case of binary
images local properties are defined by the binary patterns observed over small
neighborhoods around each pixel. With the multiscale representation we capture
the frequency of patterns observed at different scales of resolution. This
framework leads to expressive priors that depend on a relatively small number
of parameters. For inference and learning we use an MCMC method for block
sampling with very large blocks. We evaluate the approach with two example
applications. One involves contour detection. The other involves binary
segmentation.Comment: In NIPS 201
How properties hold together in Substances
This article aims to clarify how aspects of current chemical understanding relate to some important contemporary problems of philosophy. The first section points out that the long-running philosophical debates concerning how properties stay together in substances have neglected the important topic of structure-determining closure. The second part describes several chemically-important types of closure and the third part shows how such closures ground the properties of chemical substances. The fourth section introduces current discussions of structural realism (SR) and contextual emergence: the final sections reconsider the coherence of the properties of substances and concludes that recognition that structures qualify as determinants of specific outcomesāas ācausesā as that designation is used in Standard Englishāclarifies how properties stay together in chemical entities, and by analogy, how characteristics cohere in ordinary items
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