25,768 research outputs found
A Bio-Logical Theory of Animal Learning
This article provides the foundation for a new predictive theory of animal learning that is based upon a simple logical model. The knowledge of experimental subjects at a given time is described using logical equations. These logical equations are then used to predict a subject’s response when presented with a known or a previously unknown situation. This new theory suc- cessfully anticipates phenomena that existing theories predict, as well as phenomena that they cannot. It provides a theoretical account for phenomena that are beyond the domain of existing models, such as extinction and the detection of novelty, from which “external inhibition” can be explained. Examples of the methods applied to make predictions are given using previously published results. The present theory proposes a new way to envision the minimal functions of the nervous system, and provides possible new insights into the way that brains ultimately create and use knowledge about the world
Maude: specification and programming in rewriting logic
Maude is a high-level language and a high-performance system supporting executable specification and declarative programming in rewriting logic. Since rewriting logic contains equational logic, Maude also supports equational specification and programming in its sublanguage of functional modules and theories. The underlying equational logic chosen for Maude is membership equational logic, that has sorts, subsorts, operator overloading, and partiality definable by membership and equality conditions. Rewriting logic is reflective, in the sense of being able to express its own metalevel at the object level. Reflection is systematically exploited in Maude endowing the language with powerful metaprogramming capabilities, including both user-definable module operations and declarative strategies to guide the deduction process. This paper explains and illustrates with examples the main concepts of Maude's language design, including its underlying logic, functional, system and object-oriented modules, as well as parameterized modules, theories, and views. We also explain how Maude supports reflection, metaprogramming and internal strategies. The paper outlines the principles underlying the Maude system implementation, including its semicompilation techniques. We conclude with some remarks about applications, work on a formal environment for Maude, and a mobile language extension of Maude
Mathematical Basis for Physical Inference
While the axiomatic introduction of a probability distribution over a space
is common, its use for making predictions, using physical theories and prior
knowledge, suffers from a lack of formalization. We propose to introduce, in
the space of all probability distributions, two operations, the OR and the AND
operation, that bring to the space the necessary structure for making
inferences on possible values of physical parameters. While physical theories
are often asumed to be analytical, we argue that consistent inference needs to
replace analytical theories by probability distributions over the parameter
space, and we propose a systematic way of obtaining such "theoretical
correlations", using the OR operation on the results of physical experiments.
Predicting the outcome of an experiment or solving "inverse problems" are then
examples of the use of the AND operation. This leads to a simple and complete
mathematical basis for general physical inference.Comment: 24 pages, 4 figure
The Inflation Technique for Causal Inference with Latent Variables
The problem of causal inference is to determine if a given probability
distribution on observed variables is compatible with some causal structure.
The difficult case is when the causal structure includes latent variables. We
here introduce the for tackling this problem. An
inflation of a causal structure is a new causal structure that can contain
multiple copies of each of the original variables, but where the ancestry of
each copy mirrors that of the original. To every distribution of the observed
variables that is compatible with the original causal structure, we assign a
family of marginal distributions on certain subsets of the copies that are
compatible with the inflated causal structure. It follows that compatibility
constraints for the inflation can be translated into compatibility constraints
for the original causal structure. Even if the constraints at the level of
inflation are weak, such as observable statistical independences implied by
disjoint causal ancestry, the translated constraints can be strong. We apply
this method to derive new inequalities whose violation by a distribution
witnesses that distribution's incompatibility with the causal structure (of
which Bell inequalities and Pearl's instrumental inequality are prominent
examples). We describe an algorithm for deriving all such inequalities for the
original causal structure that follow from ancestral independences in the
inflation. For three observed binary variables with pairwise common causes, it
yields inequalities that are stronger in at least some aspects than those
obtainable by existing methods. We also describe an algorithm that derives a
weaker set of inequalities but is more efficient. Finally, we discuss which
inflations are such that the inequalities one obtains from them remain valid
even for quantum (and post-quantum) generalizations of the notion of a causal
model.Comment: Minor final corrections, updated to match the published version as
closely as possibl
Stochastic simulations of conditional states of partially observed systems, quantum and classical
In a partially observed quantum or classical system the information that we
cannot access results in our description of the system becoming mixed even if
we have perfect initial knowledge. That is, if the system is quantum the
conditional state will be given by a state matrix and if classical
the conditional state will be given by a probability distribution
where is the result of the measurement. Thus to determine the evolution of
this conditional state under continuous-in-time monitoring requires an
expensive numerical calculation. In this paper we demonstrating a numerical
technique based on linear measurement theory that allows us to determine the
conditional state using only pure states. That is, our technique reduces the
problem size by a factor of , the number of basis states for the system.
Furthermore we show that our method can be applied to joint classical and
quantum systems as arises in modeling realistic measurement.Comment: 16 pages, 11 figure
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