21,341 research outputs found
Faster Mutation Analysis via Equivalence Modulo States
Mutation analysis has many applications, such as asserting the quality of
test suites and localizing faults. One important bottleneck of mutation
analysis is scalability. The latest work explores the possibility of reducing
the redundant execution via split-stream execution. However, split-stream
execution is only able to remove redundant execution before the first mutated
statement.
In this paper we try to also reduce some of the redundant execution after the
execution of the first mutated statement. We observe that, although many
mutated statements are not equivalent, the execution result of those mutated
statements may still be equivalent to the result of the original statement. In
other words, the statements are equivalent modulo the current state.
In this paper we propose a fast mutation analysis approach, AccMut. AccMut
automatically detects the equivalence modulo states among a statement and its
mutations, then groups the statements into equivalence classes modulo states,
and uses only one process to represent each class. In this way, we can
significantly reduce the number of split processes. Our experiments show that
our approach can further accelerate mutation analysis on top of split-stream
execution with a speedup of 2.56x on average.Comment: Submitted to conferenc
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Learning-based constraints on schemata
Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models
Correspondences between Classical, Intuitionistic and Uniform Provability
Based on an analysis of the inference rules used, we provide a
characterization of the situations in which classical provability entails
intuitionistic provability. We then examine the relationship of these
derivability notions to uniform provability, a restriction of intuitionistic
provability that embodies a special form of goal-directedness. We determine,
first, the circumstances in which the former relations imply the latter. Using
this result, we identify the richest versions of the so-called abstract logic
programming languages in classical and intuitionistic logic. We then study the
reduction of classical and, derivatively, intuitionistic provability to uniform
provability via the addition to the assumption set of the negation of the
formula to be proved. Our focus here is on understanding the situations in
which this reduction is achieved. However, our discussions indicate the
structure of a proof procedure based on the reduction, a matter also considered
explicitly elsewhere.Comment: 31 page
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Elements of latent learning in a maze environment
A general purpose learning program is described which demonstrates a latent learning ability by operating at two separate goal pursuit levels. At one level are the constant, implicit goals associated with the system's memory management mechanisms. At the higher level are the dynamic, explicit behavioral goals which the implicit goals enable by manipulating memory representations to conform to the external surroundings. The program is shown to negotiate a simulated maze environment by the step-wise refinement of its latently learned experiences
A Developmental Organization for Robot Behavior
This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions
of dynamic pattern theory in which behavior
is an artifact of coupled dynamical systems
with a number of controllable degrees of freedom. In our model, the events that delineate
control decisions are derived from the pattern
of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential
knowledge gathering and representation tasks
and provide examples of the kind of developmental milestones that this approach has
already produced in our lab
Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice
Two complementary processes may be distinguished in learning a complex cognitive skill such as computer programming. First, automation offers task-specific procedures that may directly control programming behavior, second, schema acquisition offers cognitive structures that provide analogies in new problem situations. The goal of this paper is to explore what the nature of these processes can teach us for a more effective design of practice. The authors argue that conventional training strategies in elementary programming provide little guidance to the learner and offer little opportunities for mindful abstraction, which results in suboptimal automation and schema acquisition. Practice is considered to be most beneficial to learning outcomes and transfer under strict conditions, in particular, a heavy emphasis on the use of worked examples during practice and the assignment of programming tasks that demand mindful abstraction from these examples
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