4,727 research outputs found
Decision-making and problem-solving methods in automation technology
The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming
A Learning-Based Approach to Caching in Heterogenous Small Cell Networks
A heterogenous network with base stations (BSs), small base stations (SBSs)
and users distributed according to independent Poisson point processes is
considered. SBS nodes are assumed to possess high storage capacity and to form
a distributed caching network. Popular files are stored in local caches of
SBSs, so that a user can download the desired files from one of the SBSs in its
vicinity. The offloading-loss is captured via a cost function that depends on
the random caching strategy proposed here. The popularity profile of cached
content is unknown and estimated using instantaneous demands from users within
a specified time interval. An estimate of the cost function is obtained from
which an optimal random caching strategy is devised. The training time to
achieve an difference between the achieved and optimal costs is
finite provided the user density is greater than a predefined threshold, and
scales as , where is the support of the popularity profile. A transfer
learning-based approach to improve this estimate is proposed. The training time
is reduced when the popularity profile is modeled using a parametric family of
distributions; the delay is independent of and scales linearly with the
dimension of the distribution parameter.Comment: 12 pages, 5 figures, published in IEEE Transactions on
Communications, 2016. arXiv admin note: text overlap with arXiv:1504.0363
Mathematical applications of inductive logic programming
Accepted versio
Improving QED-Tutrix by Automating the Generation of Proofs
The idea of assisting teachers with technological tools is not new.
Mathematics in general, and geometry in particular, provide interesting
challenges when developing educative softwares, both in the education and
computer science aspects. QED-Tutrix is an intelligent tutor for geometry
offering an interface to help high school students in the resolution of
demonstration problems. It focuses on specific goals: 1) to allow the student
to freely explore the problem and its figure, 2) to accept proofs elements in
any order, 3) to handle a variety of proofs, which can be customized by the
teacher, and 4) to be able to help the student at any step of the resolution of
the problem, if the need arises. The software is also independent from the
intervention of the teacher. QED-Tutrix offers an interesting approach to
geometry education, but is currently crippled by the lengthiness of the process
of implementing new problems, a task that must still be done manually.
Therefore, one of the main focuses of the QED-Tutrix' research team is to ease
the implementation of new problems, by automating the tedious step of finding
all possible proofs for a given problem. This automation must follow
fundamental constraints in order to create problems compatible with QED-Tutrix:
1) readability of the proofs, 2) accessibility at a high school level, and 3)
possibility for the teacher to modify the parameters defining the
"acceptability" of a proof. We present in this paper the result of our
preliminary exploration of possible avenues for this task. Automated theorem
proving in geometry is a widely studied subject, and various provers exist.
However, our constraints are quite specific and some adaptation would be
required to use an existing prover. We have therefore implemented a prototype
of automated prover to suit our needs. The future goal is to compare
performances and usability in our specific use-case between the existing
provers and our implementation.Comment: In Proceedings ThEdu'17, arXiv:1803.0072
FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement Learning
The human-like automatic deductive reasoning has always been one of the most
challenging open problems in the interdiscipline of mathematics and artificial
intelligence. This paper is the third in a series of our works. We built a
neural-symbolic system, called FGeoDRL, to automatically perform human-like
geometric deductive reasoning. The neural part is an AI agent based on
reinforcement learning, capable of autonomously learning problem-solving
methods from the feedback of a formalized environment, without the need for
human supervision. It leverages a pre-trained natural language model to
establish a policy network for theorem selection and employ Monte Carlo Tree
Search for heuristic exploration. The symbolic part is a reinforcement learning
environment based on geometry formalization theory and FormalGeo, which models
GPS as a Markov Decision Process. In this formal symbolic system, the known
conditions and objectives of the problem form the state space, while the set of
theorems forms the action space. Leveraging FGeoDRL, we have achieved readable
and verifiable automated solutions to geometric problems. Experiments conducted
on the formalgeo7k dataset have achieved a problem-solving success rate of
86.40%. The project is available at https://github.com/PersonNoName/FGeoDRL.Comment: 15 page
A Framework to Synergize Partial Order Reduction with State Interpolation
We address the problem of reasoning about interleavings in safety
verification of concurrent programs. In the literature, there are two prominent
techniques for pruning the search space. First, there are well-investigated
trace-based methods, collectively known as "Partial Order Reduction (POR)",
which operate by weakening the concept of a trace by abstracting the total
order of its transitions into a partial order. Second, there is state-based
interpolation where a collection of formulas can be generalized by taking into
account the property to be verified. Our main contribution is a framework that
synergistically combines POR with state interpolation so that the sum is more
than its parts
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