37,321 research outputs found
Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction
Automatic bill payment is an important part of business operations in fintech
companies. The practice of deduction was mainly based on the total amount or
heuristic search by dividing the bill into smaller parts to deduct as much as
possible. This article proposes an end-to-end approach of automatically
learning the optimal deduction paths (deduction amount in order), which reduces
the cost of manual path design and maximizes the amount of successful
deduction. Specifically, in view of the large search space of the paths and the
extreme sparsity of historical successful deduction records, we propose a deep
hierarchical reinforcement learning approach which abstracts the action into a
two-level hierarchical space: an upper agent that determines the number of
steps of deductions each day and a lower agent that decides the amount of
deduction at each step. In such a way, the action space is structured via prior
knowledge and the exploration space is reduced. Moreover, the inherited
information incompleteness of the business makes the environment just partially
observable. To be precise, the deducted amounts indicate merely the lower
bounds of the available account balance. To this end, we formulate the problem
as a partially observable Markov decision problem (POMDP) and employ an
environment correction algorithm based on the characteristics of the business.
In the world's largest electronic payment business, we have verified the
effectiveness of this scheme offline and deployed it online to serve millions
of users
Hierarchical combination of intruder theories
International audienceRecently automated deduction tools have proved to be very effective for detecting attacks on cryptographic protocols. These analysis can be improved, for finding more subtle weaknesses, by a more accurate modelling of operators employed by protocols. Several works have shown how to handle a single algebraic operator (associated with a fixed intruder theory) or how to combine several operators satisfying disjoint theories. However several interesting equational theories, such as exponentiation with an abelian group law for exponents remain out of the scope of these techniques. This has motivated us to introduce a new notion of hierarchical combination for non-disjoint intruder theories and to show decidability results for the deduction problem in these theories. We have also shown that under natural hypotheses hierarchical intruder constraints can be decided. This result applies to an exponentiation theory that appears to be more general than the one considered before
Higher-order Linear Logic Programming of Categorial Deduction
We show how categorial deduction can be implemented in higher-order (linear)
logic programming, thereby realising parsing as deduction for the associative
and non-associative Lambek calculi. This provides a method of solution to the
parsing problem of Lambek categorial grammar applicable to a variety of its
extensions.Comment: 8 pages LaTeX, uses eaclap.sty, to appear EACL9
Towards Intelligent Databases
This article is a presentation of the objectives and techniques
of deductive databases. The deductive approach to databases aims at extending
with intensional definitions other database paradigms that describe
applications extensionaUy. We first show how constructive specifications can
be expressed with deduction rules, and how normative conditions can be defined
using integrity constraints. We outline the principles of bottom-up and
top-down query answering procedures and present the techniques used for
integrity checking. We then argue that it is often desirable to manage with
a database system not only database applications, but also specifications of
system components. We present such meta-level specifications and discuss
their advantages over conventional approaches
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453
Reasoning about goal-directed real-time teleo-reactive programs
The teleo-reactive programming model is a high-level approach to developing real-time systems that supports hierarchical composition and durative actions. The model is different from frameworks such as action systems, timed automata and TLA+, and allows programs to be more compact and descriptive of their intended behaviour. Teleo-reactive programs are particularly useful for implementing controllers for autonomous agents that must react robustly to their dynamically changing environments. In this paper, we develop a real-time logic that is based on Duration Calculus and use this logic to formalise the semantics of teleo-reactive programs. We develop rely/guarantee rules that facilitate reasoning about a program and its environment in a compositional manner. We present several theorems for simplifying proofs of teleo-reactive programs and present a partially mechanised method for proving progress properties of goal-directed agents. © 2013 British Computer Society
TLA+ Proofs
TLA+ is a specification language based on standard set theory and temporal
logic that has constructs for hierarchical proofs. We describe how to write
TLA+ proofs and check them with TLAPS, the TLA+ Proof System. We use Peterson's
mutual exclusion algorithm as a simple example to describe the features of
TLAPS and show how it and the Toolbox (an IDE for TLA+) help users to manage
large, complex proofs.Comment: A shorter version of this article appeared in the proceedings of the
conference Formal Methods 2012 (FM 2012, Paris, France, Springer LNCS 7436,
pp. 147-154
Planning and Proof Planning
. The paper adresses proof planning as a specific AI planning. It describes some peculiarities of proof planning and discusses some possible cross-fertilization of planning and proof planning. 1 Introduction Planning is an established area of Artificial Intelligence (AI) whereas proof planning introduced by Bundy in [2] still lives in its childhood. This means that the development of proof planning needs maturing impulses and the natural questions arise What can proof planning learn from its Big Brother planning?' and What are the specific characteristics of the proof planning domain that determine the answer?'. In turn for planning, the analysis of approaches points to a need of mature techniques for practical planning. Drummond [8], e.g., analyzed approaches with the conclusion that the success of Nonlin, SIPE, and O-Plan in practical planning can be attributed to hierarchical action expansion, the explicit representation of a plan's causal structure, and a very simple form of propo..
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