23,005 research outputs found
An Expressive Language and Efficient Execution System for Software Agents
Software agents can be used to automate many of the tedious, time-consuming
information processing tasks that humans currently have to complete manually.
However, to do so, agent plans must be capable of representing the myriad of
actions and control flows required to perform those tasks. In addition, since
these tasks can require integrating multiple sources of remote information ?
typically, a slow, I/O-bound process ? it is desirable to make execution as
efficient as possible. To address both of these needs, we present a flexible
software agent plan language and a highly parallel execution system that enable
the efficient execution of expressive agent plans. The plan language allows
complex tasks to be more easily expressed by providing a variety of operators
for flexibly processing the data as well as supporting subplans (for
modularity) and recursion (for indeterminate looping). The executor is based on
a streaming dataflow model of execution to maximize the amount of operator and
data parallelism possible at runtime. We have implemented both the language and
executor in a system called THESEUS. Our results from testing THESEUS show that
streaming dataflow execution can yield significant speedups over both
traditional serial (von Neumann) as well as non-streaming dataflow-style
execution that existing software and robot agent execution systems currently
support. In addition, we show how plans written in the language we present can
represent certain types of subtasks that cannot be accomplished using the
languages supported by network query engines. Finally, we demonstrate that the
increased expressivity of our plan language does not hamper performance;
specifically, we show how data can be integrated from multiple remote sources
just as efficiently using our architecture as is possible with a
state-of-the-art streaming-dataflow network query engine
Serendipitous research process
This article presents the results of an exploratory study asking faculty in the first-year writing program and instruction librarians about their research process focusing on results specifically related to serendipity. Steps to prepare for serendipity are highlighted as well as a model for incorporating serendipity into a first-year writing course
Improving reading: a handbook for improving reading in key stages 3 and 4 (National Strategies: secondary)
"This handbook explores what it means to be a reader and some core challenges and skills that need to be addressed in the teaching of reading.
The handbook outlines a route to improvement that can be followed to ensure that all pupils make expected levels of progress so that they can become skilled and independent readers.
Detailed guidance is provided for each stage of the improvement process: gathering and analysing information; writing the improvement plan; evaluating planning, approaches to teaching and learning and the assessment of reading.
Subject leaders can decide which stages of the process their department is confident with and which areas need to be developed further. Each section provides relevant resources and tools to guide and support this work." - National Strategies website
Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats
Advanced persistent threats (APTs) are stealthy attacks which make use of
social engineering and deception to give adversaries insider access to
networked systems. Against APTs, active defense technologies aim to create and
exploit information asymmetry for defenders. In this paper, we study a scenario
in which a powerful defender uses honeynets for active defense in order to
observe an attacker who has penetrated the network. Rather than immediately
eject the attacker, the defender may elect to gather information. We introduce
an undiscounted, infinite-horizon Markov decision process on a continuous state
space in order to model the defender's problem. We find a threshold of
information that the defender should gather about the attacker before ejecting
him. Then we study the robustness of this policy using a Stackelberg game.
Finally, we simulate the policy for a conceptual network. Our results provide a
quantitative foundation for studying optimal timing for attacker engagement in
network defense.Comment: Submitted to the 2019 Intl. Symp. Modeling and Optimization in
Mobile, Ad Hoc, and Wireless Nets. (WiOpt
Adaptive Information Gathering via Imitation Learning
In the adaptive information gathering problem, a policy is required to select
an informative sensing location using the history of measurements acquired thus
far. While there is an extensive amount of prior work investigating effective
practical approximations using variants of Shannon's entropy, the efficacy of
such policies heavily depends on the geometric distribution of objects in the
world. On the other hand, the principled approach of employing online POMDP
solvers is rendered impractical by the need to explicitly sample online from a
posterior distribution of world maps.
We present a novel data-driven imitation learning framework to efficiently
train information gathering policies. The policy imitates a clairvoyant oracle
- an oracle that at train time has full knowledge about the world map and can
compute maximally informative sensing locations. We analyze the learnt policy
by showing that offline imitation of a clairvoyant oracle is implicitly
equivalent to online oracle execution in conjunction with posterior sampling.
This observation allows us to obtain powerful near-optimality guarantees for
information gathering problems possessing an adaptive sub-modularity property.
As demonstrated on a spectrum of 2D and 3D exploration problems, the trained
policies enjoy the best of both worlds - they adapt to different world map
distributions while being computationally inexpensive to evaluate.Comment: Robotics Science and Systems, 201
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