4,373 research outputs found
Visualizations for an Explainable Planning Agent
In this paper, we report on the visualization capabilities of an Explainable
AI Planning (XAIP) agent that can support human in the loop decision making.
Imposing transparency and explainability requirements on such agents is
especially important in order to establish trust and common ground with the
end-to-end automated planning system. Visualizing the agent's internal
decision-making processes is a crucial step towards achieving this. This may
include externalizing the "brain" of the agent -- starting from its sensory
inputs, to progressively higher order decisions made by it in order to drive
its planning components. We also show how the planner can bootstrap on the
latest techniques in explainable planning to cast plan visualization as a plan
explanation problem, and thus provide concise model-based visualization of its
plans. We demonstrate these functionalities in the context of the automated
planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator
(appeared in AAAI 2017 Fall Symposium on Human-Agent Groups
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
With advances in reinforcement learning (RL), agents are now being developed
in high-stakes application domains such as healthcare and transportation.
Explaining the behavior of these agents is challenging, as the environments in
which they act have large state spaces, and their decision-making can be
affected by delayed rewards, making it difficult to analyze their behavior. To
address this problem, several approaches have been developed. Some approaches
attempt to convey the behavior of the agent, describing the
actions it takes in different states. Other approaches devised
explanations which provide information regarding the agent's decision-making in
a particular state. In this paper, we combine global and local explanation
methods, and evaluate their joint and separate contributions, providing (to the
best of our knowledge) the first user study of combined local and global
explanations for RL agents. Specifically, we augment strategy summaries that
extract important trajectories of states from simulations of the agent with
saliency maps which show what information the agent attends to. Our results
show that the choice of what states to include in the summary (global
information) strongly affects people's understanding of agents: participants
shown summaries that included important states significantly outperformed
participants who were presented with agent behavior in a randomly set of chosen
world-states. We find mixed results with respect to augmenting demonstrations
with saliency maps (local information), as the addition of saliency maps did
not significantly improve performance in most cases. However, we do find some
evidence that saliency maps can help users better understand what information
the agent relies on in its decision making, suggesting avenues for future work
that can further improve explanations of RL agents
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Summarization from Medical Documents: A Survey
Objective:
The aim of this paper is to survey the recent work in medical documents
summarization.
Background:
During the last decade, documents summarization got increasing attention by
the AI research community. More recently it also attracted the interest of the
medical research community as well, due to the enormous growth of information
that is available to the physicians and researchers in medicine, through the
large and growing number of published journals, conference proceedings, medical
sites and portals on the World Wide Web, electronic medical records, etc.
Methodology:
This survey gives first a general background on documents summarization,
presenting the factors that summarization depends upon, discussing evaluation
issues and describing briefly the various types of summarization techniques. It
then examines the characteristics of the medical domain through the different
types of medical documents. Finally, it presents and discusses the
summarization techniques used so far in the medical domain, referring to the
corresponding systems and their characteristics.
Discussion and conclusions:
The paper discusses thoroughly the promising paths for future research in
medical documents summarization. It mainly focuses on the issue of scaling to
large collections of documents in various languages and from different media,
on personalization issues, on portability to new sub-domains, and on the
integration of summarization technology in practical applicationsComment: 21 pages, 4 table
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
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