3,135 research outputs found
A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging
In this paper, we propose a new approach to construct a system of
transformation rules for the Part-of-Speech (POS) tagging task. Our approach is
based on an incremental knowledge acquisition method where rules are stored in
an exception structure and new rules are only added to correct the errors of
existing rules; thus allowing systematic control of the interaction between the
rules. Experimental results on 13 languages show that our approach is fast in
terms of training time and tagging speed. Furthermore, our approach obtains
very competitive accuracy in comparison to state-of-the-art POS and
morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the
European Journal on Artificial Intelligence. Version 3: Resubmitted after
major revisions. Version 4: Resubmitted after minor revisions. Version 5: to
appear in AI Communications (accepted for publication on 3/12/2015
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of
multi-agent learning ranging from computing approximations to fundamental
concepts in game theory to simulating social dilemmas in rich spatial
environments and training 3-d humanoids in difficult team coordination tasks. A
signature aim of our group is to use the resources and expertise made available
to us at DeepMind in deep reinforcement learning to explore multi-agent systems
in complex environments and use these benchmarks to advance our understanding.
Here, we summarise the recent work of our team and present a taxonomy that we
feel highlights many important open challenges in multi-agent research.Comment: Published in AI Communications 202
Experimental evaluation of algorithms forsolving problems with combinatorial explosion
Solving problems with combinatorial explosionplays an important role in decision-making, sincefeasible or optimal decisions often depend on anon-trivial combination of various factors. Gener-ally, an effective strategy for solving such problemsis merging different viewpoints adopted in differ-ent communities that try to solve similar prob-lems; such that algorithms developed in one re-search area are applicable to other problems, orcan be hybridised with techniques in other ar-eas. This is one of the aims of the RCRA (Ra-gionamento Automatico e Rappresentazione dellaConoscenza) group,1the interest group of the Ital-ian Association for Artificial Intelligence (AI*IA)on knowledge representation and automated rea-soning, which organises its annual meetings since1994
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to
accomplish a given task is a core area of research in artificial intelligence
and machine learning. Towards this goal, the Autonomous Agents Research Group
develops novel machine learning algorithms for autonomous systems control, with
a specific focus on deep reinforcement learning and multi-agent reinforcement
learning. Research problems include scalable learning of coordinated agent
policies and inter-agent communication; reasoning about the behaviours, goals,
and composition of other agents from limited observations; and sample-efficient
learning based on intrinsic motivation, curriculum learning, causal inference,
and representation learning. This article provides a broad overview of the
ongoing research portfolio of the group and discusses open problems for future
directions.Comment: Published in AI Communications Special Issue on Multi-Agent Systems
Research in the U
MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers
MaLeS is an automatic tuning framework for automated theorem provers. It
provides solutions for both the strategy finding as well as the strategy
scheduling problem. This paper describes the tool and the methods used in it,
and evaluates its performance on three automated theorem provers: E, LEO-II and
Satallax. An evaluation on a subset of the TPTP library problems shows that on
average a MaLeS-tuned prover solves 8.67% more problems than the prover with
its default settings
ChatGPT: Vision and Challenges
Artificial intelligence (AI) and machine learning have changed the nature of
scientific inquiry in recent years. Of these, the development of virtual
assistants has accelerated greatly in the past few years, with ChatGPT becoming
a prominent AI language model. In this study, we examine the foundations,
vision, research challenges of ChatGPT. This article investigates into the
background and development of the technology behind it, as well as its popular
applications. Moreover, we discuss the advantages of bringing everything
together through ChatGPT and Internet of Things (IoT). Further, we speculate on
the future of ChatGPT by considering various possibilities for study and
development, such as energy-efficiency, cybersecurity, enhancing its
applicability to additional technologies (Robotics and Computer Vision),
strengthening human-AI communications, and bridging the technological gap.
Finally, we discuss the important ethics and current trends of ChatGPT
Engineering regulated open multiagent systems
In this thesis,
w
e
focus on the
d
e
velopment
o
f normati
v
e open
multiagent systems. The
y are
systems in which
heterogeneous
and autonomous agents may need to coexist in a complex social and legal framework that can evolve to address the
different
and often
conflicting objecti
ves of the
man
y
stak
eholders inv
olved.
This
thesis
presents
ROMAS,
a set
o
f
methods and
tools
for analyzing
and designing systems
o
f
this
kind.
R
OMAS
inte
grates
the analysis,
design
and
v
erification
o
f
these systems
by means
o
f
a metamodel,
a
methodology that includes
specific de
v
elopment guidelines and
a model-dri
ven CASE tool.The author wish to thank R. Bejar and F. Manya for supervising this PhD thesis. Supported by MINECO projects TIN2009-14704-C03-01 and TIN2010-20967-C04-01/03.Garcia Marques, ME. (2014). Engineering regulated open multiagent systems. AI Communications. 27:417-419. https://doi.org/10.3233/AIC-1406104174192
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