79 research outputs found
Logic Programming and Machine Ethics
Transparency is a key requirement for ethical machines. Verified ethical
behavior is not enough to establish justified trust in autonomous intelligent
agents: it needs to be supported by the ability to explain decisions. Logic
Programming (LP) has a great potential for developing such perspective ethical
systems, as in fact logic rules are easily comprehensible by humans.
Furthermore, LP is able to model causality, which is crucial for ethical
decision making.Comment: In Proceedings ICLP 2020, arXiv:2009.09158. Invited paper for the
ICLP2020 Panel on "Machine Ethics". arXiv admin note: text overlap with
arXiv:1909.0825
How Fast Can We Play Tetris Greedily With Rectangular Pieces?
Consider a variant of Tetris played on a board of width and infinite
height, where the pieces are axis-aligned rectangles of arbitrary integer
dimensions, the pieces can only be moved before letting them drop, and a row
does not disappear once it is full. Suppose we want to follow a greedy
strategy: let each rectangle fall where it will end up the lowest given the
current state of the board. To do so, we want a data structure which can always
suggest a greedy move. In other words, we want a data structure which maintains
a set of rectangles, supports queries which return where to drop the
rectangle, and updates which insert a rectangle dropped at a certain position
and return the height of the highest point in the updated set of rectangles. We
show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on
a board of width , if the OMv conjecture [Henzinger et al., 2015]
is true, then both operations cannot be supported in time
simultaneously. The reduction also implies polynomial bounds from the 3-SUM
conjecture and the APSP conjecture. On the other hand, we show that there is a
data structure supporting both operations in time on
boards of width , matching the lower bound up to a factor.Comment: Correction of typos and other minor correction
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning
With the Deep Neural Networks (DNNs) as a powerful function approximator,
Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic
control tasks. Compared to DNNs with vanilla artificial neurons, the
biologically plausible Spiking Neural Network (SNN) contains a diverse
population of spiking neurons, making it naturally powerful on state
representation with spatial and temporal information. Based on a hybrid
learning framework, where a spike actor-network infers actions from states and
a deep critic network evaluates the actor, we propose a Population-coding and
Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state
representation from two different scales: input coding and neuronal coding. For
input coding, we apply population coding with dynamically receptive fields to
directly encode each input state component. For neuronal coding, we propose
different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal
dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN
is trained in conjunction with deep critic networks using the Twin Delayed Deep
Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental
results show that our TD3-PDSAN model achieves better performance than
state-of-the-art models on four OpenAI gym benchmark tasks. It is an important
attempt to improve RL with SNN towards the effective computation satisfying
biological plausibility.Comment: 27 pages, 11 figures, accepted by Journal of Neural Network
On learning history based policies for controlling Markov decision processes
Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas
recurrent neural nets or history-based state abstraction, perform better than
their memory-less counterparts, due to the fact that function approximation in
Markov decision processes (MDP) can be viewed as inducing a Partially
observable MDP. However, there has been little formal analysis of such
history-based algorithms, as most existing frameworks focus exclusively on
memory-less features. In this paper, we introduce a theoretical framework for
studying the behaviour of RL algorithms that learn to control an MDP using
history-based feature abstraction mappings. Furthermore, we use this framework
to design a practical RL algorithm and we numerically evaluate its
effectiveness on a set of continuous control tasks
Semantic HELM: A Human-Readable Memory for Reinforcement Learning
Reinforcement learning agents deployed in the real world often have to cope
with partially observable environments. Therefore, most agents employ memory
mechanisms to approximate the state of the environment. Recently, there have
been impressive success stories in mastering partially observable environments,
mostly in the realm of computer games like Dota 2, StarCraft II, or MineCraft.
However, existing methods lack interpretability in the sense that it is not
comprehensible for humans what the agent stores in its memory. In this regard,
we propose a novel memory mechanism that represents past events in human
language. Our method uses CLIP to associate visual inputs with language tokens.
Then we feed these tokens to a pretrained language model that serves the agent
as memory and provides it with a coherent and human-readable representation of
the past. We train our memory mechanism on a set of partially observable
environments and find that it excels on tasks that require a memory component,
while mostly attaining performance on-par with strong baselines on tasks that
do not. On a challenging continuous recognition task, where memorizing the past
is crucial, our memory mechanism converges two orders of magnitude faster than
prior methods. Since our memory mechanism is human-readable, we can peek at an
agent's memory and check whether crucial pieces of information have been
stored. This significantly enhances troubleshooting and paves the way toward
more interpretable agents.Comment: To appear at NeurIPS 2023, 10 pages (+ references and appendix),
Code: https://github.com/ml-jku/hel
Educational Technology and Related Education Conferences for June to December 2015
The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
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