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1911.01417 | Alexander Trott | Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher | Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing
Shaped Rewards | NeurIPS 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While using shaped rewards can be beneficial when solving sparse reward
tasks, their successful application often requires careful engineering and is
problem specific. For instance, in tasks where the agent must achieve some goal
state, simple distance-to-goal reward shaping often fails, as it renders
learning vulnerable to local optima. We introduce a simple and effective
model-free method to learn from shaped distance-to-goal rewards on tasks where
success depends on reaching a goal state. Our method introduces an auxiliary
distance-based reward based on pairs of rollouts to encourage diverse
exploration. This approach effectively prevents learning dynamics from
stabilizing around local optima induced by the naive distance-to-goal reward
shaping and enables policies to efficiently solve sparse reward tasks. Our
augmented objective does not require any additional reward engineering or
domain expertise to implement and converges to the original sparse objective as
the agent learns to solve the task. We demonstrate that our method successfully
solves a variety of hard-exploration tasks (including maze navigation and 3D
construction in a Minecraft environment), where naive distance-based reward
shaping otherwise fails, and intrinsic curiosity and reward relabeling
strategies exhibit poor performance.
| [
{
"version": "v1",
"created": "Mon, 4 Nov 2019 18:58:06 GMT"
}
] | 1,572,912,000,000 | [
[
"Trott",
"Alexander",
""
],
[
"Zheng",
"Stephan",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Socher",
"Richard",
""
]
] |
1911.01547 | Francois Chollet | Fran\c{c}ois Chollet | On the Measure of Intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To make deliberate progress towards more intelligent and more human-like
artificial systems, we need to be following an appropriate feedback signal: we
need to be able to define and evaluate intelligence in a way that enables
comparisons between two systems, as well as comparisons with humans. Over the
past hundred years, there has been an abundance of attempts to define and
measure intelligence, across both the fields of psychology and AI. We summarize
and critically assess these definitions and evaluation approaches, while making
apparent the two historical conceptions of intelligence that have implicitly
guided them. We note that in practice, the contemporary AI community still
gravitates towards benchmarking intelligence by comparing the skill exhibited
by AIs and humans at specific tasks such as board games and video games. We
argue that solely measuring skill at any given task falls short of measuring
intelligence, because skill is heavily modulated by prior knowledge and
experience: unlimited priors or unlimited training data allow experimenters to
"buy" arbitrary levels of skills for a system, in a way that masks the system's
own generalization power. We then articulate a new formal definition of
intelligence based on Algorithmic Information Theory, describing intelligence
as skill-acquisition efficiency and highlighting the concepts of scope,
generalization difficulty, priors, and experience. Using this definition, we
propose a set of guidelines for what a general AI benchmark should look like.
Finally, we present a benchmark closely following these guidelines, the
Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors
designed to be as close as possible to innate human priors. We argue that ARC
can be used to measure a human-like form of general fluid intelligence and that
it enables fair general intelligence comparisons between AI systems and humans.
| [
{
"version": "v1",
"created": "Tue, 5 Nov 2019 00:31:38 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Nov 2019 13:02:04 GMT"
}
] | 1,574,726,400,000 | [
[
"Chollet",
"François",
""
]
] |
1911.01875 | Lora Aroyo | Chris Welty, Praveen Paritosh, Lora Aroyo | Metrology for AI: From Benchmarks to Instruments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present the first steps towards hardening the science of
measuring AI systems, by adopting metrology, the science of measurement and its
application, and applying it to human (crowd) powered evaluations. We begin
with the intuitive observation that evaluating the performance of an AI system
is a form of measurement. In all other science and engineering disciplines, the
devices used to measure are called instruments, and all measurements are
recorded with respect to the characteristics of the instruments used. One does
not report mass, speed, or length, for example, of a studied object without
disclosing the precision (measurement variance) and resolution (smallest
detectable change) of the instrument used. It is extremely common in the AI
literature to compare the performance of two systems by using a crowd-sourced
dataset as an instrument, but failing to report if the performance difference
lies within the capability of that instrument to measure. To illustrate the
adoption of metrology to benchmark datasets we use the word similarity
benchmark WS353 and several previously published experiments that use it for
evaluation.
| [
{
"version": "v1",
"created": "Tue, 5 Nov 2019 15:30:08 GMT"
}
] | 1,572,998,400,000 | [
[
"Welty",
"Chris",
""
],
[
"Paritosh",
"Praveen",
""
],
[
"Aroyo",
"Lora",
""
]
] |
1911.02224 | Naibo Wang | Meng Xi, Zhiling Luo, Naibo Wang, Jianwei Yin | A Latent Feelings-aware RNN Model for User Churn Prediction with
Behavioral Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting user churn and taking personalized measures to retain users is a
set of common and effective practices for online game operators. However,
different from the traditional user churn relevant researches that can involve
demographic, economic, and behavioral data, most online games can only obtain
logs of user behavior and have no access to users' latent feelings. There are
mainly two challenges in this work: 1. The latent feelings, which cannot be
directly observed in this work, need to be estimated and verified; 2. User
churn needs to be predicted with only behavioral data. In this work, a
Recurrent Neural Network(RNN) called LaFee (Latent Feeling) is proposed, which
can get the users' latent feelings while predicting user churn. Besides, we
proposed a method named BMM-UCP (Behavior-based Modeling Method for User Churn
Prediction) to help models predict user churn with only behavioral data. The
latent feelings are names as satisfaction and aspiration in this work. We
designed experiments on a real dataset and the results show that our methods
outperform baselines and are more suitable for long-term sequential learning.
The latent feelings learned are fully discussed and proven meaningful.
| [
{
"version": "v1",
"created": "Wed, 6 Nov 2019 06:49:36 GMT"
}
] | 1,573,084,800,000 | [
[
"Xi",
"Meng",
""
],
[
"Luo",
"Zhiling",
""
],
[
"Wang",
"Naibo",
""
],
[
"Yin",
"Jianwei",
""
]
] |
1911.02887 | Javier Segovia Aguas | Javier Segovia-Aguas and Sergio Jim\'enez and Anders Jonsson | Hierarchical Finite State Controllers for Generalized Planning | IJCAI-16 Distinguished Paper Awards, 7 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Finite State Controllers (FSCs) are an effective way to represent sequential
plans compactly. By imposing appropriate conditions on transitions, FSCs can
also represent generalized plans that solve a range of planning problems from a
given domain. In this paper we introduce the concept of {\it hierarchical FSCs}
for planning by allowing controllers to call other controllers. We show that
hierarchical FSCs can represent generalized plans more compactly than
individual FSCs. Moreover, our call mechanism makes it possible to generate
hierarchical FSCs in a modular fashion, or even to apply recursion. We also
introduce a compilation that enables a classical planner to generate
hierarchical FSCs that solve challenging generalized planning problems. The
compilation takes as input a set of planning problems from a given domain and
outputs a single classical planning problem, whose solution corresponds to a
hierarchical FSC.
| [
{
"version": "v1",
"created": "Thu, 7 Nov 2019 13:21:28 GMT"
}
] | 1,573,430,400,000 | [
[
"Segovia-Aguas",
"Javier",
""
],
[
"Jiménez",
"Sergio",
""
],
[
"Jonsson",
"Anders",
""
]
] |
1911.03388 | Ishan Srivastava | Ishan Srivastava | A different take on the best-first game tree pruning algorithms | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The alpha-beta pruning algorithms have been popular in game tree searching
ever since they were discovered. Numerous enhancements are proposed in
literature and it is often overwhelming as to which would be the best for
implementation. A certain enhancement can take far too long to fine tune its
hyper parameters or to decide whether it is going to not make much of a
difference due to the memory limitations. On the other hand are the best first
pruning techniques, mostly the counterparts of the infamous SSS* algorithm, the
algorithm which proved out to be disruptive at the time of its discovery but
gradually became outcast as being too memory intensive and having a higher time
complexity. Later research doesn't see the best first approaches to be
completely different from the depth first based enhancements but both seem to
be transitionary in the sense that a best first approach could be looked as a
depth first approach with a certain set of enhancements and with the growing
power of the computers, SSS* didn't seem to be as taxing on the memory either.
Even so, there seems to be quite difficulty in understanding the nature of the
SSS* algorithm, why it does what it does and it being termed as being too
complex to fathom, visualize and understand on an intellectual level. This
article tries to bridge this gap and provide some experimental results
comparing the two with the most promising advances.
| [
{
"version": "v1",
"created": "Fri, 8 Nov 2019 17:13:09 GMT"
}
] | 1,573,430,400,000 | [
[
"Srivastava",
"Ishan",
""
]
] |
1911.04766 | Tobias Geibinger | Tobias Geibinger, Florian Mischek and Nysret Musliu | Investigating Constraint Programming and Hybrid Methods for Real World
Industrial Test Laboratory Scheduling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we deal with a complex real world scheduling problem closely
related to the well-known Resource-Constrained Project Scheduling Problem
(RCPSP). The problem concerns industrial test laboratories in which a large
number of tests has to be performed by qualified personnel using specialised
equipment, while respecting deadlines and other constraints. We present
different constraint programming models and search strategies for this problem.
Furthermore, we propose a Very Large Neighborhood Search approach based on our
CP methods. Our models are evaluated using CP solvers and a MIP solver both on
real-world test laboratory data and on a set of generated instances of
different sizes based on the real-world data. Further, we compare the exact
approaches with VLNS and a Simulated Annealing heuristic. We could find
feasible solutions for all instances and several optimal solutions and we show
that using VLNS we can improve upon the results of the other approaches.
| [
{
"version": "v1",
"created": "Tue, 12 Nov 2019 10:03:16 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Sep 2021 11:52:55 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Dec 2022 10:16:33 GMT"
}
] | 1,670,457,600,000 | [
[
"Geibinger",
"Tobias",
""
],
[
"Mischek",
"Florian",
""
],
[
"Musliu",
"Nysret",
""
]
] |
1911.04868 | Changmao Li | Changmao Li | Challenging On Car Racing Problem from OpenAI gym | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This project challenges the car racing problem from OpenAI gym environment.
The problem is very challenging since it requires computer to finish the
continuous control task by learning from pixels. To tackle this challenging
problem, we explored two approaches including evolutionary algorithm based
genetic multi-layer perceptron and double deep Q-learning network. The result
shows that the genetic multi-layer perceptron can converge fast but when
training many episodes, double deep Q-learning can get better score. We analyze
the result and draw a conclusion that for limited hardware resources, using
genetic multi-layer perceptron sometimes can be more efficient.
| [
{
"version": "v1",
"created": "Sat, 2 Nov 2019 20:14:55 GMT"
}
] | 1,573,603,200,000 | [
[
"Li",
"Changmao",
""
]
] |
1911.04869 | EPTCS | Severin Kacianka (TU Munich), Amjad Ibrahim (TU Munich), Alexander
Pretschner (TU Munich), Alexander Trende (Offis), Andreas L\"udtke (Offis) | Extending Causal Models from Machines into Humans | In Proceedings CREST 2019, arXiv:1910.13641 | EPTCS 308, 2019, pp. 17-31 | 10.4204/EPTCS.308.2 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal Models are increasingly suggested as a means to reason about the
behavior of cyber-physical systems in socio-technical contexts. They allow us
to analyze courses of events and reason about possible alternatives. Until now,
however, such reasoning is confined to the technical domain and limited to
single systems or at most groups of systems. The humans that are an integral
part of any such socio-technical system are usually ignored or dealt with by
"expert judgment". We show how a technical causal model can be extended with
models of human behavior to cover the complexity and interplay between humans
and technical systems. This integrated socio-technical causal model can then be
used to reason not only about actions and decisions taken by the machine, but
also about those taken by humans interacting with the system. In this paper we
demonstrate the feasibility of merging causal models about machines with causal
models about humans and illustrate the usefulness of this approach with a
highly automated vehicle example.
| [
{
"version": "v1",
"created": "Thu, 31 Oct 2019 02:30:07 GMT"
}
] | 1,573,603,200,000 | [
[
"Kacianka",
"Severin",
"",
"TU Munich"
],
[
"Ibrahim",
"Amjad",
"",
"TU Munich"
],
[
"Pretschner",
"Alexander",
"",
"TU Munich"
],
[
"Trende",
"Alexander",
"",
"Offis"
],
[
"Lüdtke",
"Andreas",
"",
"Offis"
]
] |
1911.04888 | Vitaliy Tsyganok | Sergii Kadenko and Vitaliy Tsyganok | Comparing Efficiency of Expert Data Aggregation Methods | 16 pages, 6 figures, 5 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Expert estimation of objects takes place when there are no benchmark values
of object weights, but these weights still have to be defined. That is why it
is problematic to define the efficiency of expert estimation methods. We
propose to define efficiency of such methods based on stability of their
results under perturbations of input data. We compare two modifications of
combinatorial method of expert data aggregation (spanning tree enumeration).
Using the example of these two methods, we illustrate two approaches to
efficiency evaluation. The first approach is based on usage of real data,
obtained through estimation of a set of model objects by a group of experts.
The second approach is based on simulation of the whole expert examination
cycle (including expert estimates). During evaluation of efficiency of the two
listed modifications of combinatorial expert data aggregation method the
simulation-based approach proved more robust and credible. Our experimental
study confirms that if weights of spanning trees are taken into consideration,
the results of combinatorial data aggregation method become more stable. So,
weighted spanning tree enumeration method has an advantage over non-weighted
method (and, consequently, over logarithmic least squares and row geometric
mean methods).
| [
{
"version": "v1",
"created": "Sat, 9 Nov 2019 10:26:26 GMT"
}
] | 1,573,603,200,000 | [
[
"Kadenko",
"Sergii",
""
],
[
"Tsyganok",
"Vitaliy",
""
]
] |
1911.05041 | Maen Alzubi | Maen Alzubi, Szilveszter Kovacs | Some Considerations and a Benchmark Related to the CNF Property of the
Koczy-Hirota Fuzzy Rule Interpolation | null | International Journal on Advanced Science, Engineering and
Information Technology 2019. Vol.9. No 5 | 10.18517/ijaseit.9.5.8356 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of this paper is twofold. Once to highlight some basic problematic
properties of the KH Fuzzy Rule Interpolation through examples, secondly to set
up a brief Benchmark set of Examples, which is suitable for testing other Fuzzy
Rule Interpolation (FRI) methods against these ill conditions. Fuzzy Rule
Interpolation methods were originally proposed to handle the situation of
missing fuzzy rules (sparse rule-bases) and to reduce the decision complexity.
Fuzzy Rule Interpolation is an important technique for implementing inference
with sparse fuzzy rule-bases. Even if a given observation has no overlap with
the antecedent of any rule from the rule-base, FRI may still conclude a
conclusion. The first FRI method was the Koczy and Hirota proposed "Linear
Interpolation", which was later renamed to "KH Fuzzy Interpolation" by the
followers. There are several conditions and criteria have been suggested for
unifying the common requirements an FRI methods have to satisfy. One of the
most common one is the demand for a convex and normal fuzzy (CNF) conclusion,
if all the rule antecedents and consequents are CNF sets. The KH FRI is the
one, which cannot fulfill this condition. This paper is focusing on the
conditions, where the KH FRI fails the demand for the CNF conclusion. By
setting up some CNF rule examples, the paper also defines a Benchmark, in which
other FRI methods can be tested if they can produce CNF conclusion where the KH
FRI fails.
| [
{
"version": "v1",
"created": "Tue, 12 Nov 2019 18:02:14 GMT"
}
] | 1,574,035,200,000 | [
[
"Alzubi",
"Maen",
""
],
[
"Kovacs",
"Szilveszter",
""
]
] |
1911.05499 | Damien Pellier | D. H\"oller, G. Behnke, P. Bercher, S. Biundo, H. Fiorino, D. Pellier
and R. Alford | HDDL -- A Language to Describe Hierarchical Planning Problems | International Workshop on HTN Planning (ICAPS), 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The research in hierarchical planning has made considerable progress in the
last few years. Many recent systems do not rely on hand-tailored advice anymore
to find solutions, but are supposed to be domain-independent systems that come
with sophisticated solving techniques. In principle, this development would
make the comparison between systems easier (because the domains are not
tailored to a single system anymore) and -- much more important -- also the
integration into other systems, because the modeling process is less tedious
(due to the lack of advice) and there is no (or less) commitment to a certain
planning system the model is created for. However, these advantages are
destroyed by the lack of a common input language and feature set supported by
the different systems. In this paper, we propose an extension to PDDL, the
description language used in non-hierarchical planning, to the needs of
hierarchical planning systems. We restrict our language to a basic feature set
shared by many recent systems, give an extension of PDDL's EBNF syntax
definition, and discuss our extensions with respect to several planner-specific
input languages from related work.
| [
{
"version": "v1",
"created": "Wed, 13 Nov 2019 14:23:55 GMT"
}
] | 1,573,689,600,000 | [
[
"Höller",
"D.",
""
],
[
"Behnke",
"G.",
""
],
[
"Bercher",
"P.",
""
],
[
"Biundo",
"S.",
""
],
[
"Fiorino",
"H.",
""
],
[
"Pellier",
"D.",
""
],
[
"Alford",
"R.",
""
]
] |
1911.05876 | Jennifer Nelson | Jennifer M. Nelson and Rogelio E. Cardona-Rivera | Partial-Order, Partially-Seen Observations of Fluents or Actions for
Plan Recognition as Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work aims to make plan recognition as planning more ready for real-world
scenarios by adapting previous compilations to work with partial-order,
half-seen observations of both fluents and actions. We first redefine what
observations can be and what it means to satisfy each kind. We then provide a
compilation from plan recognition problem to classical planning problem,
similar to original work by Ramirez and Geffner, but accommodating these more
complex observation types. This compilation can be adapted towards other
planning-based plan recognition techniques. Lastly we evaluate this method
against an "ignore complexity" strategy that uses the original method by
Ramirez and Geffner. Our experimental results suggest that, while slower, our
method is equally or more accurate than baseline methods; our technique
sometimes significantly reduces the size of the solution to the plan
recognition problem, i.e, the size of the optimal goal set. We discuss these
findings in the context of plan recognition problem difficulty and present an
avenue for future work.
| [
{
"version": "v1",
"created": "Thu, 14 Nov 2019 00:53:36 GMT"
}
] | 1,573,776,000,000 | [
[
"Nelson",
"Jennifer M.",
""
],
[
"Cardona-Rivera",
"Rogelio E.",
""
]
] |
1911.06226 | Olivier Spanjaard | Hugo Gilbert, Tom Portoleau, Olivier Spanjaard | Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny
Aggregation Problem | 36 pages, extends a work published at AAAI 2020. Compared to the
previous version on arXiv, some notations have been changed, and section 5
has been added | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we advocate the use of setwise contests for aggregating a set
of input rankings into an output ranking. We propose a generalization of the
Kemeny rule where one minimizes the number of k-wise disagreements instead of
pairwise disagreements (one counts 1 disagreement each time the top choice in a
subset of alternatives of cardinality at most k differs between an input
ranking and the output ranking). After an algorithmic study of this k-wise
Kemeny aggregation problem, we introduce a k-wise counterpart of the majority
graph. This graph reveals useful to divide the aggregation problem into several
sub-problems, which enables to speed up the exact computation of a consensus
ranking. By introducing a k-wise counterpart of the Spearman distance, we also
provide a 2-approximation algorithm for the k-wise Kemeny aggregation problem.
We conclude with numerical tests.
| [
{
"version": "v1",
"created": "Thu, 14 Nov 2019 16:37:00 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Feb 2022 15:18:48 GMT"
}
] | 1,644,451,200,000 | [
[
"Gilbert",
"Hugo",
""
],
[
"Portoleau",
"Tom",
""
],
[
"Spanjaard",
"Olivier",
""
]
] |
1911.06473 | Himabindu Lakkaraju | Himabindu Lakkaraju, Osbert Bastani | "How do I fool you?": Manipulating User Trust via Misleading Black Box
Explanations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As machine learning black boxes are increasingly being deployed in critical
domains such as healthcare and criminal justice, there has been a growing
emphasis on developing techniques for explaining these black boxes in a human
interpretable manner. It has recently become apparent that a high-fidelity
explanation of a black box ML model may not accurately reflect the biases in
the black box. As a consequence, explanations have the potential to mislead
human users into trusting a problematic black box. In this work, we rigorously
explore the notion of misleading explanations and how they influence user trust
in black-box models. More specifically, we propose a novel theoretical
framework for understanding and generating misleading explanations, and carry
out a user study with domain experts to demonstrate how these explanations can
be used to mislead users. Our work is the first to empirically establish how
user trust in black box models can be manipulated via misleading explanations.
| [
{
"version": "v1",
"created": "Fri, 15 Nov 2019 04:20:11 GMT"
}
] | 1,574,035,200,000 | [
[
"Lakkaraju",
"Himabindu",
""
],
[
"Bastani",
"Osbert",
""
]
] |
1911.06657 | Paolo Pareti Dr. | Paolo Pareti and George Konstantinidis and Timothy J. Norman | A Policy Editor for Semantic Sensor Networks | Demo paper presented at the 18th International Semantic Web
Conference (ISWC 2019) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An important use of sensors and actuator networks is to comply with health
and safety policies in hazardous environments. In order to deal with
increasingly large and dynamic environments, and to quickly react to
emergencies, tools are needed to simplify the process of translating high-level
policies into executable queries and rules. We present a framework to produce
such tools, which uses rules to aggregate low-level sensor data, described
using the Semantic Sensor Network Ontology, into more useful and actionable
abstractions. Using the schema of the underlying data sources as an input, we
automatically generate abstractions which are relevant to the use case at hand.
In this demonstration we present a policy editor tool and a simulation on which
policies can be tested.
| [
{
"version": "v1",
"created": "Fri, 15 Nov 2019 14:21:54 GMT"
}
] | 1,574,035,200,000 | [
[
"Pareti",
"Paolo",
""
],
[
"Konstantinidis",
"George",
""
],
[
"Norman",
"Timothy J.",
""
]
] |
1911.07040 | Marcel Gehrke | Marcel Gehrke, Ralf M\"oller, and Tanya Braun | Taming Reasoning in Temporal Probabilistic Relational Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evidence often grounds temporal probabilistic relational models over time,
which makes reasoning infeasible. To counteract groundings over time and to
keep reasoning polynomial by restoring a lifted representation, we present
temporal approximate merging (TAMe), which incorporates (i) clustering for
grouping submodels as well as (ii) statistical significance checks to test the
fitness of the clustering outcome. In exchange for faster runtimes, TAMe
introduces a bounded error that becomes negligible over time. Empirical results
show that TAMe significantly improves the runtime performance of inference,
while keeping errors small.
| [
{
"version": "v1",
"created": "Sat, 16 Nov 2019 14:51:55 GMT"
}
] | 1,574,121,600,000 | [
[
"Gehrke",
"Marcel",
""
],
[
"Möller",
"Ralf",
""
],
[
"Braun",
"Tanya",
""
]
] |
1911.07229 | Cosimo Persia | Ana Ozaki, Cosimo Persia, Andrea Mazzullo | Learning Query Inseparable ELH Ontologies | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the complexity of learning query inseparable ELH ontologies in
a variant of Angluin's exact learning model. Given a fixed data instance A* and
a query language Q, we are interested in computing an ontology H that entails
the same queries as a target ontology T on A*, that is, H and T are inseparable
w.r.t. A* and Q. The learner is allowed to pose two kinds of questions. The
first is `Does (T,A)\models q?', with A an arbitrary data instance and q and
query in Q. An oracle replies this question with `yes' or `no'. In the second,
the learner asks `Are H and T inseparable w.r.t. A* and Q?'. If so, the
learning process finishes, otherwise, the learner receives (A*,q) with q in Q,
(T,A*)\models q and (H,A*)\not\models q (or vice-versa). Then, we analyse
conditions in which query inseparability is preserved if A* changes. Finally,
we consider the PAC learning model and a setting where the algorithms learn
from a batch of classified data, limiting interactions with the oracles.
| [
{
"version": "v1",
"created": "Sun, 17 Nov 2019 13:05:38 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Nov 2019 09:38:02 GMT"
},
{
"version": "v3",
"created": "Sun, 17 May 2020 15:59:24 GMT"
},
{
"version": "v4",
"created": "Wed, 17 Jun 2020 10:54:13 GMT"
},
{
"version": "v5",
"created": "Thu, 18 Jun 2020 06:53:13 GMT"
}
] | 1,592,524,800,000 | [
[
"Ozaki",
"Ana",
""
],
[
"Persia",
"Cosimo",
""
],
[
"Mazzullo",
"Andrea",
""
]
] |
1911.07318 | Parisa Zehtabi | Michael Cashmore, Alessandro Cimatti, Daniele Magazzeni, Andrea
Micheli, Parisa Zehtabi | Towards Efficient Anytime Computation and Execution of Decoupled
Robustness Envelopes for Temporal Plans | 8 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the major limitations for the employment of model-based planning and
scheduling in practical applications is the need of costly re-planning when an
incongruence between the observed reality and the formal model is encountered
during execution. Robustness Envelopes characterize the set of possible
contingencies that a plan is able to address without re-planning, but their
exact computation is extremely expensive; furthermore, general robustness
envelopes are not amenable for efficient execution. In this paper, we present a
novel, anytime algorithm to approximate Robustness Envelopes, making them
scalable and executable. This is proven by an experimental analysis showing the
efficiency of the algorithm, and by a concrete case study where the execution
of robustness envelopes significantly reduces the number of re-plannings.
| [
{
"version": "v1",
"created": "Sun, 17 Nov 2019 19:09:22 GMT"
}
] | 1,574,121,600,000 | [
[
"Cashmore",
"Michael",
""
],
[
"Cimatti",
"Alessandro",
""
],
[
"Magazzeni",
"Daniele",
""
],
[
"Micheli",
"Andrea",
""
],
[
"Zehtabi",
"Parisa",
""
]
] |
1911.07712 | Shi Zhenyu | Runsheng Yu, Zhenyu Shi, Xinrun Wang, Rundong Wang, Buhong Liu, Xinwen
Hou, Hanjiang Lai, Bo An | Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing value-factorized based Multi-Agent deep Reinforce-ment Learning
(MARL) approaches are well-performing invarious multi-agent cooperative
environment under thecen-tralized training and decentralized execution(CTDE)
scheme,where all agents are trained together by the centralized valuenetwork
and each agent execute its policy independently. How-ever, an issue remains
open: in the centralized training process,when the environment for the team is
partially observable ornon-stationary, i.e., the observation and action
informationof all the agents cannot represent the global states,
existingmethods perform poorly and sample inefficiently. Regret Min-imization
(RM) can be a promising approach as it performswell in partially observable and
fully competitive settings.However, it tends to model others as opponents and
thus can-not work well under the CTDE scheme. In this work, wepropose a novel
team RM based Bayesian MARL with threekey contributions: (a) we design a novel
RM method to traincooperative agents as a team and obtain a team
regret-basedpolicy for that team; (b) we introduce a novel method to de-compose
the team regret to generate the policy for each agentfor decentralized
execution; (c) to further improve the perfor-mance, we leverage a differential
particle filter (a SequentialMonte Carlo method) network to get an accurate
estimation ofthe state for each agent. Experimental results on two-step ma-trix
games (cooperative game) and battle games (large-scalemixed
cooperative-competitive games) demonstrate that ouralgorithm significantly
outperforms state-of-the-art methods.
| [
{
"version": "v1",
"created": "Mon, 18 Nov 2019 15:41:15 GMT"
}
] | 1,574,121,600,000 | [
[
"Yu",
"Runsheng",
""
],
[
"Shi",
"Zhenyu",
""
],
[
"Wang",
"Xinrun",
""
],
[
"Wang",
"Rundong",
""
],
[
"Liu",
"Buhong",
""
],
[
"Hou",
"Xinwen",
""
],
[
"Lai",
"Hanjiang",
""
],
[
"An",
"Bo",
""
]
] |
1911.07750 | Angelika Kimmig | Efthymia Tsamoura, Victor Gutierrez-Basulto, Angelika Kimmig | Beyond the Grounding Bottleneck: Datalog Techniques for Inference in
Probabilistic Logic Programs (Technical Report) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State-of-the-art inference approaches in probabilistic logic programming
typically start by computing the relevant ground program with respect to the
queries of interest, and then use this program for probabilistic inference
using knowledge compilation and weighted model counting. We propose an
alternative approach that uses efficient Datalog techniques to integrate
knowledge compilation with forward reasoning with a non-ground program. This
effectively eliminates the grounding bottleneck that so far has prohibited the
application of probabilistic logic programming in query answering scenarios
over knowledge graphs, while also providing fast approximations on classical
benchmarks in the field.
| [
{
"version": "v1",
"created": "Mon, 18 Nov 2019 16:29:52 GMT"
}
] | 1,574,121,600,000 | [
[
"Tsamoura",
"Efthymia",
""
],
[
"Gutierrez-Basulto",
"Victor",
""
],
[
"Kimmig",
"Angelika",
""
]
] |
1911.07960 | Tristan Cazenave | Tristan Cazenave and V\'eronique Ventos | The {\alpha}{\mu} Search Algorithm for the Game of Bridge | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | {\alpha}{\mu} is an anytime heuristic search algorithm for incomplete
information games that assumes perfect information for the opponents.
{\alpha}{\mu} addresses the strategy fusion and non-locality problems
encountered by Perfect Information Monte Carlo sampling. In this paper
{\alpha}{\mu} is applied to the game of Bridge.
| [
{
"version": "v1",
"created": "Mon, 18 Nov 2019 21:18:50 GMT"
}
] | 1,574,208,000,000 | [
[
"Cazenave",
"Tristan",
""
],
[
"Ventos",
"Véronique",
""
]
] |
1911.08439 | Rui Zhao | Rui Zhao, Malcolm Atkinson | Towards a computer-interpretable actionable formal model to encode data
governance rules | The non-draft version of this paper has been submitted and accepted
to BC2DC 19 (at IEEE eScience 2019) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the needs of science and business, data sharing and re-use has become an
intensive activity for various areas. In many cases, governance imposes rules
concerning data use, but there is no existing computational technique to help
data-users comply with such rules. We argue that intelligent systems can be
used to improve the situation, by recording provenance records during
processing, encoding the rules and performing reasoning. We present our initial
work, designing formal models for data rules and flow rules and the reasoning
system, as the first step towards helping data providers and data users sustain
productive relationships.
| [
{
"version": "v1",
"created": "Tue, 19 Nov 2019 18:02:52 GMT"
}
] | 1,574,208,000,000 | [
[
"Zhao",
"Rui",
""
],
[
"Atkinson",
"Malcolm",
""
]
] |
1911.08833 | Kai Sauerwald | Kai Sauerwald and Gabriele Kern-Isberner and Christoph Beierle | A Conditional Perspective for Iterated Belief Contraction | null | null | 10.3233/FAIA200180 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to Boutillier, Darwiche, Pearl and others, principles for iterated
revision can be characterised in terms of changing beliefs about conditionals.
For iterated contraction a similar formulation is not known. This is especially
because for iterated belief change the connection between revision and
contraction via the Levi and Harper identity is not straightforward, and
therefore, characterisation results do not transfer easily between iterated
revision and contraction. In this article, we develop an axiomatisation of
iterated contraction in terms of changing conditional beliefs. We prove that
the new set of postulates conforms semantically to the class of operators like
the ones given by Konieczny and Pino P\'erez for iterated contraction.
| [
{
"version": "v1",
"created": "Wed, 20 Nov 2019 11:23:17 GMT"
}
] | 1,643,846,400,000 | [
[
"Sauerwald",
"Kai",
""
],
[
"Kern-Isberner",
"Gabriele",
""
],
[
"Beierle",
"Christoph",
""
]
] |
1911.08872 | Carl Corea | Carl Corea, Matthias Thimm | Towards Inconsistency Measurement in Business Rule Bases | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the application of inconsistency measures to the problem of
analysing business rule bases. Due to some intricacies of the domain of
business rule bases, a straightforward application is not feasible. We
therefore develop some new rationality postulates for this setting as well as
adapt and modify existing inconsistency measures. We further adapt the notion
of inconsistency values (or culpability measures) for this setting and give a
comprehensive feasibility study.
| [
{
"version": "v1",
"created": "Tue, 19 Nov 2019 11:20:42 GMT"
}
] | 1,574,294,400,000 | [
[
"Corea",
"Carl",
""
],
[
"Thimm",
"Matthias",
""
]
] |
1911.09365 | Javier Segovia Aguas | Javier Segovia-Aguas and Sergio Jim\'enez and Anders Jonsson | Generalized Planning with Positive and Negative Examples | Accepted at AAAI-20 (oral presentation) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generalized planning aims at computing an algorithm-like structure
(generalized plan) that solves a set of multiple planning instances. In this
paper we define negative examples for generalized planning as planning
instances that must not be solved by a generalized plan. With this regard the
paper extends the notion of validation of a generalized plan as the problem of
verifying that a given generalized plan solves the set of input positives
instances while it fails to solve a given input set of negative examples. This
notion of plan validation allows us to define quantitative metrics to asses the
generalization capacity of generalized plans. The paper also shows how to
incorporate this new notion of plan validation into a compilation for plan
synthesis that takes both positive and negative instances as input. Experiments
show that incorporating negative examples can accelerate plan synthesis in
several domains and leverage quantitative metrics to evaluate the
generalization capacity of the synthesized plans.
| [
{
"version": "v1",
"created": "Thu, 21 Nov 2019 09:41:56 GMT"
}
] | 1,574,380,800,000 | [
[
"Segovia-Aguas",
"Javier",
""
],
[
"Jiménez",
"Sergio",
""
],
[
"Jonsson",
"Anders",
""
]
] |
1911.12200 | Pawel Gomoluch | Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo, Antonio Bucchiarone | Learning Neural Search Policies for Classical Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heuristic forward search is currently the dominant paradigm in classical
planning. Forward search algorithms typically rely on a single, relatively
simple variation of best-first search and remain fixed throughout the process
of solving a planning problem. Existing work combining multiple search
techniques usually aims at supporting best-first search with an additional
exploratory mechanism, triggered using a handcrafted criterion. A notable
exception is very recent work which combines various search techniques using a
trainable policy. It is, however, confined to a discrete action space
comprising several fixed subroutines.
In this paper, we introduce a parametrized search algorithm template which
combines various search techniques within a single routine. The template's
parameter space defines an infinite space of search algorithms, including,
among others, BFS, local and random search. We further introduce a neural
architecture for designating the values of the search parameters given the
state of the search. This enables expressing neural search policies that change
the values of the parameters as the search progresses. The policies can be
learned automatically, with the objective of maximizing the planner's
performance on a given distribution of planning problems. We consider a
training setting based on a stochastic optimization algorithm known as the
cross-entropy method (CEM). Experimental evaluation of our approach shows that
it is capable of finding effective distribution-specific search policies,
outperforming the relevant baselines.
| [
{
"version": "v1",
"created": "Wed, 27 Nov 2019 14:58:41 GMT"
}
] | 1,574,899,200,000 | [
[
"Gomoluch",
"Pawel",
""
],
[
"Alrajeh",
"Dalal",
""
],
[
"Russo",
"Alessandra",
""
],
[
"Bucchiarone",
"Antonio",
""
]
] |
1911.12399 | Abdur Rakib | Abba Lawan and Abdur Rakib | FT-SWRL: A Fuzzy-Temporal Extension of Semantic Web Rule Language | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present, FT-SWRL, a fuzzy temporal extension to the Semantic Web Rule
Language (SWRL), which combines fuzzy theories based on the valid-time temporal
model to provide a standard approach for modeling imprecise temporal domain
knowledge in OWL ontologies. The proposal introduces a fuzzy temporal model for
the semantic web, which is syntactically defined as a fuzzy temporal SWRL
ontology (SWRL-FTO) with a new set of fuzzy temporal SWRL built-ins for
defining their semantics. The SWRL-FTO hierarchically defines the necessary
linguistic terminologies and variables for the fuzzy temporal model. An example
model demonstrating the usefulness of the fuzzy temporal SWRL built-ins to
model imprecise temporal information is also represented. Fuzzification process
of interval-based temporal logic is further discussed as a reasoning paradigm
for our FT-SWRL rules, with the aim of achieving a complete OWL-based fuzzy
temporal reasoning. Literature review on fuzzy temporal representation
approaches, both within and without the use of ontologies, led to the
conclusion that the FT-SWRL model can authoritatively serve as a formal
specification for handling imprecise temporal expressions on the semantic web.
| [
{
"version": "v1",
"created": "Wed, 27 Nov 2019 19:51:19 GMT"
}
] | 1,575,244,800,000 | [
[
"Lawan",
"Abba",
""
],
[
"Rakib",
"Abdur",
""
]
] |
1911.12949 | Zhanhao Xiao | Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Andreas Herzig, Laurent
Perrussel, Peilin Chen | Refining HTN Methods via Task Insertion with Preferences | 8 pages,7 figures, Accepted in AAAI-20 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchical Task Network (HTN) planning is showing its power in real-world
planning. Although domain experts have partial hierarchical domain knowledge,
it is time-consuming to specify all HTN methods, leaving them incomplete. On
the other hand, traditional HTN learning approaches focus only on declarative
goals, omitting the hierarchical domain knowledge. In this paper, we propose a
novel learning framework to refine HTN methods via task insertion with
completely preserving the original methods. As it is difficult to identify
incomplete methods without designating declarative goals for compound tasks, we
introduce the notion of prioritized preference to capture the incompleteness
possibility of methods. Specifically, the framework first computes the
preferred completion profile w.r.t. the prioritized preference to refine the
incomplete methods. Then it finds the minimal set of refined methods via a
method substitution operation. Experimental analysis demonstrates that our
approach is effective, especially in solving new HTN planning instances.
| [
{
"version": "v1",
"created": "Fri, 29 Nov 2019 04:38:22 GMT"
}
] | 1,575,244,800,000 | [
[
"Xiao",
"Zhanhao",
""
],
[
"Wan",
"Hai",
""
],
[
"Zhuo",
"Hankui Hankz",
""
],
[
"Herzig",
"Andreas",
""
],
[
"Perrussel",
"Laurent",
""
],
[
"Chen",
"Peilin",
""
]
] |
1911.13071 | Sebastian Risi | Sebastian Risi, Julian Togelius | Increasing Generality in Machine Learning through Procedural Content
Generation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Procedural Content Generation (PCG) refers to the practice, in videogames and
other games, of generating content such as levels, quests, or characters
algorithmically. Motivated by the need to make games replayable, as well as to
reduce authoring burden, limit storage space requirements, and enable
particular aesthetics, a large number of PCG methods have been devised by game
developers. Additionally, researchers have explored adapting methods from
machine learning, optimization, and constraint solving to PCG problems. Games
have been widely used in AI research since the inception of the field, and in
recent years have been used to develop and benchmark new machine learning
algorithms. Through this practice, it has become more apparent that these
algorithms are susceptible to overfitting. Often, an algorithm will not learn a
general policy, but instead a policy that will only work for a particular
version of a particular task with particular initial parameters. In response,
researchers have begun exploring randomization of problem parameters to
counteract such overfitting and to allow trained policies to more easily
transfer from one environment to another, such as from a simulated robot to a
robot in the real world. Here we review the large amount of existing work on
PCG, which we believe has an important role to play in increasing the
generality of machine learning methods. The main goal here is to present RL/AI
with new tools from the PCG toolbox, and its secondary goal is to explain to
game developers and researchers a way in which their work is relevant to AI
research.
| [
{
"version": "v1",
"created": "Fri, 29 Nov 2019 11:55:10 GMT"
},
{
"version": "v2",
"created": "Mon, 16 Mar 2020 22:00:52 GMT"
}
] | 1,584,489,600,000 | [
[
"Risi",
"Sebastian",
""
],
[
"Togelius",
"Julian",
""
]
] |
1912.00109 | Xinyang Deng | Xinyang Deng | Belief and plausibility measures for D numbers | 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As a generalization of Dempster-Shafer theory, D number theory provides a
framework to deal with uncertain information with non-exclusiveness and
incompleteness. However, some basic concepts in D number theory are not well
defined. In this note, the belief and plausibility measures for D numbers have
been proposed, and basic properties of these measures have been revealed as
well.
| [
{
"version": "v1",
"created": "Sat, 30 Nov 2019 01:28:18 GMT"
}
] | 1,575,331,200,000 | [
[
"Deng",
"Xinyang",
""
]
] |
1912.00760 | Christian Jilek | Tobias Tempel, Claudia Nieder\'ee, Christian Jilek, Andrea Ceroni,
Heiko Maus, Yannick Runge, Christian Frings | Temporarily Unavailable: Memory Inhibition in Cognitive and Computer
Science | 46 pages, 5 figures, preprint, final version published in IWC | Interacting with Computers, Volume 31, Issue 3, May 2019, pp.
231-249 | 10.1093/iwc/iwz013 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inhibition is one of the core concepts in Cognitive Psychology. The idea of
inhibitory mechanisms actively weakening representations in the human mind has
inspired a great number of studies in various research domains. In contrast,
Computer Science only recently has begun to consider inhibition as a second
basic processing quality beside activation. Here, we review psychological
research on inhibition in memory and link the gained insights with the current
efforts in Computer Science of incorporating inhibitory principles for
optimizing information retrieval in Personal Information Management. Four
common aspects guide this review in both domains: 1. The purpose of inhibition
to increase processing efficiency. 2. Its relation to activation. 3. Its links
to contexts. 4. Its temporariness. In summary, the concept of inhibition has
been used by Computer Science for enhancing software in various ways already.
Yet, we also identify areas for promising future developments of inhibitory
mechanisms, particularly context inhibition.
| [
{
"version": "v1",
"created": "Fri, 15 Nov 2019 07:21:45 GMT"
}
] | 1,575,331,200,000 | [
[
"Tempel",
"Tobias",
""
],
[
"Niederée",
"Claudia",
""
],
[
"Jilek",
"Christian",
""
],
[
"Ceroni",
"Andrea",
""
],
[
"Maus",
"Heiko",
""
],
[
"Runge",
"Yannick",
""
],
[
"Frings",
"Christian",
""
]
] |
1912.00915 | Ta-Chung Chi | Ta-Chung Chi, Mihail Eric, Seokhwan Kim, Minmin Shen, Dilek
Hakkani-tur | Just Ask:An Interactive Learning Framework for Vision and Language
Navigation | 8 pages, accepted to AAAI 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the vision and language navigation task, the agent may encounter ambiguous
situations that are hard to interpret by just relying on visual information and
natural language instructions. We propose an interactive learning framework to
endow the agent with the ability to ask for users' help in such situations. As
part of this framework, we investigate multiple learning approaches for the
agent with different levels of complexity. The simplest model-confusion-based
method lets the agent ask questions based on its confusion, relying on the
predefined confidence threshold of a next action prediction model. To build on
this confusion-based method, the agent is expected to demonstrate more
sophisticated reasoning such that it discovers the timing and locations to
interact with a human. We achieve this goal using reinforcement learning (RL)
with a proposed reward shaping term, which enables the agent to ask questions
only when necessary. The success rate can be boosted by at least 15% with only
one question asked on average during the navigation. Furthermore, we show that
the RL agent is capable of adjusting dynamically to noisy human responses.
Finally, we design a continual learning strategy, which can be viewed as a data
augmentation method, for the agent to improve further utilizing its interaction
history with a human. We demonstrate the proposed strategy is substantially
more realistic and data-efficient compared to previously proposed
pre-exploration techniques.
| [
{
"version": "v1",
"created": "Mon, 2 Dec 2019 16:45:39 GMT"
}
] | 1,575,331,200,000 | [
[
"Chi",
"Ta-Chung",
""
],
[
"Eric",
"Mihail",
""
],
[
"Kim",
"Seokhwan",
""
],
[
"Shen",
"Minmin",
""
],
[
"Hakkani-tur",
"Dilek",
""
]
] |
1912.01160 | Hangyu Mao | Hangyu Mao, Wulong Liu, Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang,
Jun Wang, Zhen Xiao | Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning | Accepted by AAAI2020 with oral presentation
(https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf).
Since AAAI2020 has started, I have the right to distribute this paper on
arXiv | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Social psychology and real experiences show that cognitive consistency plays
an important role to keep human society in order: if people have a more
consistent cognition about their environments, they are more likely to achieve
better cooperation. Meanwhile, only cognitive consistency within a neighborhood
matters because humans only interact directly with their neighbors. Inspired by
these observations, we take the first step to introduce \emph{neighborhood
cognitive consistency} (NCC) into multi-agent reinforcement learning (MARL).
Our NCC design is quite general and can be easily combined with existing MARL
methods. As examples, we propose neighborhood cognition consistent deep
Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations.
Extensive experiments on several challenging tasks (i.e., packet routing, wifi
configuration, and Google football player control) justify the superior
performance of our methods compared with state-of-the-art MARL approaches.
| [
{
"version": "v1",
"created": "Tue, 3 Dec 2019 02:34:11 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Feb 2020 02:38:59 GMT"
}
] | 1,581,379,200,000 | [
[
"Mao",
"Hangyu",
""
],
[
"Liu",
"Wulong",
""
],
[
"Hao",
"Jianye",
""
],
[
"Luo",
"Jun",
""
],
[
"Li",
"Dong",
""
],
[
"Zhang",
"Zhengchao",
""
],
[
"Wang",
"Jun",
""
],
[
"Xiao",
"Zhen",
""
]
] |
1912.01217 | Carroll Wainwright | Carroll L. Wainwright and Peter Eckersley | SafeLife 1.0: Exploring Side Effects in Complex Environments | Updated version was presented at the AAAI SafeAI 2020 Workshop, but
now with updated contact info. Previously presented at the 2019 NeurIPS
Safety and Robustness in Decision Making Workshop | CEUR Workshop Proceedings, 2560 (2020) 117-127 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present SafeLife, a publicly available reinforcement learning environment
that tests the safety of reinforcement learning agents. It contains complex,
dynamic, tunable, procedurally generated levels with many opportunities for
unsafe behavior. Agents are graded both on their ability to maximize their
explicit reward and on their ability to operate safely without unnecessary side
effects. We train agents to maximize rewards using proximal policy optimization
and score them on a suite of benchmark levels. The resulting agents are
performant but not safe -- they tend to cause large side effects in their
environments -- but they form a baseline against which future safety research
can be measured.
| [
{
"version": "v1",
"created": "Tue, 3 Dec 2019 06:44:48 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Feb 2021 05:49:51 GMT"
}
] | 1,614,556,800,000 | [
[
"Wainwright",
"Carroll L.",
""
],
[
"Eckersley",
"Peter",
""
]
] |
1912.01683 | Alexander Turner | Alexander Matt Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad
Tadepalli | Optimal Policies Tend to Seek Power | Accepted to NeurIPS 2021 as spotlight paper. 12 pages, 44 pages with
appendices. Since the 2021 acceptance, we updated the paper to point out that
optimal policies can be qualitatively divorced from real-world learned
policies | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Some researchers speculate that intelligent reinforcement learning (RL)
agents would be incentivized to seek resources and power in pursuit of their
objectives. Other researchers point out that RL agents need not have human-like
power-seeking instincts. To clarify this discussion, we develop the first
formal theory of the statistical tendencies of optimal policies. In the context
of Markov decision processes, we prove that certain environmental symmetries
are sufficient for optimal policies to tend to seek power over the environment.
These symmetries exist in many environments in which the agent can be shut down
or destroyed. We prove that in these environments, most reward functions make
it optimal to seek power by keeping a range of options available and, when
maximizing average reward, by navigating towards larger sets of potential
terminal states.
| [
{
"version": "v1",
"created": "Tue, 3 Dec 2019 20:45:49 GMT"
},
{
"version": "v10",
"created": "Sat, 28 Jan 2023 19:15:05 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Jan 2020 19:25:51 GMT"
},
{
"version": "v3",
"created": "Mon, 13 Apr 2020 14:56:27 GMT"
},
{
"version": "v4",
"created": "Tue, 14 Apr 2020 22:13:56 GMT"
},
{
"version": "v5",
"created": "Fri, 5 Jun 2020 22:41:45 GMT"
},
{
"version": "v6",
"created": "Wed, 2 Dec 2020 21:40:39 GMT"
},
{
"version": "v7",
"created": "Tue, 1 Jun 2021 16:59:04 GMT"
},
{
"version": "v8",
"created": "Sat, 23 Oct 2021 20:12:14 GMT"
},
{
"version": "v9",
"created": "Fri, 3 Dec 2021 17:27:16 GMT"
}
] | 1,675,123,200,000 | [
[
"Turner",
"Alexander Matt",
""
],
[
"Smith",
"Logan",
""
],
[
"Shah",
"Rohin",
""
],
[
"Critch",
"Andrew",
""
],
[
"Tadepalli",
"Prasad",
""
]
] |
1912.01819 | Yanou Ramon | Yanou Ramon, David Martens, Foster Provost, Theodoros Evgeniou | Counterfactual Explanation Algorithms for Behavioral and Textual Data | 24 pages, 7 figures, currently under review | null | 10.1007/s11634-020-00418-3 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the interpretability of predictive systems that use high-dimensonal
behavioral and textual data. Examples include predicting product interest based
on online browsing data and detecting spam emails or objectionable web content.
Recently, counterfactual explanations have been proposed for generating insight
into model predictions, which focus on what is relevant to a particular
instance. Conducting a complete search to compute counterfactuals is very
time-consuming because of the huge dimensionality. To our knowledge, for
behavioral and text data, only one model-agnostic heuristic algorithm (SEDC)
for finding counterfactual explanations has been proposed in the literature.
However, there may be better algorithms for finding counterfactuals quickly.
This study aligns the recently proposed Linear Interpretable Model-agnostic
Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of
counterfactual explanations, and empirically benchmarks their effectiveness and
efficiency against SEDC using a collection of 13 data sets. Results show that
LIME-Counterfactual (LIME-C) and SHAP-Counterfactual (SHAP-C) have low and
stable computation times, but mostly, they are less efficient than SEDC.
However, for certain instances on certain data sets, SEDC's run time is
comparably large. With regard to effectiveness, LIME-C and SHAP-C find
reasonable, if not always optimal, counterfactual explanations. SHAP-C,
however, seems to have difficulties with highly unbalanced data. Because of its
good overall performance, LIME-C seems to be a favorable alternative to SEDC,
which failed for some nonlinear models to find counterfactuals because of the
particular heuristic search algorithm it uses. A main upshot of this paper is
that there is a good deal of room for further research. For example, we propose
algorithmic adjustments that are direct upshots of the paper's findings.
| [
{
"version": "v1",
"created": "Wed, 4 Dec 2019 06:48:34 GMT"
}
] | 1,625,097,600,000 | [
[
"Ramon",
"Yanou",
""
],
[
"Martens",
"David",
""
],
[
"Provost",
"Foster",
""
],
[
"Evgeniou",
"Theodoros",
""
]
] |
1912.02288 | Hengyuan Hu | Hengyuan Hu, Jakob N Foerster | Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years we have seen fast progress on a number of benchmark problems
in AI, with modern methods achieving near or super human performance in Go,
Poker and Dota. One common aspect of all of these challenges is that they are
by design adversarial or, technically speaking, zero-sum. In contrast to these
settings, success in the real world commonly requires humans to collaborate and
communicate with others, in settings that are, at least partially, cooperative.
In the last year, the card game Hanabi has been established as a new benchmark
environment for AI to fill this gap. In particular, Hanabi is interesting to
humans since it is entirely focused on theory of mind, i.e., the ability to
effectively reason over the intentions, beliefs and point of view of other
agents when observing their actions. Learning to be informative when observed
by others is an interesting challenge for Reinforcement Learning (RL):
Fundamentally, RL requires agents to explore in order to discover good
policies. However, when done naively, this randomness will inherently make
their actions less informative to others during training. We present a new deep
multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this
contradiction exploiting the centralized training phase. During training SAD
allows other agents to not only observe the (exploratory) action chosen, but
agents instead also observe the greedy action of their team mates. By combining
this simple intuition with best practices for multi-agent learning, SAD
establishes a new SOTA for learning methods for 2-5 players on the self-play
part of the Hanabi challenge. Our ablations show the contributions of SAD
compared with the best practice components. All of our code and trained agents
are available at https://github.com/facebookresearch/Hanabi_SAD.
| [
{
"version": "v1",
"created": "Wed, 4 Dec 2019 22:34:54 GMT"
},
{
"version": "v2",
"created": "Wed, 12 May 2021 05:32:45 GMT"
}
] | 1,620,864,000,000 | [
[
"Hu",
"Hengyuan",
""
],
[
"Foerster",
"Jakob N",
""
]
] |
1912.02552 | Maor Gaon | Maor Gaon and Ronen I. Brafman | Reinforcement Learning with Non-Markovian Rewards | To Appear in AAAI 2020 | null | null | Report-no: AAAI20 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The standard RL world model is that of a Markov Decision Process (MDP). A
basic premise of MDPs is that the rewards depend on the last state and action
only. Yet, many real-world rewards are non-Markovian. For example, a reward for
bringing coffee only if requested earlier and not yet served, is non-Markovian
if the state only records current requests and deliveries. Past work considered
the problem of modeling and solving MDPs with non-Markovian rewards (NMR), but
we know of no principled approaches for RL with NMR. Here, we address the
problem of policy learning from experience with such rewards. We describe and
evaluate empirically four combinations of the classical RL algorithm Q-learning
and R-max with automata learning algorithms to obtain new RL algorithms for
domains with NMR. We also prove that some of these variants converge to an
optimal policy in the limit.
| [
{
"version": "v1",
"created": "Thu, 5 Dec 2019 13:09:16 GMT"
}
] | 1,575,590,400,000 | [
[
"Gaon",
"Maor",
""
],
[
"Brafman",
"Ronen I.",
""
]
] |
1912.02734 | Martin Diller | Gerhard Brewka, Martin Diller, Georg Heissenberger, Thomas
Linsbichler, Stefan Woltran | Solving Advanced Argumentation Problems with Answer Set Programming | Under consideration in Theory and Practice of Logic Programming
(TPLP) | Theory and Practice of Logic Programming 20 (2020) 391-431 | 10.1017/S1471068419000474 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Powerful formalisms for abstract argumentation have been proposed, among them
abstract dialectical frameworks (ADFs) that allow for a succinct and flexible
specification of the relationship between arguments, and the GRAPPA framework
which allows argumentation scenarios to be represented as arbitrary
edge-labelled graphs. The complexity of ADFs and GRAPPA is located beyond NP
and ranges up to the third level of the polynomial hierarchy. The combined
complexity of Answer Set Programming (ASP) exactly matches this complexity when
programs are restricted to predicates of bounded arity. In this paper, we
exploit this coincidence and present novel efficient translations from ADFs and
GRAPPA to ASP. More specifically, we provide reductions for the five main ADF
semantics of admissible, complete, preferred, grounded, and stable
interpretations, and exemplify how these reductions need to be adapted for
GRAPPA for the admissible, complete and preferred semantics. Under
consideration in Theory and Practice of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Thu, 5 Dec 2019 17:20:34 GMT"
}
] | 1,587,513,600,000 | [
[
"Brewka",
"Gerhard",
""
],
[
"Diller",
"Martin",
""
],
[
"Heissenberger",
"Georg",
""
],
[
"Linsbichler",
"Thomas",
""
],
[
"Woltran",
"Stefan",
""
]
] |
1912.03298 | Siddhant Bhambri | Mudit Verma, Siddhant Bhambri, Saurabh Gupta, Arun Balaji Buduru | Making Smart Homes Smarter: Optimizing Energy Consumption with Human in
the Loop | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rapid advancements in the Internet of Things (IoT) have facilitated more
efficient deployment of smart environment solutions for specific user
requirement. With the increase in the number of IoT devices, it has become
difficult for the user to control or operate every individual smart device into
achieving some desired goal like optimized power consumption, scheduled
appliance running time, etc. Furthermore, existing solutions to automatically
adapt the IoT devices are not capable enough to incorporate the user behavior.
This paper presents a novel approach to accurately configure IoT devices while
achieving the twin objectives of energy optimization along with conforming to
user preferences. Our work comprises of unsupervised clustering of devices'
data to find the states of operation for each device, followed by
probabilistically analyzing user behavior to determine their preferred states.
Eventually, we deploy an online reinforcement learning (RL) agent to find the
best device settings automatically. Results for three different smart homes'
data-sets show the effectiveness of our methodology. To the best of our
knowledge, this is the first time that a practical approach has been adopted to
achieve the above mentioned objectives without any human interaction within the
system.
| [
{
"version": "v1",
"created": "Fri, 6 Dec 2019 18:58:44 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Apr 2020 07:47:20 GMT"
},
{
"version": "v3",
"created": "Mon, 4 May 2020 15:22:16 GMT"
}
] | 1,588,636,800,000 | [
[
"Verma",
"Mudit",
""
],
[
"Bhambri",
"Siddhant",
""
],
[
"Gupta",
"Saurabh",
""
],
[
"Buduru",
"Arun Balaji",
""
]
] |
1912.04816 | Blai Bonet | Blai Bonet and Hector Geffner | Qualitative Numeric Planning: Reductions and Complexity | null | Journal of Artificial Intelligence Research 69 (2020) 923-961 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Qualitative numerical planning is classical planning extended with
non-negative real variables that can be increased or decreased "qualitatively",
i.e., by positive indeterminate amounts. While deterministic planning with
numerical variables is undecidable in general, qualitative numerical planning
is decidable and provides a convenient abstract model for generalized planning.
The solutions to qualitative numerical problems (QNPs) were shown to correspond
to the strong cyclic solutions of an associated fully observable
non-deterministic (FOND) problem that terminate. This leads to a
generate-and-test algorithm for solving QNPs where solutions to a FOND problem
are generated one by one and tested for termination. The computational
shortcomings of this approach for solving QNPs, however, are that it is not
simple to amend FOND planners to generate all solutions, and that the number of
solutions to check can be doubly exponential in the number of variables. In
this work we address these limitations while providing additional insights on
QNPs. More precisely, we introduce two polynomial-time reductions, one from
QNPs to FOND problems and the other from FOND problems to QNPs both of which do
not involve termination tests. A result of these reductions is that QNPs are
shown to have the same expressive power and the same complexity as FOND
problems.
| [
{
"version": "v1",
"created": "Tue, 10 Dec 2019 16:50:41 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Nov 2020 14:48:27 GMT"
}
] | 1,606,694,400,000 | [
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
1912.04999 | Maen Alzubi | Maen Alzubi, Mohammad Almseidin, Mohd Aaqib Lone and Szilveszter
Kovacs | Fuzzy Rule Interpolation Toolbox for the GNU Open-Source OCTAVE | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In most fuzzy control applications (applying classical fuzzy reasoning), the
reasoning method requires a complete fuzzy rule-base, i.e all the possible
observations must be covered by the antecedents of the fuzzy rules, which is
not always available. Fuzzy control systems based on the Fuzzy Rule
Interpolation (FRI) concept play a major role in different platforms, in case
if only a sparse fuzzy rule-base is available. This cases the fuzzy model
contains only the most relevant rules, without covering all the antecedent
universes. The first FRI toolbox being able to handle different FRI methods was
developed by Johanyak et. al. in 2006 for the MATLAB environment. The goal of
this paper is to introduce some details of the adaptation of the FRI toolbox to
support the GNU/OCTAVE programming language. The OCTAVE Fuzzy Rule
Interpolation (OCTFRI) Toolbox is an open-source toolbox for OCTAVE programming
language, providing a large functionally compatible subset of the MATLAB FRI
toolbox as well as many extensions. The OCTFRI Toolbox includes functions that
enable the user to evaluate Fuzzy Inference Systems (FISs) from the command
line and from OCTAVE scripts, read/write FISs and OBS to/from files, and
produce a graphical visualisation of both the membership functions and the FIS
outputs. Future work will focus on implementing advanced fuzzy inference
techniques and GUI tools.
| [
{
"version": "v1",
"created": "Tue, 10 Dec 2019 22:04:29 GMT"
}
] | 1,576,108,800,000 | [
[
"Alzubi",
"Maen",
""
],
[
"Almseidin",
"Mohammad",
""
],
[
"Lone",
"Mohd Aaqib",
""
],
[
"Kovacs",
"Szilveszter",
""
]
] |
1912.05407 | Yanghao Lin | Xu Cao, Yanghao Lin | UCT-ADP Progressive Bias Algorithm for Solving Gomoku | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We combine Adaptive Dynamic Programming (ADP), a reinforcement learning
method and UCB applied to trees (UCT) algorithm with a more powerful heuristic
function based on Progressive Bias method and two pruning strategies for a
traditional board game Gomoku. For the Adaptive Dynamic Programming part, we
train a shallow forward neural network to give a quick evaluation of Gomoku
board situations. UCT is a general approach in MCTS as a tree policy. Our
framework use UCT to balance the exploration and exploitation of Gomoku game
trees while we also apply powerful pruning strategies and heuristic function to
re-select the available 2-adjacent grids of the state and use ADP instead of
simulation to give estimated values of expanded nodes. Experiment result shows
that this method can eliminate the search depth defect of the simulation
process and converge to the correct value faster than single UCT. This approach
can be applied to design new Gomoku AI and solve other Gomoku-like board game.
| [
{
"version": "v1",
"created": "Wed, 11 Dec 2019 16:05:39 GMT"
}
] | 1,576,108,800,000 | [
[
"Cao",
"Xu",
""
],
[
"Lin",
"Yanghao",
""
]
] |
1912.05935 | Eros Grigoryan | E. Grigoryan | Linear algorithm for solution n-Queens Completion problem | 37 pages, 11 figures, 2 tables, Prepared for publication in "Discrete
Mathematics & Theoretical Computer Science" | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A linear algorithm is described for solving the n-Queens Completion problem
for an arbitrary composition of k queens, consistently distributed on a
chessboard of size n x n. Two important rules are used in the algorithm: a) the
rule of sequential risk elimination for the entire system as a whole; b) the
rule of formation of minimal damage in the given selection conditions. For any
composition of k queens (1<= k<n), a solution is provided, or a decision is
made that this composition can't be completed. The probability of an error in
making such a decision does not exceed 0.0001, and its value decreases, with
increasing n. It is established that the average time, required for the queen
to be placed on one row, decreases with increasing value of n. A description is
given of two random selection models and the results of their comparative
analysis. A model for organizing the Back Tracking procedure is proposed based
on the separation of the solution matrix into two basic levels. Regression
formulas are given for the dependence of basic levels on the value of n. It was
found that for n=(7-100000) the number of solutions in which the Back Tracking
procedure has never been used exceeds 35%. Moreover, for n=(320-22500), the
number of such cases exceeds 50 %. A quick algorithm for verifying the
correctness of n-Queens problem solution or arbitrary composition of k queens
is given.
| [
{
"version": "v1",
"created": "Thu, 5 Dec 2019 14:21:16 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Dec 2019 10:05:41 GMT"
}
] | 1,577,836,800,000 | [
[
"Grigoryan",
"E.",
""
]
] |
1912.06594 | Thierry Denoeux | Thierry Denoeux and Prakash P. Shenoy | An Interval-Valued Utility Theory for Decision Making with
Dempster-Shafer Belief Functions | null | International Journal of Approximate Reasoning, vol. 124, pages
194-216, 2020 | 10.1016/j.ijar.2020.06.008 | Working Paper No. 336, August 2019, School of Business, University
of Kansas | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main goal of this paper is to describe an axiomatic utility theory for
Dempster-Shafer belief function lotteries. The axiomatic framework used is
analogous to von Neumann-Morgenstern's utility theory for probabilistic
lotteries as described by Luce and Raiffa. Unlike the probabilistic case, our
axiomatic framework leads to interval-valued utilities, and therefore, to a
partial (incomplete) preference order on the set of all belief function
lotteries. If the belief function reference lotteries we use are Bayesian
belief functions, then our representation theorem coincides with Jaffray's
representation theorem for his linear utility theory for belief functions. We
illustrate our representation theorem using some examples discussed in the
literature, and we propose a simple model for assessing utilities based on an
interval-valued pessimism index representing a decision-maker's attitude to
ambiguity and indeterminacy. Finally, we compare our decision theory with those
proposed by Jaffray, Smets, Dubois et al., Giang and Shenoy, and Shafer.
| [
{
"version": "v1",
"created": "Fri, 13 Dec 2019 16:37:32 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Jun 2020 02:27:35 GMT"
}
] | 1,594,857,600,000 | [
[
"Denoeux",
"Thierry",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1912.06612 | Henri Prade M | Zied Bouraoui and Antoine Cornu\'ejols and Thierry Den{\oe}ux and
S\'ebastien Destercke and Didier Dubois and Romain Guillaume and Jo\~ao
Marques-Silva and J\'er\^ome Mengin and Henri Prade and Steven Schockaert and
Mathieu Serrurier and Christel Vrain | From Shallow to Deep Interactions Between Knowledge Representation,
Reasoning and Machine Learning (Kay R. Amel group) | 53 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a tentative and original survey of meeting points between
Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two
areas which have been developing quite separately in the last three decades.
Some common concerns are identified and discussed such as the types of used
representation, the roles of knowledge and data, the lack or the excess of
information, or the need for explanations and causal understanding. Then some
methodologies combining reasoning and learning are reviewed (such as inductive
logic programming, neuro-symbolic reasoning, formal concept analysis,
rule-based representations and ML, uncertainty in ML, or case-based reasoning
and analogical reasoning), before discussing examples of synergies between KRR
and ML (including topics such as belief functions on regression, EM algorithm
versus revision, the semantic description of vector representations, the
combination of deep learning with high level inference, knowledge graph
completion, declarative frameworks for data mining, or preferences and
recommendation). This paper is the first step of a work in progress aiming at a
better mutual understanding of research in KRR and ML, and how they could
cooperate.
| [
{
"version": "v1",
"created": "Fri, 13 Dec 2019 17:20:52 GMT"
}
] | 1,576,454,400,000 | [
[
"Bouraoui",
"Zied",
""
],
[
"Cornuéjols",
"Antoine",
""
],
[
"Denœux",
"Thierry",
""
],
[
"Destercke",
"Sébastien",
""
],
[
"Dubois",
"Didier",
""
],
[
"Guillaume",
"Romain",
""
],
[
"Marques-Silva",
"João",
""
],
[
"Mengin",
"Jérôme",
""
],
[
"Prade",
"Henri",
""
],
[
"Schockaert",
"Steven",
""
],
[
"Serrurier",
"Mathieu",
""
],
[
"Vrain",
"Christel",
""
]
] |
1912.07045 | Janarthanan Rajendran | Janarthanan Rajendran, Richard Lewis, Vivek Veeriah, Honglak Lee and
Satinder Singh | How Should an Agent Practice? | AAAI-2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for learning intrinsic reward functions to drive the
learning of an agent during periods of practice in which extrinsic task rewards
are not available. During practice, the environment may differ from the one
available for training and evaluation with extrinsic rewards. We refer to this
setup of alternating periods of practice and objective evaluation as
practice-match, drawing an analogy to regimes of skill acquisition common for
humans in sports and games. The agent must effectively use periods in the
practice environment so that performance improves during matches. In the
proposed method the intrinsic practice reward is learned through a
meta-gradient approach that adapts the practice reward parameters to reduce the
extrinsic match reward loss computed from matches. We illustrate the method on
a simple grid world, and evaluate it in two games in which the practice
environment differs from match: Pong with practice against a wall without an
opponent, and PacMan with practice in a maze without ghosts. The results show
gains from learning in practice in addition to match periods over learning in
matches only.
| [
{
"version": "v1",
"created": "Sun, 15 Dec 2019 14:14:51 GMT"
}
] | 1,576,540,800,000 | [
[
"Rajendran",
"Janarthanan",
""
],
[
"Lewis",
"Richard",
""
],
[
"Veeriah",
"Vivek",
""
],
[
"Lee",
"Honglak",
""
],
[
"Singh",
"Satinder",
""
]
] |
1912.07060 | Mayukh Das | Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa and Sriraam Natarajan | One-Shot Induction of Generalized Logical Concepts via Human Guidance | STARAI '20, Workshop version | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of learning generalized first-order representations
of concepts from a single example. To address this challenging problem, we
augment an inductive logic programming learner with two novel algorithmic
contributions. First, we define a distance measure between candidate concept
representations that improves the efficiency of search for target concept and
generalization. Second, we leverage richer human inputs in the form of advice
to improve the sample-efficiency of learning. We prove that the proposed
distance measure is semantically valid and use that to derive a PAC bound. Our
experimental analysis on diverse concept learning tasks demonstrates both the
effectiveness and efficiency of the proposed approach over a first-order
concept learner using only examples.
| [
{
"version": "v1",
"created": "Sun, 15 Dec 2019 15:31:45 GMT"
}
] | 1,576,540,800,000 | [
[
"Das",
"Mayukh",
""
],
[
"Ramanan",
"Nandini",
""
],
[
"Doppa",
"Janardhan Rao",
""
],
[
"Natarajan",
"Sriraam",
""
]
] |
1912.08664 | Aleksandr Panov | Alexey Skrynnik, Aleksey Staroverov, Ermek Aitygulov, Kirill Aksenov,
Vasilii Davydov, Aleksandr I. Panov | Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present Hierarchical Deep Q-Network (HDQfD) that took first place in the
MineRL competition. HDQfD works on imperfect demonstrations and utilizes the
hierarchical structure of expert trajectories. We introduce the procedure of
extracting an effective sequence of meta-actions and subgoals from
demonstration data. We present a structured task-dependent replay buffer and
adaptive prioritizing technique that allow the HDQfD agent to gradually erase
poor-quality expert data from the buffer. In this paper, we present the details
of the HDQfD algorithm and give the experimental results in the Minecraft
domain.
| [
{
"version": "v1",
"created": "Wed, 18 Dec 2019 15:30:49 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Feb 2020 07:49:09 GMT"
},
{
"version": "v3",
"created": "Thu, 9 Jul 2020 16:37:44 GMT"
},
{
"version": "v4",
"created": "Mon, 13 Jul 2020 09:24:50 GMT"
}
] | 1,594,684,800,000 | [
[
"Skrynnik",
"Alexey",
""
],
[
"Staroverov",
"Aleksey",
""
],
[
"Aitygulov",
"Ermek",
""
],
[
"Aksenov",
"Kirill",
""
],
[
"Davydov",
"Vasilii",
""
],
[
"Panov",
"Aleksandr I.",
""
]
] |
1912.09024 | Andreas Holzinger | Andreas Holzinger, Andr\'e Carrington, Heimo M\"uller | Measuring the Quality of Explanations: The System Causability Scale
(SCS). Comparing Human and Machine Explanations | 6 pages, 1 figure, 1 table, will appear in Springer/Nature KI -
K\"unstliche Intelligenz (2020), Volume 34, Issue 2 | Springer/Nature KI Kuenstliche Intelligenz 34, 193-198 (2020) | 10.1007/s13218-020-00636-z | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent success in Artificial Intelligence (AI) and Machine Learning (ML)
allow problem solving automatically without any human intervention. Autonomous
approaches can be very convenient. However, in certain domains, e.g., in the
medical domain, it is necessary to enable a domain expert to understand, why an
algorithm came up with a certain result. Consequently, the field of Explainable
AI (xAI) rapidly gained interest worldwide in various domains, particularly in
medicine. Explainable AI studies transparency and traceability of opaque AI/ML
and there are already a huge variety of methods. For example with layer-wise
relevance propagation relevant parts of inputs to, and representations in, a
neural network which caused a result, can be highlighted. This is a first
important step to ensure that end users, e.g., medical professionals, assume
responsibility for decision making with AI/ML and of interest to professionals
and regulators. Interactive ML adds the component of human expertise to AI/ML
processes by enabling them to re-enact and retrace AI/ML results, e.g. let them
check it for plausibility. This requires new human-AI interfaces for
explainable AI. In order to build effective and efficient interactive human-AI
interfaces we have to deal with the question of how to evaluate the quality of
explanations given by an explainable AI system. In this paper we introduce our
System Causability Scale (SCS) to measure the quality of explanations. It is
based on our notion of Causability (Holzinger et al., 2019) combined with
concepts adapted from a widely accepted usability scale.
| [
{
"version": "v1",
"created": "Thu, 19 Dec 2019 05:34:08 GMT"
}
] | 1,614,729,600,000 | [
[
"Holzinger",
"Andreas",
""
],
[
"Carrington",
"André",
""
],
[
"Müller",
"Heimo",
""
]
] |
1912.09211 | Jorge Fandinno | Jorge Fandinno and Johannes Fichte | Proceedings of the twelfth Workshop on Answer Set Programming and Other
Computing Paradigms 2019 | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the twelfth Workshop on Answer Set Programming and
Other Computing Paradigms (ASPOCP) 2019, which was held in Philadelphia, USA,
June 3rd , 2019.
| [
{
"version": "v1",
"created": "Thu, 21 Nov 2019 15:47:40 GMT"
}
] | 1,576,800,000,000 | [
[
"Fandinno",
"Jorge",
""
],
[
"Fichte",
"Johannes",
""
]
] |
1912.09987 | Daniel A Arag\~ao | Daniel Arag\~ao Abreu Filho | Busca de melhor caminho entre m\'ultiplas origens e m\'ultiplos destinos
em redes complexas que representam cidades | 40 pages, in Portuguese, 21 figures, 4 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Was investigated in this paper the use of a search strategy in the problem of
finding the best path among multiple origins and multiple destinations. In this
kind of problem, it must be decided within a lot of combinations which is the
best origin and the best destination, and also the best path between these two
regions. One remarkable difficulty to answer this sort of problem is to perform
the search in a reduced time. This monography is a extension of previous
research in which the problem described here was studied only in a bus network
in the city of Fortaleza. This extension consisted of an exploration of the
search strategy in graphs that represent public ways in cities like Fortaleza,
Mumbai and Tokyo. Using this strategy with a heuristic algorithm, Haversine
distance, was noticed that is possible to reduce substantially the time of the
search, but introducing an error because of the loss of the admissible
characteristic of the heuristic function applied.
| [
{
"version": "v1",
"created": "Wed, 18 Dec 2019 13:04:22 GMT"
}
] | 1,577,059,200,000 | [
[
"Filho",
"Daniel Aragão Abreu",
""
]
] |
1912.10005 | Holger Lyre | Holger Lyre | Does AlphaGo actually play Go? Concerning the State Space of Artificial
Intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The overarching goal of this paper is to develop a general model of the state
space of AI. Given the breathtaking progress in AI research and technologies in
recent years, such conceptual work is of substantial theoretical interest. The
present AI hype is mainly driven by the triumph of deep learning neural
networks. As the distinguishing feature of such networks is the ability to
self-learn, self-learning is identified as one important dimension of the AI
state space. Another main dimension lies in the possibility to go over from
specific to more general types of problems. The third main dimension is
provided by semantic grounding. Since this is a philosophically complex and
controversial dimension, a larger part of the paper is devoted to it. We take a
fresh look at known foundational arguments in the philosophy of mind and
cognition that are gaining new relevance in view of the recent AI developments
including the blockhead objection, the Turing test, the symbol grounding
problem, the Chinese room argument, and general use-theoretic considerations of
meaning. Finally, the AI state space, spanned by the main dimensions
generalization, grounding and "selfx-ness", possessing self-x properties such
as self-learning, is outlined.
| [
{
"version": "v1",
"created": "Fri, 13 Dec 2019 23:35:18 GMT"
}
] | 1,577,059,200,000 | [
[
"Lyre",
"Holger",
""
]
] |
1912.10445 | Fabricio Olivetti de Franca | Fabricio Olivetti de Franca, Denis Fantinato, Karine Miras, A.E.
Eiben, Patricia A. Vargas | EvoMan: Game-playing Competition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a competition proposal for evolving Intelligent Agents
for the game-playing framework called EvoMan. The framework is based on the
boss fights of the game called Mega Man II developed by Capcom. For this
particular competition, the main goal is to beat all of the eight bosses using
a generalist strategy. In other words, the competitors should train the agent
to beat a set of the bosses and then the agent will be evaluated by its
performance against all eight bosses. At the end of this paper, the competitors
are provided with baseline results so that they can have an intuition on how
good their results are.
| [
{
"version": "v1",
"created": "Sun, 22 Dec 2019 13:30:41 GMT"
},
{
"version": "v2",
"created": "Sat, 28 Dec 2019 14:39:10 GMT"
},
{
"version": "v3",
"created": "Sat, 4 Jan 2020 14:24:55 GMT"
}
] | 1,578,355,200,000 | [
[
"de Franca",
"Fabricio Olivetti",
""
],
[
"Fantinato",
"Denis",
""
],
[
"Miras",
"Karine",
""
],
[
"Eiben",
"A. E.",
""
],
[
"Vargas",
"Patricia A.",
""
]
] |
1912.11038 | Christophe Demko | Christophe Demko and Karell Bertet and Cyril Faucher and
Jean-Fran\c{c}ois Viaud and Serge\"i Kuznetsov | Next Priority Concept: A new and generic algorithm computing concepts
from complex and heterogeneous data | 28 pages, 8 figures, 7 algorithms | null | 10.1016/j.tcs.2020.08.026 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we present a new data type agnostic algorithm calculating a
concept lattice from heterogeneous and complex data. Our NextPriorityConcept
algorithm is first introduced and proved in the binary case as an extension of
Bordat's algorithm with the notion of strategies to select only some
predecessors of each concept, avoiding the generation of unreasonably large
lattices. The algorithm is then extended to any type of data in a generic way.
It is inspired from pattern structure theory, where data are locally described
by predicates independent of their types, allowing the management of
heterogeneous data.
| [
{
"version": "v1",
"created": "Fri, 20 Dec 2019 19:55:39 GMT"
}
] | 1,599,523,200,000 | [
[
"Demko",
"Christophe",
""
],
[
"Bertet",
"Karell",
""
],
[
"Faucher",
"Cyril",
""
],
[
"Viaud",
"Jean-François",
""
],
[
"Kuznetsov",
"Sergeï",
""
]
] |
1912.11323 | Gal Cohensius | Gal Cohensius, Reshef Meir, Nadav Oved and Roni Stern | Bidding in Spades | 13 pages, 7 figures, to be published in ECAI 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a Spades bidding algorithm that is superior to recreational human
players and to publicly available bots. Like in Bridge, the game of Spades is
composed of two independent phases, \textit{bidding} and \textit{playing}. This
paper focuses on the bidding algorithm, since this phase holds a precise
challenge: based on the input, choose the bid that maximizes the agent's
winning probability. Our \emph{Bidding-in-Spades} (BIS) algorithm heuristically
determines the bidding strategy by comparing the expected utility of each
possible bid. A major challenge is how to estimate these expected utilities. To
this end, we propose a set of domain-specific heuristics, and then correct them
via machine learning using data from real-world players. The \BIS algorithm we
present can be attached to any playing algorithm. It beats rule-based bidding
bots when all use the same playing component. When combined with a rule-based
playing algorithm, it is superior to the average recreational human.
| [
{
"version": "v1",
"created": "Tue, 24 Dec 2019 12:49:53 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Feb 2020 13:45:27 GMT"
}
] | 1,581,379,200,000 | [
[
"Cohensius",
"Gal",
""
],
[
"Meir",
"Reshef",
""
],
[
"Oved",
"Nadav",
""
],
[
"Stern",
"Roni",
""
]
] |
1912.11462 | Thibaut Vidal | Florian Arnold, \'Italo Santana, Kenneth S\"orensen, Thibaut Vidal | PILS: Exploring high-order neighborhoods by pattern mining and injection | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce pattern injection local search (PILS), an optimization strategy
that uses pattern mining to explore high-order local-search neighborhoods, and
illustrate its application on the vehicle routing problem. PILS operates by
storing a limited number of frequent patterns from elite solutions. During the
local search, each pattern is used to define one move in which 1) incompatible
edges are disconnected, 2) the edges defined by the pattern are reconnected,
and 3) the remaining solution fragments are optimally reconnected. Each such
move is accepted only in case of solution improvement. As visible in our
experiments, this strategy results in a new paradigm of local search, which
complements and enhances classical search approaches in a controllable amount
of computational time. We demonstrate that PILS identifies useful high-order
moves (e.g., 9-opt and 10-opt) which would otherwise not be found by
enumeration, and that it significantly improves the performance of
state-of-the-art population-based and neighborhood-centered metaheuristics.
| [
{
"version": "v1",
"created": "Tue, 24 Dec 2019 18:36:07 GMT"
}
] | 1,577,232,000,000 | [
[
"Arnold",
"Florian",
""
],
[
"Santana",
"Ítalo",
""
],
[
"Sörensen",
"Kenneth",
""
],
[
"Vidal",
"Thibaut",
""
]
] |
1912.11599 | Zhenzhen Gu | Zhenzhen Gu, Cungen Cao, Ya Wang and Yuefei Sui | A Logical Model for Supporting Social Commonsense Knowledge Acquisition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To make machine exhibit human-like abilities in the domains like robotics and
conversation, social commonsense knowledge (SCK), i.e., common sense about
social contexts and social roles, is absolutely necessarily. Therefor, our
ultimate goal is to acquire large-scale SCK to support much more intelligent
applications. Before that, we need to know clearly what is SCK and how to
represent it, since automatic information processing requires data and
knowledge are organized in structured and semantically related ways. For this
reason, in this paper, we identify and formalize three basic types of SCK based
on first-order theory. Firstly, we identify and formalize the
interrelationships, such as having-role and having-social_relation, among
social contexts, roles and players from the perspective of considering both
contexts and roles as first-order citizens and not generating role instances.
Secondly, we provide a four level structure to identify and formalize the
intrinsic information, such as events and desires, of social contexts, roles
and players, and illustrate the way of harvesting the intrinsic information of
social contexts and roles from the exhibition of players in concrete contexts.
And thirdly, enlightened by some observations of actual contexts, we further
introduce and formalize the embedding of social contexts, and depict the way of
excavating the intrinsic information of social contexts and roles from the
embedded smaller and simpler contexts. The results of this paper lay the
foundation not only for formalizing much more complex SCK but also for
acquiring these three basic types of SCK.
| [
{
"version": "v1",
"created": "Wed, 25 Dec 2019 05:50:20 GMT"
}
] | 1,577,664,000,000 | [
[
"Gu",
"Zhenzhen",
""
],
[
"Cao",
"Cungen",
""
],
[
"Wang",
"Ya",
""
],
[
"Sui",
"Yuefei",
""
]
] |
1912.12613 | Pulkit Verma | Pulkit Verma, Shashank Rao Marpally, Siddharth Srivastava | Asking the Right Questions: Learning Interpretable Action Models Through
Query Answering | AAAI 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper develops a new approach for estimating an interpretable,
relational model of a black-box autonomous agent that can plan and act. Our
main contributions are a new paradigm for estimating such models using a
minimal query interface with the agent, and a hierarchical querying algorithm
that generates an interrogation policy for estimating the agent's internal
model in a vocabulary provided by the user. Empirical evaluation of our
approach shows that despite the intractable search space of possible agent
models, our approach allows correct and scalable estimation of interpretable
agent models for a wide class of black-box autonomous agents. Our results also
show that this approach can use predicate classifiers to learn interpretable
models of planning agents that represent states as images.
| [
{
"version": "v1",
"created": "Sun, 29 Dec 2019 09:05:06 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Feb 2020 02:45:09 GMT"
},
{
"version": "v3",
"created": "Sat, 18 Jul 2020 02:28:51 GMT"
},
{
"version": "v4",
"created": "Mon, 14 Sep 2020 17:17:24 GMT"
},
{
"version": "v5",
"created": "Sat, 6 Mar 2021 04:44:49 GMT"
},
{
"version": "v6",
"created": "Fri, 9 Apr 2021 16:17:14 GMT"
}
] | 1,618,185,600,000 | [
[
"Verma",
"Pulkit",
""
],
[
"Marpally",
"Shashank Rao",
""
],
[
"Srivastava",
"Siddharth",
""
]
] |
1912.12633 | Marti Sanchez-Fibla | Marco Jerome Gasparrini, Mart\'i S\'anchez-Fibla | Loss aversion fosters coordination among independent reinforcement
learners | 5 pages, 2 figures, appeared in CCIA 2018 | null | 10.3233/978-1-61499-918-8-307 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We study what are the factors that can accelerate the emergence of
collaborative behaviours among independent selfish learning agents. We depart
from the "Battle of the Exes" (BoE), a spatial repeated game from which human
behavioral data has been obtained (by Hawkings and Goldstone, 2016) that we
find interesting because it considers two cases: a classic game theory version,
called ballistic, in which agents can only make one action/decision (equivalent
to the Battle of the Sexes) and a spatial version, called dynamic, in which
agents can change decision (a spatial continuous version). We model both
versions of the game with independent reinforcement learning agents and we
manipulate the reward function transforming it into an utility introducing
"loss aversion": the reward that an agent obtains can be perceived as less
valuable when compared to what the other got. We prove experimentally the
introduction of loss aversion fosters cooperation by accelerating its
appearance, and by making it possible in some cases like in the dynamic
condition. We suggest that this may be an important factor explaining the rapid
converge of human behaviour towards collaboration reported in the experiment of
Hawkings and Goldstone.
| [
{
"version": "v1",
"created": "Sun, 29 Dec 2019 11:22:30 GMT"
}
] | 1,577,836,800,000 | [
[
"Gasparrini",
"Marco Jerome",
""
],
[
"Sánchez-Fibla",
"Martí",
""
]
] |
1912.12957 | EPTCS | Claudia Schon, Sophie Siebert, Frieder Stolzenburg | Using ConceptNet to Teach Common Sense to an Automated Theorem Prover | In Proceedings ARCADE 2019, arXiv:1912.11786 | EPTCS 311, 2019, pp. 19-24 | 10.4204/EPTCS.311.3 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The CoRg system is a system to solve commonsense reasoning problems. The core
of the CoRg system is the automated theorem prover Hyper that is fed with large
amounts of background knowledge. This background knowledge plays a crucial role
in solving commonsense reasoning problems. In this paper we present different
ways to use knowledge graphs as background knowledge and discuss challenges
that arise.
| [
{
"version": "v1",
"created": "Mon, 30 Dec 2019 15:13:53 GMT"
}
] | 1,577,836,800,000 | [
[
"Schon",
"Claudia",
""
],
[
"Siebert",
"Sophie",
""
],
[
"Stolzenburg",
"Frieder",
""
]
] |
1912.13186 | Robert B. Allen | Robert B. Allen | Definitions and Semantic Simulations Based on Object-Oriented Analysis
and Modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have proposed going beyond traditional ontologies to use rich semantics
implemented in programming languages for modeling. In this paper, we discuss
the application of executable semantic models to two examples, first a
structured definition of a waterfall and second the cardiopulmonary system. We
examine the components of these models and the way those components interact.
Ultimately, such models should provide the basis for direct representation.
| [
{
"version": "v1",
"created": "Tue, 31 Dec 2019 05:59:02 GMT"
}
] | 1,577,836,800,000 | [
[
"Allen",
"Robert B.",
""
]
] |
2001.01007 | Volodymyr Leno | Volodymyr Leno, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi,
Artem Polyvyanyy | Automated Discovery of Data Transformations for Robotic Process
Automation | 8 pages, 5 figures. To be published in proceedings of AAAI-20
workshop on Intelligent Process Automation | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robotic Process Automation (RPA) is a technology for automating repetitive
routines consisting of sequences of user interactions with one or more
applications. In order to fully exploit the opportunities opened by RPA,
companies need to discover which specific routines may be automated, and how.
In this setting, this paper addresses the problem of analyzing User Interaction
(UI) logs in order to discover routines where a user transfers data from one
spreadsheet or (Web) form to another. The paper maps this problem to that of
discovering data transformations by example - a problem for which several
techniques are available. The paper shows that a naive application of a
state-of-the-art technique for data transformation discovery is computationally
inefficient. Accordingly, the paper proposes two optimizations that take
advantage of the information in the UI log and the fact that data transfers
across applications typically involve copying alphabetic and numeric tokens
separately. The proposed approach and its optimizations are evaluated using UI
logs that replicate a real-life repetitive data transfer routine.
| [
{
"version": "v1",
"created": "Fri, 3 Jan 2020 23:15:45 GMT"
}
] | 1,578,355,200,000 | [
[
"Leno",
"Volodymyr",
""
],
[
"Dumas",
"Marlon",
""
],
[
"La Rosa",
"Marcello",
""
],
[
"Maggi",
"Fabrizio Maria",
""
],
[
"Polyvyanyy",
"Artem",
""
]
] |
2001.01326 | Jakub Kowalski | Jakub Kowalski, Rados{\l}aw Miernik | Evolutionary Approach to Collectible Card Game Arena Deckbuilding using
Active Genes | Accepted to IEEE Congress on Evolutionary Computation 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we evolve a card-choice strategy for the arena mode of Legends
of Code and Magic, a programming game inspired by popular collectible card
games like Hearthstone or TES: Legends. In the arena game mode, before each
match, a player has to construct his deck choosing cards one by one from the
previously unknown options. Such a scenario is difficult from the optimization
point of view, as not only the fitness function is non-deterministic, but its
value, even for a given problem instance, is impossible to be calculated
directly and can only be estimated with simulation-based approaches. We propose
a variant of the evolutionary algorithm that uses a concept of an active gene
to reduce the range of the operators only to generation-specific subsequences
of the genotype. Thus, we batched learning process and constrained evolutionary
updates only to the cards relevant for the particular draft, without forgetting
the knowledge from the previous tests. We developed and tested various
implementations of this idea, investigating their performance by taking into
account the computational cost of each variant. Performed experiments show that
some of the introduced active-genes algorithms tend to learn faster and produce
statistically better draft policies than the compared methods.
| [
{
"version": "v1",
"created": "Sun, 5 Jan 2020 22:46:08 GMT"
},
{
"version": "v2",
"created": "Wed, 13 May 2020 12:27:51 GMT"
}
] | 1,589,414,400,000 | [
[
"Kowalski",
"Jakub",
""
],
[
"Miernik",
"Radosław",
""
]
] |
2001.01577 | Francisco Garcia | Francisco M. Garcia, Chris Nota, Philip S. Thomas | Learning Reusable Options for Multi-Task Reinforcement Learning | 15 pages, 7 figures, pre-print | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL) has become an increasingly active area of
research in recent years. Although there are many algorithms that allow an
agent to solve tasks efficiently, they often ignore the possibility that prior
experience related to the task at hand might be available. For many practical
applications, it might be unfeasible for an agent to learn how to solve a task
from scratch, given that it is generally a computationally expensive process;
however, prior experience could be leveraged to make these problems tractable
in practice. In this paper, we propose a framework for exploiting existing
experience by learning reusable options. We show that after an agent learns
policies for solving a small number of problems, we are able to use the
trajectories generated from those policies to learn reusable options that allow
an agent to quickly learn how to solve novel and related problems.
| [
{
"version": "v1",
"created": "Mon, 6 Jan 2020 13:49:31 GMT"
}
] | 1,578,355,200,000 | [
[
"Garcia",
"Francisco M.",
""
],
[
"Nota",
"Chris",
""
],
[
"Thomas",
"Philip S.",
""
]
] |
2001.01772 | Thomas Unger | Thomas A. Unger, Elia Bruni | Generalizing Emergent Communication | Summary of a master thesis by Thomas A. Unger, supervised by Elia
Bruni at the University of Amsterdam from January to August 2019. 9 pages, 6
figures, 2 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We converted the recently developed BabyAI grid world platform to a
sender/receiver setup in order to test the hypothesis that established deep
reinforcement learning techniques are sufficient to incentivize the emergence
of a grounded discrete communication protocol between generalized agents. This
is in contrast to previous experiments that employed straight-through
estimation or specialized inductive biases. Our results show that these can
indeed be avoided, by instead providing proper environmental incentives.
Moreover, they show that a longer interval between communications incentivized
more abstract semantics. In some cases, the communicating agents adapted to new
environments more quickly than a monolithic agent, showcasing the potential of
emergent communication for transfer learning and generalization in general.
| [
{
"version": "v1",
"created": "Mon, 6 Jan 2020 20:48:42 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Sep 2020 18:49:57 GMT"
},
{
"version": "v3",
"created": "Mon, 14 Dec 2020 23:40:39 GMT"
}
] | 1,608,076,800,000 | [
[
"Unger",
"Thomas A.",
""
],
[
"Bruni",
"Elia",
""
]
] |
2001.01781 | Kumar Sankar Ray | Sandip Paul, Kumar Sankar Ray and Diganta Saha | Modeling Uncertainty and Imprecision in Nonmonotonic Reasoning using
Fuzzy Numbers | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To deal with uncertainty in reasoning, interval-valued logic has been
developed. But uniform intervals cannot capture the difference in degrees of
belief for different values in the interval. To salvage the problem triangular
and trapezoidal fuzzy numbers are used as the set of truth values along with
traditional intervals. Preorder-based truth and knowledge ordering are defined
over the set of fuzzy numbers defined over $[0,1]$. Based on this enhanced set
of epistemic states, an answer set framework is developed, with properly
defined logical connectives. This type of framework is efficient in knowledge
representation and reasoning with vague and uncertain information under
nonmonotonic environment where rules may posses exceptions.
| [
{
"version": "v1",
"created": "Fri, 3 Jan 2020 07:58:33 GMT"
}
] | 1,578,441,600,000 | [
[
"Paul",
"Sandip",
""
],
[
"Ray",
"Kumar Sankar",
""
],
[
"Saha",
"Diganta",
""
]
] |
2001.02021 | Tanya Braun | Tanya Braun, Ralf M\"oller | Exploring Unknown Universes in Probabilistic Relational Models | Also accepted at the 9th StarAI Workshop at AAAI-20 | Proceedings of AI 2019: Advances in Artificial Intelligence, 2019,
91-103 | 10.1007/978-3-030-35288-2_8 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large probabilistic models are often shaped by a pool of known individuals (a
universe) and relations between them. Lifted inference algorithms handle sets
of known individuals for tractable inference. Universes may not always be
known, though, or may only described by assumptions such as "small universes
are more likely". Without a universe, inference is no longer possible for
lifted algorithms, losing their advantage of tractable inference. The aim of
this paper is to define a semantics for models with unknown universes decoupled
from a specific constraint language to enable lifted and thereby, tractable
inference.
| [
{
"version": "v1",
"created": "Tue, 7 Jan 2020 13:26:55 GMT"
}
] | 1,578,441,600,000 | [
[
"Braun",
"Tanya",
""
],
[
"Möller",
"Ralf",
""
]
] |
2001.02094 | Emir Zunic Dr. | Emir Zunic, Dzenana Donko, Emir Buza | An adaptive data-driven approach to solve real-world vehicle routing
problems in logistics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transportation occupies one-third of the amount in the logistics costs, and
accordingly transportation systems largely influence the performance of the
logistics system. This work presents an adaptive data-driven innovative modular
approach for solving the real-world Vehicle Routing Problems (VRP) in the field
of logistics. The work consists of two basic units: (i) an innovative
multi-step algorithm for successful and entirely feasible solving of the VRP
problems in logistics, (ii) an adaptive approach for adjusting and setting up
parameters and constants of the proposed algorithm. The proposed algorithm
combines several data transformation approaches, heuristics and Tabu search.
Moreover, as the performance of the algorithm depends on the set of control
parameters and constants, a predictive model that adaptively adjusts these
parameters and constants according to historical data is proposed. A comparison
of the acquired results has been made using the Decision Support System with
predictive models: Generalized Linear Models (GLM) and Support Vector Machine
(SVM). The algorithm, along with the control parameters, which using the
prediction method were acquired, was incorporated into a web-based enterprise
system, which is in use in several big distribution companies in Bosnia and
Herzegovina. The results of the proposed algorithm were compared with a set of
benchmark instances and validated over real benchmark instances as well. The
successful feasibility of the given routes, in a real environment, is also
presented.
| [
{
"version": "v1",
"created": "Sun, 5 Jan 2020 17:47:41 GMT"
}
] | 1,578,441,600,000 | [
[
"Zunic",
"Emir",
""
],
[
"Donko",
"Dzenana",
""
],
[
"Buza",
"Emir",
""
]
] |
2001.02095 | Konstantinos Xylogiannopoulos | Konstantinos F. Xylogiannopoulos | Data Curves Clustering Using Common Patterns Detection | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For the past decades we have experienced an enormous expansion of the
accumulated data that humanity produces. Daily a numerous number of smart
devices, usually interconnected over internet, produce vast, real-values
datasets. Time series representing datasets from completely irrelevant domains
such as finance, weather, medical applications, traffic control etc. become
more and more crucial in human day life. Analyzing and clustering these time
series, or in general any kind of curves, could be critical for several human
activities. In the current paper, the new Curves Clustering Using Common
Patterns (3CP) methodology is introduced, which applies a repeated pattern
detection algorithm in order to cluster sequences according to their shape and
the similarities of common patterns between time series, data curves and
eventually any kind of discrete sequences. For this purpose, the Longest
Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has
been used in combination with the All Repeated Patterns Detection (ARPaD)
algorithm in order to perform highly accurate and efficient detection of
similarities among data curves that can be used for clustering purposes and
which also provides additional flexibility and features.
| [
{
"version": "v1",
"created": "Sun, 5 Jan 2020 18:36:38 GMT"
}
] | 1,578,441,600,000 | [
[
"Xylogiannopoulos",
"Konstantinos F.",
""
]
] |
2001.02122 | Christoph Gebhardt | Christoph Gebhardt, Antti Oulasvirta, Otmar Hilliges | Hierarchical Reinforcement Learning as a Model of Human Task
Interleaving | 8 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How do people decide how long to continue in a task, when to switch, and to
which other task? Understanding the mechanisms that underpin task interleaving
is a long-standing goal in the cognitive sciences. Prior work suggests greedy
heuristics and a policy maximizing the marginal rate of return. However, it is
unclear how such a strategy would allow for adaptation to everyday environments
that offer multiple tasks with complex switch costs and delayed rewards. Here
we develop a hierarchical model of supervisory control driven by reinforcement
learning (RL). The supervisory level learns to switch using task-specific
approximate utility estimates, which are computed on the lower level. A
hierarchically optimal value function decomposition can be learned from
experience, even in conditions with multiple tasks and arbitrary and uncertain
reward and cost structures. The model reproduces known empirical effects of
task interleaving. It yields better predictions of individual-level data than a
myopic baseline in a six-task problem (N=211). The results support hierarchical
RL as a plausible model of task interleaving.
| [
{
"version": "v1",
"created": "Sat, 4 Jan 2020 17:53:28 GMT"
}
] | 1,578,441,600,000 | [
[
"Gebhardt",
"Christoph",
""
],
[
"Oulasvirta",
"Antti",
""
],
[
"Hilliges",
"Otmar",
""
]
] |
2001.02619 | Tathagata Chakraborti | Tathagata Chakraborti and Yasaman Khazaeni | D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic
Planning | Appears in the Proceedings of the AAAI 2020 Workshop on Intelligent
Process Automation | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper builds upon recent work in the declarative design of dialogue
agents and proposes an exciting new tool -- D3BA -- Declarative Design for
Digital Business Automation, built to optimize business processes using the
power of AI planning. The tool provides a powerful framework to build,
optimize, and maintain complex business processes and optimize them by
composing with services that automate one or more subtasks. We illustrate
salient features of this composition technique, compare with other philosophies
of composition, and highlight exciting opportunities for research in this
emerging field of business process automation.
| [
{
"version": "v1",
"created": "Wed, 8 Jan 2020 16:58:14 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Feb 2020 22:13:37 GMT"
}
] | 1,580,947,200,000 | [
[
"Chakraborti",
"Tathagata",
""
],
[
"Khazaeni",
"Yasaman",
""
]
] |
2001.03210 | Porter Jenkins | Porter Jenkins, Hua Wei, J. Stockton Jenkins, Zhenhui Li | A Probabilistic Simulator of Spatial Demand for Product Allocation | 8 pages, The AAAI-20 Workshop on Intelligent Process Automation | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Connecting consumers with relevant products is a very important problem in
both online and offline commerce. In physical retail, product placement is an
effective way to connect consumers with products. However, selecting product
locations within a store can be a tedious process. Moreover, learning important
spatial patterns in offline retail is challenging due to the scarcity of data
and the high cost of exploration and experimentation in the physical world. To
address these challenges, we propose a stochastic model of spatial demand in
physical retail. We show that the proposed model is more predictive of demand
than existing baselines. We also perform a preliminary study into different
automation techniques and show that an optimal product allocation policy can be
learned through Deep Q-Learning.
| [
{
"version": "v1",
"created": "Thu, 9 Jan 2020 20:18:37 GMT"
}
] | 1,578,873,600,000 | [
[
"Jenkins",
"Porter",
""
],
[
"Wei",
"Hua",
""
],
[
"Jenkins",
"J. Stockton",
""
],
[
"Li",
"Zhenhui",
""
]
] |
2001.03543 | Yara Rizk | Yara Rizk, Abhishek Bhandwalder, Scott Boag, Tathagata Chakraborti,
Vatche Isahagian, Yasaman Khazaeni, Falk Pollock, Merve Unuvar | A Unified Conversational Assistant Framework for Business Process
Automation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Business process automation is a booming multi-billion-dollar industry that
promises to remove menial tasks from workers' plates -- through the
introduction of autonomous agents -- and free up their time and brain power for
more creative and engaging tasks. However, an essential component to the
successful deployment of such autonomous agents is the ability of business
users to monitor their performance and customize their execution. A simple and
user-friendly interface with a low learning curve is necessary to increase the
adoption of such agents in banking, insurance, retail and other domains. As a
result, proactive chatbots will play a crucial role in the business automation
space. Not only can they respond to users' queries and perform actions on their
behalf but also initiate communication with the users to inform them of the
system's behavior. This will provide business users a natural language
interface to interact with, monitor and control autonomous agents. In this
work, we present a multi-agent orchestration framework to develop such
proactive chatbots by discussing the types of skills that can be composed into
agents and how to orchestrate these agents. Two use cases on a travel
preapproval business process and a loan application business process are
adopted to qualitatively analyze the proposed framework based on four criteria:
performance, coding overhead, scalability, and agent overlap.
| [
{
"version": "v1",
"created": "Tue, 7 Jan 2020 22:30:05 GMT"
}
] | 1,578,873,600,000 | [
[
"Rizk",
"Yara",
""
],
[
"Bhandwalder",
"Abhishek",
""
],
[
"Boag",
"Scott",
""
],
[
"Chakraborti",
"Tathagata",
""
],
[
"Isahagian",
"Vatche",
""
],
[
"Khazaeni",
"Yasaman",
""
],
[
"Pollock",
"Falk",
""
],
[
"Unuvar",
"Merve",
""
]
] |
2001.03809 | Maxime Bouton | Maxime Bouton, Jana Tumova, and Mykel J. Kochenderfer | Point-Based Methods for Model Checking in Partially Observable Markov
Decision Processes | 8 pages, 3 figures, AAAI 2020 | AAAI 2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous systems are often required to operate in partially observable
environments. They must reliably execute a specified objective even with
incomplete information about the state of the environment. We propose a
methodology to synthesize policies that satisfy a linear temporal logic formula
in a partially observable Markov decision process (POMDP). By formulating a
planning problem, we show how to use point-based value iteration methods to
efficiently approximate the maximum probability of satisfying a desired logical
formula and compute the associated belief state policy. We demonstrate that our
method scales to large POMDP domains and provides strong bounds on the
performance of the resulting policy.
| [
{
"version": "v1",
"created": "Sat, 11 Jan 2020 23:09:25 GMT"
}
] | 1,578,960,000,000 | [
[
"Bouton",
"Maxime",
""
],
[
"Tumova",
"Jana",
""
],
[
"Kochenderfer",
"Mykel J.",
""
]
] |
2001.04186 | Kristina Yordanova | Debajyoti Paul Chowdhury and Arghya Biswas and Tomasz Sosnowski and
Kristina Yordanova | Towards Evaluating Plan Generation Approaches with Instructional Texts | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research in behaviour understanding through language grounding has
shown it is possible to automatically generate behaviour models from textual
instructions. These models usually have goal-oriented structure and are
modelled with different formalisms from the planning domain such as the
Planning Domain Definition Language. One major problem that still remains is
that there are no benchmark datasets for comparing the different model
generation approaches, as each approach is usually evaluated on domain-specific
application. To allow the objective comparison of different methods for model
generation from textual instructions, in this report we introduce a dataset
consisting of 83 textual instructions in English language, their refinement in
a more structured form as well as manually developed plans for each of the
instructions. The dataset is publicly available to the community.
| [
{
"version": "v1",
"created": "Mon, 13 Jan 2020 12:35:16 GMT"
}
] | 1,578,960,000,000 | [
[
"Chowdhury",
"Debajyoti Paul",
""
],
[
"Biswas",
"Arghya",
""
],
[
"Sosnowski",
"Tomasz",
""
],
[
"Yordanova",
"Kristina",
""
]
] |
2001.04233 | Mikael Zayenz Lagerkvist | Mikael Zayenz Lagerkvist | State Representation and Polyomino Placement for the Game Patchwork | In ModRef 2019, The 18th workshop on Constraint Modelling and
Reformulation | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Modern board games are a rich source of entertainment for many people, but
also contain interesting and challenging structures for game playing research
and implementing game playing agents. This paper studies the game Patchwork, a
two player strategy game using polyomino tile drafting and placement. The core
polyomino placement mechanic is implemented in a constraint model using regular
constraints, extending and improving the model in (Lagerkvist, Pesant, 2008)
with: explicit rotation handling; optional placements; and new constraints for
resource usage. Crucial for implementing good game playing agents is to have
great heuristics for guiding the search when faced with large branching
factors. This paper divides placing tiles into two parts: a policy used for
placing parts and an evaluation used to select among different placements.
Policies are designed based on classical packing literature as well as common
standard constraint programming heuristics. For evaluation, global propagation
guided regret is introduced, choosing placements based on not ruling out later
placements. Extensive evaluations are performed, showing the importance of
using a good evaluation and that the proposed global propagation guided regret
is a very effective guide.
| [
{
"version": "v1",
"created": "Mon, 13 Jan 2020 13:29:38 GMT"
}
] | 1,578,960,000,000 | [
[
"Lagerkvist",
"Mikael Zayenz",
""
]
] |
2001.04238 | Mikael Zayenz Lagerkvist | Mikael Zayenz Lagerkvist | Nmbr9 as a Constraint Programming Challenge | Abstract at the 25th International Conference on Principles and
Practice of Constraint Programming | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Modern board games are a rich source of interesting and new challenges for
combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using
polyominoes. The rules of the game are simple to explain, but modelling the
game effectively using constraint programming is hard. This abstract presents
the game, contributes new generalized variants of the game suitable for
benchmarking and testing, and describes a model for the presented variants. The
question of the top possible score in the standard game is an open challenge.
| [
{
"version": "v1",
"created": "Mon, 13 Jan 2020 13:40:49 GMT"
}
] | 1,578,960,000,000 | [
[
"Lagerkvist",
"Mikael Zayenz",
""
]
] |
2001.04270 | Joel Colloc | Jo\"el Colloc (IDEES) | Perspectives and Ethics of the Autonomous Artificial Thinking Systems | The 28th International Conference on Systems Research, Informatics
and Cybernetics, Symposium on Spotlight Research in Modelling & Simulation of
Physical & Biological Systems Depending on Space, Time, Retardation,
Anticipation, Aug 2016, Baden-Baden, Germany | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The feasibility of autonomous artificial thinking systems needs to compare
the way the human beings acquire their information and develops the thought
with the current capacities of the autonomous information systems. Our model
uses four hierarchies: the hierarchy of information systems, the cognitive
hierarchy, the linguistic hierarchy and the digital informative hierarchy that
combines artificial intelligence, the power of computers models, methods and
tools to develop autonomous information systems. The question of the capability
of autonomous system to provide a form of artificial thought arises with the
ethical consequences on the social life and the perspective of transhumanism.
| [
{
"version": "v1",
"created": "Mon, 13 Jan 2020 14:23:21 GMT"
}
] | 1,578,960,000,000 | [
[
"Colloc",
"Joël",
"",
"IDEES"
]
] |
2001.04418 | Michiel Van Der Meer | Michiel van der Meer, Matteo Pirotta, Elia Bruni | Exploiting Language Instructions for Interpretable and Compositional
Reinforcement Learning | 10 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present an alternative approach to making an agent
compositional through the use of a diagnostic classifier. Because of the need
for explainable agents in automated decision processes, we attempt to interpret
the latent space from an RL agent to identify its current objective in a
complex language instruction. Results show that the classification process
causes changes in the hidden states which makes them more easily interpretable,
but also causes a shift in zero-shot performance to novel instructions. Lastly,
we limit the supervisory signal on the classification, and observe a similar
but less notable effect.
| [
{
"version": "v1",
"created": "Mon, 13 Jan 2020 17:35:56 GMT"
}
] | 1,578,960,000,000 | [
[
"van der Meer",
"Michiel",
""
],
[
"Pirotta",
"Matteo",
""
],
[
"Bruni",
"Elia",
""
]
] |
2001.04432 | Michael Skinner | Michael A. Skinner, Lakshmi Raman, Neel Shah, Abdelaziz Farhat,
Sriraam Natarajan | A Preliminary Approach for Learning Relational Policies for the
Management of Critically Ill Children | 6 pages, 1 figure, presented at the 2020 AAAI StarAI workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The increased use of electronic health records has made possible the
automated extraction of medical policies from patient records to aid in the
development of clinical decision support systems. We adapted a boosted
Statistical Relational Learning (SRL) framework to learn probabilistic rules
from clinical hospital records for the management of physiologic parameters of
children with severe cardiac or respiratory failure who were managed with
extracorporeal membrane oxygenation. In this preliminary study, the results
were promising. In particular, the algorithm returned logic rules for medical
actions that are consistent with medical reasoning.
| [
{
"version": "v1",
"created": "Mon, 13 Jan 2020 18:02:34 GMT"
}
] | 1,578,960,000,000 | [
[
"Skinner",
"Michael A.",
""
],
[
"Raman",
"Lakshmi",
""
],
[
"Shah",
"Neel",
""
],
[
"Farhat",
"Abdelaziz",
""
],
[
"Natarajan",
"Sriraam",
""
]
] |
2001.04566 | Pedro Zuidberg Dos Martires | Pedro Zuidberg Dos Martires, Samuel Kolb | Monte Carlo Anti-Differentiation for Approximate Weighted Model
Integration | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic inference in the hybrid domain, i.e. inference over
discrete-continuous domains, requires tackling two well known #P-hard problems
1)~weighted model counting (WMC) over discrete variables and 2)~integration
over continuous variables. For both of these problems inference techniques have
been developed separately in order to manage their #P-hardness, such as
knowledge compilation for WMC and Monte Carlo (MC) methods for (approximate)
integration in the continuous domain. Weighted model integration (WMI), the
extension of WMC to the hybrid domain, has been proposed as a formalism to
study probabilistic inference over discrete and continuous variables alike.
Recently developed WMI solvers have focused on exploiting structure in WMI
problems, for which they rely on symbolic integration to find the primitive of
an integrand, i.e. to perform anti-differentiation. To combine these advances
with state-of-the-art Monte Carlo integration techniques, we introduce
\textit{Monte Carlo anti-differentiation} (MCAD), which computes MC
approximations of anti-derivatives. In our empirical evaluation we substitute
the exact symbolic integration backend in an existing WMI solver with an MCAD
backend. Our experiments show that that equipping existing WMI solvers with
MCAD yields a fast yet reliable approximate inference scheme.
| [
{
"version": "v1",
"created": "Mon, 13 Jan 2020 23:45:10 GMT"
}
] | 1,579,046,400,000 | [
[
"Martires",
"Pedro Zuidberg Dos",
""
],
[
"Kolb",
"Samuel",
""
]
] |
2001.04861 | Xueru Zhang | Xueru Zhang, Mingyan Liu | Fairness in Learning-Based Sequential Decision Algorithms: A Survey | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Algorithmic fairness in decision-making has been studied extensively in
static settings where one-shot decisions are made on tasks such as
classification. However, in practice most decision-making processes are of a
sequential nature, where decisions made in the past may have an impact on
future data. This is particularly the case when decisions affect the
individuals or users generating the data used for future decisions. In this
survey, we review existing literature on the fairness of data-driven sequential
decision-making. We will focus on two types of sequential decisions: (1) past
decisions have no impact on the underlying user population and thus no impact
on future data; (2) past decisions have an impact on the underlying user
population and therefore the future data, which can then impact future
decisions. In each case the impact of various fairness interventions on the
underlying population is examined.
| [
{
"version": "v1",
"created": "Tue, 14 Jan 2020 15:49:57 GMT"
}
] | 1,579,046,400,000 | [
[
"Zhang",
"Xueru",
""
],
[
"Liu",
"Mingyan",
""
]
] |
2001.05087 | Tristan Cazenave | Tristan Cazenave | Monte Carlo Game Solver | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a general algorithm to order moves so as to speedup exact game
solvers. It uses online learning of playout policies and Monte Carlo Tree
Search. The learned policy and the information in the Monte Carlo tree are used
to order moves in game solvers. They improve greatly the solving time for
multiple games.
| [
{
"version": "v1",
"created": "Wed, 15 Jan 2020 00:20:13 GMT"
}
] | 1,579,132,800,000 | [
[
"Cazenave",
"Tristan",
""
]
] |
2001.05214 | Dell Zhang | Dell Zhang, Andre Freitas, Dacheng Tao, Dawn Song | Proceedings of the AAAI-20 Workshop on Intelligent Process Automation
(IPA-20) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the AAAI-20 Workshop on Intelligent Process
Automation (IPA-20) which took place in New York, NY, USA on February 7th 2020.
| [
{
"version": "v1",
"created": "Wed, 15 Jan 2020 10:22:12 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Feb 2020 16:00:26 GMT"
},
{
"version": "v3",
"created": "Wed, 26 Feb 2020 14:00:46 GMT"
},
{
"version": "v4",
"created": "Mon, 19 Apr 2021 16:31:34 GMT"
}
] | 1,618,876,800,000 | [
[
"Zhang",
"Dell",
""
],
[
"Freitas",
"Andre",
""
],
[
"Tao",
"Dacheng",
""
],
[
"Song",
"Dawn",
""
]
] |
2001.05288 | Joseph Tassone | Joseph Tassone, Salimur Choudhury | A Comprehensive Survey on the Ambulance Routing and Location Problems | 30 pages,7 figures,16 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this research, an extensive literature review was performed on the recent
developments of the ambulance routing problem (ARP) and ambulance location
problem (ALP). Both are respective modifications of the vehicle routing problem
(VRP) and maximum covering problem (MCP), with modifications to objective
functions and constraints. Although alike, a key distinction is emergency
service systems (EMS) are considered critical and the optimization of these has
become all the more important as a result. Similar to their parent problems,
these are NP-hard and must resort to approximations if the space size is too
large. Much of the current work has simply been on modifying existing systems
through simulation to achieve a more acceptable result. There has been attempts
towards using meta-heuristics, though practical experimentation is lacking when
compared to VRP or MCP. The contributions of this work are a comprehensive
survey of current methodologies, summarized models, and suggested future
improvements.
| [
{
"version": "v1",
"created": "Fri, 10 Jan 2020 05:33:11 GMT"
}
] | 1,579,132,800,000 | [
[
"Tassone",
"Joseph",
""
],
[
"Choudhury",
"Salimur",
""
]
] |
2001.05291 | Joseph Tassone | Joseph Tassone, Geoffrey Pond, Salimur Choudhury | Algorithms for Optimizing Fleet Staging of Air Ambulances | 15 pages,6 figures,2 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a disaster situation, air ambulance rapid response will often be the
determining factor in patient survival. Obstacles intensify this circumstance,
with geographical remoteness and limitations in vehicle placement making it an
arduous task. Considering these elements, the arrangement of responders is a
critical decision of the utmost importance. Utilizing real mission data, this
research structured an optimal coverage problem with integer linear
programming. For accurate comparison, the Gurobi optimizer was programmed with
the developed model and timed for performance. A solution implementing base
ranking followed by both local and Tabu search-based algorithms was created.
The local search algorithm proved insufficient for maximizing coverage, while
the Tabu search achieved near-optimal results. In the latter case, the total
vehicle travel distance was minimized and the runtime significantly
outperformed the one generated by Gurobi. Furthermore, variations utilizing
parallel CUDA processing further decreased the algorithmic runtime. These
proved superior as the number of test missions increased, while also
maintaining the same minimized distance.
| [
{
"version": "v1",
"created": "Fri, 10 Jan 2020 04:32:28 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Feb 2020 19:54:05 GMT"
}
] | 1,582,761,600,000 | [
[
"Tassone",
"Joseph",
""
],
[
"Pond",
"Geoffrey",
""
],
[
"Choudhury",
"Salimur",
""
]
] |
2001.05390 | Piero Bonatti | P.A. Bonatti, L. Ioffredo, I. Petrova, L. Sauro, I. R. Siahaan | Real Time Reasoning in OWL2 for GDPR Compliance | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper shows how knowledge representation and reasoning techniques can be
used to support organizations in complying with the GDPR, that is, the new
European data protection regulation. This work is carried out in a European
H2020 project called SPECIAL. Data usage policies, the consent of data
subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2
called PL (policy language); compliance checking and policy validation are
reduced to subsumption checking and concept consistency checking. This work
proposes a satisfactory tradeoff between the expressiveness requirements on PL
posed by the GDPR, and the scalability requirements that arise from the use
cases provided by SPECIAL's industrial partners. Real-time compliance checking
is achieved by means of a specialized reasoner, called PLR, that leverages
knowledge compilation and structural subsumption techniques. The performance of
a prototype implementation of PLR is analyzed through systematic experiments,
and compared with the performance of other important reasoners. Moreover, we
show how PL and PLR can be extended to support richer ontologies, by means of
import-by-query techniques. PL and its integration with OWL2's profiles
constitute new tractable fragments of OWL2. We prove also some negative
results, concerning the intractability of unrestricted reasoning in PL, and the
limitations posed on ontology import.
| [
{
"version": "v1",
"created": "Wed, 15 Jan 2020 15:50:27 GMT"
}
] | 1,579,132,800,000 | [
[
"Bonatti",
"P. A.",
""
],
[
"Ioffredo",
"L.",
""
],
[
"Petrova",
"I.",
""
],
[
"Sauro",
"L.",
""
],
[
"Siahaan",
"I. R.",
""
]
] |
2001.05490 | Miriam Enzi | Miriam Enzi, Sophie N. Parragh, David Pisinger and Matthias
Prandtstetter | Modeling and solving the multimodal car- and ride-sharing problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the multimodal car- and ride-sharing problem (MMCRP), in which a
pool of cars is used to cover a set of ride requests while uncovered requests
are assigned to other modes of transport (MOT). A car's route consists of one
or more trips. Each trip must have a specific but non-predetermined driver,
start in a depot and finish in a (possibly different) depot. Ride-sharing
between users is allowed, even when two rides do not have the same origin
and/or destination. A user has always the option of using other modes of
transport according to an individual list of preferences.
The problem can be formulated as a vehicle scheduling problem. In order to
solve the problem, an auxiliary graph is constructed in which each trip
starting and ending in a depot, and covering possible ride-shares, is modeled
as an arc in a time-space graph. We propose a two-layer decomposition algorithm
based on column generation, where the master problem ensures that each request
can only be covered at most once, and the pricing problem generates new
promising routes by solving a kind of shortest-path problem in a time-space
network. Computational experiments based on realistic instances are reported.
The benchmark instances are based on demographic, spatial, and economic data of
Vienna, Austria. We solve large instances with the column generation based
approach to near optimality in reasonable time, and we further investigate
various exact and heuristic pricing schemes.
| [
{
"version": "v1",
"created": "Wed, 15 Jan 2020 09:43:55 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Sep 2022 12:58:19 GMT"
}
] | 1,664,409,600,000 | [
[
"Enzi",
"Miriam",
""
],
[
"Parragh",
"Sophie N.",
""
],
[
"Pisinger",
"David",
""
],
[
"Prandtstetter",
"Matthias",
""
]
] |
2001.05730 | Ryuta Arisaka | Ryuta Arisaka and Takayuki Ito | Broadening Label-based Argumentation Semantics with May-Must Scales
(May-Must Argumentation) | Changes made to the previous version. 1. Definitions of satisfaction
of may/must conditions have been simplified. 2. Corrected the definition of a
maximally designating labelling which is now called a maximally proper
labelling instead | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The semantics as to which set of arguments in a given argumentation graph may
be acceptable (acceptability semantics) can be characterised in a few different
ways. Among them, labelling-based approach allows for concise and flexible
determination of acceptability statuses of arguments through assignment of a
label indicating acceptance, rejection, or undecided to each argument. In this
work, we contemplate a way of broadening it by accommodating may- and must-
conditions for an argument to be accepted or rejected, as determined by the
number(s) of rejected and accepted attacking arguments. We show that the
broadened label-based semantics can be used to express more mild indeterminacy
than inconsistency for acceptability judgement when, for example, it may be the
case that an argument is accepted and when it may also be the case that it is
rejected. We identify that finding which conditions a labelling satisfies for
every argument can be an undecidable problem, which has an unfavourable
implication to existence of a semantics. We propose to address this problem by
enforcing a labelling to maximally respect the conditions, while keeping the
rest that would necessarily cause non-termination labelled undecided. Several
semantics will be presented and the relation among them will be noted. Towards
the end, we will touch upon possible research directions that can be pursued
further.
| [
{
"version": "v1",
"created": "Thu, 16 Jan 2020 10:24:13 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Feb 2020 05:51:29 GMT"
},
{
"version": "v3",
"created": "Mon, 13 Jul 2020 03:26:44 GMT"
}
] | 1,594,684,800,000 | [
[
"Arisaka",
"Ryuta",
""
],
[
"Ito",
"Takayuki",
""
]
] |
2001.06190 | Juan-Manuel Torres-Moreno | Ana Lilia Laureano-Cruces, Laura Hern\'andez-Dom\'inguez, Martha
Mora-Torres, Juan-Manuel Torres-Moreno, Jaime Enrique Cabrera-L\'opez | Visual Simplified Characters' Emotion Emulator Implementing OCC Model | 7 pages, 14 figures, 2 tables | CGST Conference on Computer Science and Engineering, Istanbul,
Turkey, 19-21 December 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a visual emulator of the emotions seen in
characters in stories. This system is based on a simplified view of the
cognitive structure of emotions proposed by Ortony, Clore and Collins (OCC
Model). The goal of this paper is to provide a visual platform that allows us
to observe changes in the characters' different emotions, and the intricate
interrelationships between: 1) each character's emotions, 2) their affective
relationships and actions, 3) The events that take place in the development of
a plot, and 4) the objects of desire that make up the emotional map of any
story. This tool was tested on stories with a contrasting variety of emotional
and affective environments: Othello, Twilight, and Harry Potter, behaving
sensibly and in keeping with the atmosphere in which the characters were
immersed.
| [
{
"version": "v1",
"created": "Fri, 17 Jan 2020 08:41:46 GMT"
}
] | 1,579,478,400,000 | [
[
"Laureano-Cruces",
"Ana Lilia",
""
],
[
"Hernández-Domínguez",
"Laura",
""
],
[
"Mora-Torres",
"Martha",
""
],
[
"Torres-Moreno",
"Juan-Manuel",
""
],
[
"Cabrera-López",
"Jaime Enrique",
""
]
] |
2001.06322 | Piero Bonatti | P. A. Bonatti, L. Ioffredo, I. M. Petrova, L. Sauro | Fast Compliance Checking with General Vocabularies | arXiv admin note: substantial text overlap with arXiv:2001.05390 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We address the problem of complying with the GDPR while processing and
transferring personal data on the web. For this purpose we introduce an
extensible profile of OWL2 for representing data protection policies. With this
language, a company's data usage policy can be checked for compliance with data
subjects' consent and with a formalized fragment of the GDPR by means of
subsumption queries. The outer structure of the policies is restricted in order
to make compliance checking highly scalable, as required when processing
high-frequency data streams or large data volumes. However, the vocabularies
for specifying policy properties can be chosen rather freely from expressive
Horn fragments of OWL2. We exploit IBQ reasoning to integrate specialized
reasoners for the policy language and the vocabulary's language. Our
experiments show that this approach significantly improves performance.
| [
{
"version": "v1",
"created": "Thu, 16 Jan 2020 09:08:00 GMT"
}
] | 1,579,478,400,000 | [
[
"Bonatti",
"P. A.",
""
],
[
"Ioffredo",
"L.",
""
],
[
"Petrova",
"I. M.",
""
],
[
"Sauro",
"L.",
""
]
] |
2001.06781 | Bhaskar Ramasubramanian | Baicen Xiao, Qifan Lu, Bhaskar Ramasubramanian, Andrew Clark, Linda
Bushnell, Radha Poovendran | FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using
Human Feedback | Accepted as Full Paper to International Conference on Autonomous
Agents and Multi-Agent Systems (AAMAS) 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning has been successful in training autonomous agents to
accomplish goals in complex environments. Although this has been adapted to
multiple settings, including robotics and computer games, human players often
find it easier to obtain higher rewards in some environments than reinforcement
learning algorithms. This is especially true of high-dimensional state spaces
where the reward obtained by the agent is sparse or extremely delayed. In this
paper, we seek to effectively integrate feedback signals supplied by a human
operator with deep reinforcement learning algorithms in high-dimensional state
spaces. We call this FRESH (Feedback-based REward SHaping). During training, a
human operator is presented with trajectories from a replay buffer and then
provides feedback on states and actions in the trajectory. In order to
generalize feedback signals provided by the human operator to previously unseen
states and actions at test-time, we use a feedback neural network. We use an
ensemble of neural networks with a shared network architecture to represent
model uncertainty and the confidence of the neural network in its output. The
output of the feedback neural network is converted to a shaping reward that is
augmented to the reward provided by the environment. We evaluate our approach
on the Bowling and Skiing Atari games in the arcade learning environment.
Although human experts have been able to achieve high scores in these
environments, state-of-the-art deep learning algorithms perform poorly. We
observe that FRESH is able to achieve much higher scores than state-of-the-art
deep learning algorithms in both environments. FRESH also achieves a 21.4%
higher score than a human expert in Bowling and does as well as a human expert
in Skiing.
| [
{
"version": "v1",
"created": "Sun, 19 Jan 2020 06:07:20 GMT"
}
] | 1,579,651,200,000 | [
[
"Xiao",
"Baicen",
""
],
[
"Lu",
"Qifan",
""
],
[
"Ramasubramanian",
"Bhaskar",
""
],
[
"Clark",
"Andrew",
""
],
[
"Bushnell",
"Linda",
""
],
[
"Poovendran",
"Radha",
""
]
] |
2001.06917 | Jiaoyan Chen | Jiaoyan Chen, Xi Chen, Ian Horrocks, Ernesto Jimenez-Ruiz, and Erik B.
Myklebus | Correcting Knowledge Base Assertions | Accepted by The Web Conference (WWW) 2020 | null | 10.1145/3366423.3380226 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The usefulness and usability of knowledge bases (KBs) is often limited by
quality issues. One common issue is the presence of erroneous assertions, often
caused by lexical or semantic confusion. We study the problem of correcting
such assertions, and present a general correction framework which combines
lexical matching, semantic embedding, soft constraint mining and semantic
consistency checking. The framework is evaluated using DBpedia and an
enterprise medical KB.
| [
{
"version": "v1",
"created": "Sun, 19 Jan 2020 23:03:47 GMT"
}
] | 1,579,651,200,000 | [
[
"Chen",
"Jiaoyan",
""
],
[
"Chen",
"Xi",
""
],
[
"Horrocks",
"Ian",
""
],
[
"Jimenez-Ruiz",
"Ernesto",
""
],
[
"Myklebus",
"Erik B.",
""
]
] |
2001.06921 | arXiv Admin | Amit Kumar Mondal | A Survey of Reinforcement Learning Techniques: Strategies, Recent
Development, and Future Directions | This submission has been withdrawn by arXiv administrators as the
second author was added without their knowledge or consent | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning is one of the core components in designing an
artificial intelligent system emphasizing real-time response. Reinforcement
learning influences the system to take actions within an arbitrary environment
either having previous knowledge about the environment model or not. In this
paper, we present a comprehensive study on Reinforcement Learning focusing on
various dimensions including challenges, the recent development of different
state-of-the-art techniques, and future directions. The fundamental objective
of this paper is to provide a framework for the presentation of available
methods of reinforcement learning that is informative enough and simple to
follow for the new researchers and academics in this domain considering the
latest concerns. First, we illustrated the core techniques of reinforcement
learning in an easily understandable and comparable way. Finally, we analyzed
and depicted the recent developments in reinforcement learning approaches. My
analysis pointed out that most of the models focused on tuning policy values
rather than tuning other things in a particular state of reasoning.
| [
{
"version": "v1",
"created": "Sun, 19 Jan 2020 23:51:14 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Jan 2020 14:54:38 GMT"
}
] | 1,580,688,000,000 | [
[
"Mondal",
"Amit Kumar",
""
]
] |
2001.07362 | Abhishek Dubey | Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer, Yevgeniy
Vorobeychik, Abhishek Dubey | On Algorithmic Decision Procedures in Emergency Response Systems in
Smart and Connected Communities | Accepted at AAMAS 2020 (International Conference on Autonomous Agents
and Multiagent Systems) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Emergency Response Management (ERM) is a critical problem faced by
communities across the globe. Despite this, it is common for ERM systems to
follow myopic decision policies in the real world. Principled approaches to aid
ERM decision-making under uncertainty have been explored but have failed to be
accepted into real systems. We identify a key issue impeding their adoption ---
algorithmic approaches to emergency response focus on reactive, post-incident
dispatching actions, i.e. optimally dispatching a responder \textit{after}
incidents occur. However, the critical nature of emergency response dictates
that when an incident occurs, first responders always dispatch the closest
available responder to the incident. We argue that the crucial period of
planning for ERM systems is not post-incident, but between incidents. This is
not a trivial planning problem --- a major challenge with dynamically balancing
the spatial distribution of responders is the complexity of the problem. An
orthogonal problem in ERM systems is planning under limited communication,
which is particularly important in disaster scenarios that affect communication
networks. We address both problems by proposing two partially decentralized
multi-agent planning algorithms that utilize heuristics and exploit the
structure of the dispatch problem. We evaluate our proposed approach using
real-world data, and find that in several contexts, dynamic re-balancing the
spatial distribution of emergency responders reduces both the average response
time as well as its variance.
| [
{
"version": "v1",
"created": "Tue, 21 Jan 2020 07:04:38 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2020 01:03:37 GMT"
},
{
"version": "v3",
"created": "Thu, 12 Mar 2020 00:12:20 GMT"
}
] | 1,584,057,600,000 | [
[
"Pettet",
"Geoffrey",
""
],
[
"Mukhopadhyay",
"Ayan",
""
],
[
"Kochenderfer",
"Mykel",
""
],
[
"Vorobeychik",
"Yevgeniy",
""
],
[
"Dubey",
"Abhishek",
""
]
] |
2001.07374 | Joel Colloc | Ying Shen (UPN), Jacquet-Andrieu Armelle, Jo\"el Colloc (IDEES) | A multi-agent ontologies-based clinical decision support system | in French | AMINA'2012, Jan 2012, Mahdia, Tunisie | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clinical decision support systems combine knowledge and data from a variety
of sources, represented by quantitative models based on stochastic methods, or
qualitative based rather on expert heuristics and deductive reasoning. At the
same time, case-based reasoning (CBR) memorizes and returns the experience of
solving similar problems. The cooperation of heterogeneous clinical knowledge
bases (knowledge objects, semantic distances, evaluation functions, logical
rules, databases...) is based on medical ontologies. A multi-agent decision
support system (MADSS) enables the integration and cooperation of agents
specialized in different fields of knowledge (semiology, pharmacology, clinical
cases, etc.). Each specialist agent operates a knowledge base defining the
conduct to be maintained in conformity with the state of the art associated
with an ontological basis that expresses the semantic relationships between the
terms of the domain in question. Our approach is based on the specialization of
agents adapted to the knowledge models used during the clinical steps and
ontologies. This modular approach is suitable for the realization of MADSS in
many areas.
| [
{
"version": "v1",
"created": "Tue, 21 Jan 2020 08:04:13 GMT"
}
] | 1,579,651,200,000 | [
[
"Shen",
"Ying",
"",
"UPN"
],
[
"Armelle",
"Jacquet-Andrieu",
"",
"IDEES"
],
[
"Colloc",
"Joël",
"",
"IDEES"
]
] |
2001.07537 | Vinod Muthusamy | Steve T.K. Jan, Vatche Ishakian, Vinod Muthusamy | AI Trust in business processes: The need for process-aware explanations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Business processes underpin a large number of enterprise operations including
processing loan applications, managing invoices, and insurance claims. There is
a large opportunity for infusing AI to reduce cost or provide better customer
experience, and the business process management (BPM) literature is rich in
machine learning solutions including unsupervised learning to gain insights on
clusters of process traces, classification models to predict the outcomes,
duration, or paths of partial process traces, extracting business process from
documents, and models to recommend how to optimize a business process or
navigate decision points. More recently, deep learning models including those
from the NLP domain have been applied to process predictions.
Unfortunately, very little of these innovations have been applied and adopted
by enterprise companies. We assert that a large reason for the lack of adoption
of AI models in BPM is that business users are risk-averse and do not
implicitly trust AI models. There has, unfortunately, been little attention
paid to explaining model predictions to business users with process context. We
challenge the BPM community to build on the AI interpretability literature, and
the AI Trust community to understand
| [
{
"version": "v1",
"created": "Tue, 21 Jan 2020 13:51:36 GMT"
}
] | 1,579,651,200,000 | [
[
"Jan",
"Steve T. K.",
""
],
[
"Ishakian",
"Vatche",
""
],
[
"Muthusamy",
"Vinod",
""
]
] |
2001.07573 | Suzanne Tolmeijer | Suzanne Tolmeijer, Markus Kneer, Cristina Sarasua, Markus Christen,
Abraham Bernstein | Implementations in Machine Ethics: A Survey | published version, journal paper, ACM Computing Surveys, 38 pages, 7
tables, 4 figures | ACM Comput. Surv. 53, 6, Article 132 (December 2020), 38 pages | 10.1145/3419633 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Increasingly complex and autonomous systems require machine ethics to
maximize the benefits and minimize the risks to society arising from the new
technology. It is challenging to decide which type of ethical theory to employ
and how to implement it effectively. This survey provides a threefold
contribution. First, it introduces a trimorphic taxonomy to analyze machine
ethics implementations with respect to their object (ethical theories), as well
as their nontechnical and technical aspects. Second, an exhaustive selection
and description of relevant works is presented. Third, applying the new
taxonomy to the selected works, dominant research patterns, and lessons for the
field are identified, and future directions for research are suggested.
| [
{
"version": "v1",
"created": "Tue, 21 Jan 2020 14:32:23 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jan 2021 16:27:08 GMT"
}
] | 1,611,532,800,000 | [
[
"Tolmeijer",
"Suzanne",
""
],
[
"Kneer",
"Markus",
""
],
[
"Sarasua",
"Cristina",
""
],
[
"Christen",
"Markus",
""
],
[
"Bernstein",
"Abraham",
""
]
] |
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