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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1705.10898 | Jerry Lonlac | Jerry Lonlac and Engelbert Mephu Nguifo | Towards Learned Clauses Database Reduction Strategies Based on Dominance
Relationship | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clause Learning is one of the most important components of a conflict driven
clause learning (CDCL) SAT solver that is effective on industrial instances.
Since the number of learned clauses is proved to be exponential in the worse
case, it is necessary to identify the most relevant clauses to maintain and
delete the irrelevant ones. As reported in the literature, several learned
clauses deletion strategies have been proposed. However the diversity in both
the number of clauses to be removed at each step of reduction and the results
obtained with each strategy creates confusion to determine which criterion is
better. Thus, the problem to select which learned clauses are to be removed
during the search step remains very challenging. In this paper, we propose a
novel approach to identify the most relevant learned clauses without favoring
or excluding any of the proposed measures, but by adopting the notion of
dominance relationship among those measures. Our approach bypasses the problem
of the diversity of results and reaches a compromise between the assessments of
these measures. Furthermore, the proposed approach also avoids another
non-trivial problem which is the amount of clauses to be deleted at each
reduction of the learned clause database.
| [
{
"version": "v1",
"created": "Wed, 31 May 2017 00:05:26 GMT"
}
] | 1,496,275,200,000 | [
[
"Lonlac",
"Jerry",
""
],
[
"Nguifo",
"Engelbert Mephu",
""
]
] |
1705.10899 | Son Tran | Son N. Tran | Propositional Knowledge Representation and Reasoning in Restricted
Boltzmann Machines | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | While knowledge representation and reasoning are considered the keys for
human-level artificial intelligence, connectionist networks have been shown
successful in a broad range of applications due to their capacity for robust
learning and flexible inference under uncertainty. The idea of representing
symbolic knowledge in connectionist networks has been well-received and
attracted much attention from research community as this can establish a
foundation for integration of scalable learning and sound reasoning. In
previous work, there exist a number of approaches that map logical inference
rules with feed-forward propagation of artificial neural networks (ANN).
However, the discriminative structure of an ANN requires the separation of
input/output variables which makes it difficult for general reasoning where any
variables should be inferable. Other approaches address this issue by employing
generative models such as symmetric connectionist networks, however, they are
difficult and convoluted. In this paper we propose a novel method to represent
propositional formulas in restricted Boltzmann machines which is less complex,
especially in the cases of logical implications and Horn clauses. An
integration system is then developed and evaluated in real datasets which shows
promising results.
| [
{
"version": "v1",
"created": "Wed, 31 May 2017 00:24:16 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Jun 2017 00:19:24 GMT"
},
{
"version": "v3",
"created": "Tue, 29 May 2018 04:44:31 GMT"
}
] | 1,527,638,400,000 | [
[
"Tran",
"Son N.",
""
]
] |
1705.10998 | Vitaly Kurin | Vitaly Kurin, Sebastian Nowozin, Katja Hofmann, Lucas Beyer, Bastian
Leibe | The Atari Grand Challenge Dataset | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent progress in Reinforcement Learning (RL), fueled by its combination,
with Deep Learning has enabled impressive results in learning to interact with
complex virtual environments, yet real-world applications of RL are still
scarce. A key limitation is data efficiency, with current state-of-the-art
approaches requiring millions of training samples. A promising way to tackle
this problem is to augment RL with learning from human demonstrations. However,
human demonstration data is not yet readily available. This hinders progress in
this direction. The present work addresses this problem as follows. We (i)
collect and describe a large dataset of human Atari 2600 replays -- the largest
and most diverse such data set publicly released to date, (ii) illustrate an
example use of this dataset by analyzing the relation between demonstration
quality and imitation learning performance, and (iii) outline possible research
directions that are opened up by our work.
| [
{
"version": "v1",
"created": "Wed, 31 May 2017 09:08:36 GMT"
}
] | 1,496,275,200,000 | [
[
"Kurin",
"Vitaly",
""
],
[
"Nowozin",
"Sebastian",
""
],
[
"Hofmann",
"Katja",
""
],
[
"Beyer",
"Lucas",
""
],
[
"Leibe",
"Bastian",
""
]
] |
1706.00037 | Mark Lewis | Mark W. Lewis | A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic
Problems Leveraging the Graphics Processor Unit | Quality solutions quickly obtained for xQx using the GPU to perform
matrix multiplication, however improvements to solution intensification are
needed | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-start algorithms are a common and effective tool for metaheuristic
searches. In this paper we amplify multi-start capabilities by employing the
parallel processing power of the graphics processer unit (GPU) to quickly
generate a diverse starting set of solutions for the Unconstrained Binary
Quadratic Optimization Problem which are evaluated and used to implement
screening methods to select solutions for further optimization. This method is
implemented as an initial high quality solution generation phase prior to a
secondary steepest ascent search and a comparison of results to best known
approaches on benchmark unconstrained binary quadratic problems demonstrates
that GPU-enabled diversified multi-start with screening quickly yields very
good results.
| [
{
"version": "v1",
"created": "Wed, 31 May 2017 18:15:51 GMT"
}
] | 1,496,361,600,000 | [
[
"Lewis",
"Mark W.",
""
]
] |
1706.00066 | Chuyu Xiong | Chuyu Xiong | Descriptions of Objectives and Processes of Mechanical Learning | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | In [1], we introduced mechanical learning and proposed 2 approaches to
mechanical learning. Here, we follow one such approach to well describe the
objects and the processes of learning. We discuss 2 kinds of patterns:
objective and subjective pattern. Subjective pattern is crucial for learning
machine. We prove that for any objective pattern we can find a proper
subjective pattern based upon least base patterns to express the objective
pattern well. X-form is algebraic expression for subjective pattern. Collection
of X-forms form internal representation space, which is center of learning
machine. We discuss learning by teaching and without teaching. We define data
sufficiency by X-form. We then discussed some learning strategies. We show, in
each strategy, with sufficient data, and with certain capabilities, learning
machine indeed can learn any pattern (universal learning machine). In appendix,
with knowledge of learning machine, we try to view deep learning from a
different angle, i.e. its internal representation space and its learning
dynamics.
| [
{
"version": "v1",
"created": "Wed, 31 May 2017 19:42:41 GMT"
}
] | 1,496,361,600,000 | [
[
"Xiong",
"Chuyu",
""
]
] |
1706.00123 | Junping Zhou | Junping Zhou, Huanyao Sun, Feifei Ma, Jian Gao, Ke Xu, and Minghao Yin | Diversified Top-k Partial MaxSAT Solving | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a diversified top-k partial MaxSAT problem, a combination of
partial MaxSAT problem and enumeration problem. Given a partial MaxSAT formula
F and a positive integer k, the diversified top-k partial MaxSAT is to find k
maximal solutions for F such that the k maximal solutions satisfy the maximum
number of soft clauses of F. This problem can be widely used in many
applications including community detection, sensor place, motif discovery, and
combinatorial testing. We prove the problem is NP-hard and propose an approach
for solving the problem. The concrete idea of the approach is to design an
encoding EE which reduces diversified top-k partial MaxSAT problem into partial
MaxSAT problem, and then solve the resulting problem with state-of-art solvers.
In addition, we present an algorithm MEMKC exactly solving the diversified
top-k partial MaxSAT. Through several experiments we show that our approach can
be successfully applied to the interesting problem.
| [
{
"version": "v1",
"created": "Wed, 31 May 2017 23:37:18 GMT"
}
] | 1,496,361,600,000 | [
[
"Zhou",
"Junping",
""
],
[
"Sun",
"Huanyao",
""
],
[
"Ma",
"Feifei",
""
],
[
"Gao",
"Jian",
""
],
[
"Xu",
"Ke",
""
],
[
"Yin",
"Minghao",
""
]
] |
1706.00355 | Yordan Hristov | Yordan Hristov, Svetlin Penkov, Alex Lascarides and Subramanian
Ramamoorthy | Grounding Symbols in Multi-Modal Instructions | 9 pages, 8 figures, To appear in the Proceedings of the ACL workshop
Language Grounding for Robotics, Vancouver, Canada | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As robots begin to cohabit with humans in semi-structured environments, the
need arises to understand instructions involving rich variability---for
instance, learning to ground symbols in the physical world. Realistically, this
task must cope with small datasets consisting of a particular users' contextual
assignment of meaning to terms. We present a method for processing a raw stream
of cross-modal input---i.e., linguistic instructions, visual perception of a
scene and a concurrent trace of 3D eye tracking fixations---to produce the
segmentation of objects with a correspondent association to high-level
concepts. To test our framework we present experiments in a table-top object
manipulation scenario. Our results show our model learns the user's notion of
colour and shape from a small number of physical demonstrations, generalising
to identifying physical referents for novel combinations of the words.
| [
{
"version": "v1",
"created": "Thu, 1 Jun 2017 15:42:50 GMT"
}
] | 1,496,361,600,000 | [
[
"Hristov",
"Yordan",
""
],
[
"Penkov",
"Svetlin",
""
],
[
"Lascarides",
"Alex",
""
],
[
"Ramamoorthy",
"Subramanian",
""
]
] |
1706.00356 | Riccardo De Masellis | Riccardo De Masellis and Chiara Di Francescomarino and Chiara Ghidini
and Sergio Tessaris | Enhancing workflow-nets with data for trace completion | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The growing adoption of IT-systems for modeling and executing (business)
processes or services has thrust the scientific investigation towards
techniques and tools which support more complex forms of process analysis. Many
of them, such as conformance checking, process alignment, mining and
enhancement, rely on complete observation of past (tracked and logged)
executions. In many real cases, however, the lack of human or IT-support on all
the steps of process execution, as well as information hiding and abstraction
of model and data, result in incomplete log information of both data and
activities. This paper tackles the issue of automatically repairing traces with
missing information by notably considering not only activities but also data
manipulated by them. Our technique recasts such a problem in a reachability
problem and provides an encoding in an action language which allows to
virtually use any state-of-the-art planning to return solutions.
| [
{
"version": "v1",
"created": "Thu, 1 Jun 2017 15:46:47 GMT"
}
] | 1,496,361,600,000 | [
[
"De Masellis",
"Riccardo",
""
],
[
"Di Francescomarino",
"Chiara",
""
],
[
"Ghidini",
"Chiara",
""
],
[
"Tessaris",
"Sergio",
""
]
] |
1706.00536 | Christopher Grimm | Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel,
Lawson L.S. Wong, Michael L. Littman | Modeling Latent Attention Within Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks are able to solve tasks across a variety of domains and
modalities of data. Despite many empirical successes, we lack the ability to
clearly understand and interpret the learned internal mechanisms that
contribute to such effective behaviors or, more critically, failure modes. In
this work, we present a general method for visualizing an arbitrary neural
network's inner mechanisms and their power and limitations. Our dataset-centric
method produces visualizations of how a trained network attends to components
of its inputs. The computed "attention masks" support improved interpretability
by highlighting which input attributes are critical in determining output. We
demonstrate the effectiveness of our framework on a variety of deep neural
network architectures in domains from computer vision, natural language
processing, and reinforcement learning. The primary contribution of our
approach is an interpretable visualization of attention that provides unique
insights into the network's underlying decision-making process irrespective of
the data modality.
| [
{
"version": "v1",
"created": "Fri, 2 Jun 2017 02:10:39 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Dec 2017 08:08:50 GMT"
}
] | 1,514,937,600,000 | [
[
"Grimm",
"Christopher",
""
],
[
"Arumugam",
"Dilip",
""
],
[
"Karamcheti",
"Siddharth",
""
],
[
"Abel",
"David",
""
],
[
"Wong",
"Lawson L. S.",
""
],
[
"Littman",
"Michael L.",
""
]
] |
1706.00585 | Joao Leite | Martin Slota and Joao Leite | Exception-Based Knowledge Updates | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing methods for dealing with knowledge updates differ greatly depending
on the underlying knowledge representation formalism. When Classical Logic is
used, updates are typically performed by manipulating the knowledge base on the
model-theoretic level. On the opposite side of the spectrum stand the semantics
for updating Answer-Set Programs that need to rely on rule syntax. Yet, a
unifying perspective that could embrace both these branches of research is of
great importance as it enables a deeper understanding of all involved methods
and principles and creates room for their cross-fertilisation, ripening and
further development.
This paper bridges the seemingly irreconcilable approaches to updates. It
introduces a novel monotonic characterisation of rules, dubbed RE-models, and
shows it to be a more suitable semantic foundation for rule updates than
SE-models. Then it proposes a generic scheme for specifying semantic rule
update operators, based on the idea of viewing a program as the set of sets of
RE-models of its rules; updates are performed by introducing additional
interpretations - exceptions - to the sets of RE-models of rules in the
original program. The introduced scheme is used to define rule update operators
that are closely related to both classical update principles and traditional
approaches to rules updates, and serve as a basis for a solution to the
long-standing problem of state condensing, showing how they can be equivalently
defined as binary operators on some class of logic programs.
Finally, the essence of these ideas is extracted to define an abstract
framework for exception-based update operators, viewing a knowledge base as the
set of sets of models of its elements, which can capture a wide range of both
model- and formula-based classical update operators, and thus serves as the
first firm formal ground connecting classical and rule updates.
| [
{
"version": "v1",
"created": "Fri, 2 Jun 2017 08:31:10 GMT"
}
] | 1,496,620,800,000 | [
[
"Slota",
"Martin",
""
],
[
"Leite",
"Joao",
""
]
] |
1706.00637 | Prachi Jain | Prachi Jain, Shikhar Murty, Mausam, Soumen Chakrabarti | Joint Matrix-Tensor Factorization for Knowledge Base Inference | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While several matrix factorization (MF) and tensor factorization (TF) models
have been proposed for knowledge base (KB) inference, they have rarely been
compared across various datasets. Is there a single model that performs well
across datasets? If not, what characteristics of a dataset determine the
performance of MF and TF models? Is there a joint TF+MF model that performs
robustly on all datasets? We perform an extensive evaluation to compare popular
KB inference models across popular datasets in the literature. In addition to
answering the questions above, we remove a limitation in the standard
evaluation protocol for MF models, propose an extension to MF models so that
they can better handle out-of-vocabulary (OOV) entity pairs, and develop a
novel combination of TF and MF models. We also analyze and explain the results
based on models and dataset characteristics. Our best model is robust, and
obtains strong results across all datasets.
| [
{
"version": "v1",
"created": "Fri, 2 Jun 2017 11:34:37 GMT"
}
] | 1,496,620,800,000 | [
[
"Jain",
"Prachi",
""
],
[
"Murty",
"Shikhar",
""
],
[
"Mausam",
"",
""
],
[
"Chakrabarti",
"Soumen",
""
]
] |
1706.00638 | Amit Mishra | Amit Kumar Mishra | ICABiDAS: Intuition Centred Architecture for Big Data Analysis and
Synthesis | This paper is presented in the Biologically Inspired Cognitive
Architecture Conference 2017 and published by their proceedings | Procedia Computer Science Volume 123, 2018 | 10.1016/2018.01.045 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans are expert in the amount of sensory data they deal with each moment.
Human brain not only analyses these data but also starts synthesizing new
information from the existing data. The current age Big-data systems are needed
not just to analyze data but also to come up new interpretation. We believe
that the pivotal ability in human brain which enables us to do this is what is
known as "intuition". Here, we present an intuition based architecture for big
data analysis and synthesis.
| [
{
"version": "v1",
"created": "Fri, 2 Jun 2017 11:35:52 GMT"
}
] | 1,661,472,000,000 | [
[
"Mishra",
"Amit Kumar",
""
]
] |
1706.01077 | Tomoki Nishi | Tomoki Nishi and Prashant Doshi and Michael R. James and Danil
Prokhorov | Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known
Dynamics | 10 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many robotic applications, some aspects of the system dynamics can be
modeled accurately while others are difficult to obtain or model. We present a
novel reinforcement learning (RL) method for continuous state and action spaces
that learns with partial knowledge of the system and without active
exploration. It solves linearly-solvable Markov decision processes (L-MDPs),
which are well suited for continuous state and action spaces, based on an
actor-critic architecture. Compared to previous RL methods for L-MDPs and path
integral methods which are model based, the actor-critic learning does not need
a model of the uncontrolled dynamics and, importantly, transition noise levels;
however, it requires knowing the control dynamics for the problem. We evaluate
our method on two synthetic test problems, and one real-world problem in
simulation and using real traffic data. Our experiments demonstrate improved
learning and policy performance.
| [
{
"version": "v1",
"created": "Sun, 4 Jun 2017 14:02:01 GMT"
}
] | 1,496,707,200,000 | [
[
"Nishi",
"Tomoki",
""
],
[
"Doshi",
"Prashant",
""
],
[
"James",
"Michael R.",
""
],
[
"Prokhorov",
"Danil",
""
]
] |
1706.01320 | Diptangshu Pandit | Diptangshu Pandit | 3D Pathfinding and Collision Avoidance Using Uneven Search-space
Quantization and Visual Cone Search | major problems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pathfinding is a very popular area in computer game development. While
two-dimensional (2D) pathfinding is widely applied in most of the popular game
engines, little implementation of real three-dimensional (3D) pathfinding can
be found. This research presents a dynamic search space optimization algorithm
which can be applied to tessellate 3D search space unevenly, significantly
reducing the total number of resulting nodes. The algorithm can be used with
popular pathfinding algorithms in 3D game engines. Furthermore, a simplified
standalone 3D pathfinding algorithm is proposed in this paper. The proposed
algorithm relies on ray-casting or line vision to generate a feasible path
during runtime without requiring division of the search space into a 3D grid.
Both of the proposed algorithms are simulated on Unreal Engine to show
innerworkings and resultant path comparison with A*. The advantages and
shortcomings of the proposed algorithms are also discussed along with future
directions.
| [
{
"version": "v1",
"created": "Mon, 5 Jun 2017 13:49:49 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Apr 2018 23:47:51 GMT"
},
{
"version": "v3",
"created": "Tue, 19 Jun 2018 16:01:28 GMT"
}
] | 1,529,452,800,000 | [
[
"Pandit",
"Diptangshu",
""
]
] |
1706.01417 | Leonardo Anjoletto Ferreira | Leonardo A. Ferreira, Reinaldo A. C. Bianchi, Paulo E. Santos, Ramon
Lopez de Mantaras | A method for the online construction of the set of states of a Markov
Decision Process using Answer Set Programming | Submitted to IJCAI 17 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-stationary domains, that change in unpredicted ways, are a challenge for
agents searching for optimal policies in sequential decision-making problems.
This paper presents a combination of Markov Decision Processes (MDP) with
Answer Set Programming (ASP), named {\em Online ASP for MDP} (oASP(MDP)), which
is a method capable of constructing the set of domain states while the agent
interacts with a changing environment. oASP(MDP) updates previously obtained
policies, learnt by means of Reinforcement Learning (RL), using rules that
represent the domain changes observed by the agent. These rules represent a set
of domain constraints that are processed as ASP programs reducing the search
space. Results show that oASP(MDP) is capable of finding solutions for problems
in non-stationary domains without interfering with the action-value function
approximation process.
| [
{
"version": "v1",
"created": "Mon, 5 Jun 2017 16:48:23 GMT"
}
] | 1,496,707,200,000 | [
[
"Ferreira",
"Leonardo A.",
""
],
[
"Bianchi",
"Reinaldo A. C.",
""
],
[
"Santos",
"Paulo E.",
""
],
[
"de Mantaras",
"Ramon Lopez",
""
]
] |
1706.01991 | Son Tran | Son N. Tran | Unsupervised Neural-Symbolic Integration | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Symbolic has been long considered as a language of human intelligence while
neural networks have advantages of robust computation and dealing with noisy
data. The integration of neural-symbolic can offer better learning and
reasoning while providing a means for interpretability through the
representation of symbolic knowledge. Although previous works focus intensively
on supervised feedforward neural networks, little has been done for the
unsupervised counterparts. In this paper we show how to integrate symbolic
knowledge into unsupervised neural networks. We exemplify our approach with
knowledge in different forms, including propositional logic for DNA promoter
prediction and first-order logic for understanding family relationship.
| [
{
"version": "v1",
"created": "Tue, 6 Jun 2017 21:58:50 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Jun 2017 04:11:21 GMT"
}
] | 1,498,176,000,000 | [
[
"Tran",
"Son N.",
""
]
] |
1706.02048 | Yifeng Ding | Yifeng Ding | Epistemic Logic with Functional Dependency Operator | null | Studies in Logic, Vol. 9, No. 4 (2016): 55-84 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epistemic logic with non-standard knowledge operators, especially the
"knowing-value" operator, has recently gathered much attention. With the
"knowing-value" operator, we can express knowledge of individual variables, but
not of the relations between them in general. In this paper, we propose a new
operator Kf to express knowledge of the functional dependencies between
variables. The semantics of this Kf operator uses a function domain which
imposes a constraint on what counts as a functional dependency relation. By
adjusting this function domain, different interesting logics arise, and in this
paper we axiomatize three such logics in a single agent setting. Then we show
how these three logics can be unified by allowing the function domain to vary
relative to different agents and possible worlds. A multiagent axiomatization
is given in this case.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2017 05:16:54 GMT"
}
] | 1,496,880,000,000 | [
[
"Ding",
"Yifeng",
""
]
] |
1706.02462 | Marek Szyku{\l}a | Jakub Kowalski, Maksymilian Mika, Jakub Sutowicz, Marek Szyku{\l}a | Regular Boardgames | AAAI 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new General Game Playing (GGP) language called Regular
Boardgames (RBG), which is based on the theory of regular languages. The
objective of RBG is to join key properties as expressiveness, efficiency, and
naturalness of the description in one GGP formalism, compensating certain
drawbacks of the existing languages. This often makes RBG more suitable for
various research and practical developments in GGP. While dedicated mostly for
describing board games, RBG is universal for the class of all finite
deterministic turn-based games with perfect information. We establish
foundations of RBG, and analyze it theoretically and experimentally, focusing
on the efficiency of reasoning. Regular Boardgames is the first GGP language
that allows efficient encoding and playing games with complex rules and with
large branching factor (e.g.\ amazons, arimaa, large chess variants, go,
international checkers, paper soccer).
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2017 07:22:21 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Nov 2018 14:50:36 GMT"
}
] | 1,542,153,600,000 | [
[
"Kowalski",
"Jakub",
""
],
[
"Mika",
"Maksymilian",
""
],
[
"Sutowicz",
"Jakub",
""
],
[
"Szykuła",
"Marek",
""
]
] |
1706.02513 | Virginia Dignum | Virginia Dignum | Responsible Autonomy | IJCAI2017 (International Joint Conference on Artificial Intelligence) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As intelligent systems are increasingly making decisions that directly affect
society, perhaps the most important upcoming research direction in AI is to
rethink the ethical implications of their actions. Means are needed to
integrate moral, societal and legal values with technological developments in
AI, both during the design process as well as part of the deliberation
algorithms employed by these systems. In this paper, we describe leading ethics
theories and propose alternative ways to ensure ethical behavior by artificial
systems. Given that ethics are dependent on the socio-cultural context and are
often only implicit in deliberation processes, methodologies are needed to
elicit the values held by designers and stakeholders, and to make these
explicit leading to better understanding and trust on artificial autonomous
systems.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2017 11:06:52 GMT"
}
] | 1,496,966,400,000 | [
[
"Dignum",
"Virginia",
""
]
] |
1706.02686 | Mieczys{\l}aw K{\l}opotek | Andrzej Matuszewski, Mieczys{\l}aw A. K{\l}opotek | What Does a Belief Function Believe In ? | 13 pages | null | null | IPI-PAN report 758, 1994 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The conditioning in the Dempster-Shafer Theory of Evidence has been defined
(by Shafer \cite{Shafer:90} as combination of a belief function and of an
"event" via Dempster rule.
On the other hand Shafer \cite{Shafer:90} gives a "probabilistic"
interpretation of a belief function (hence indirectly its derivation from a
sample). Given the fact that conditional probability distribution of a
sample-derived probability distribution is a probability distribution derived
from a subsample (selected on the grounds of a conditioning event), the paper
investigates the empirical nature of the Dempster- rule of combination.
It is demonstrated that the so-called "conditional" belief function is not a
belief function given an event but rather a belief function given manipulation
of original empirical data.\\ Given this, an interpretation of belief function
different from that of Shafer is proposed. Algorithms for construction of
belief networks from data are derived for this interpretation.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2017 17:17:23 GMT"
}
] | 1,496,966,400,000 | [
[
"Matuszewski",
"Andrzej",
""
],
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
1706.02789 | Victor Silva | Victor do Nascimento Silva and Luiz Chaimowicz | On the Development of Intelligent Agents for MOBA Games | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multiplayer Online Battle Arena (MOBA) is one of the most played game genres
nowadays. With the increasing growth of this genre, it becomes necessary to
develop effective intelligent agents to play alongside or against human
players. In this paper we address the problem of agent development for MOBA
games. We implement a two-layered architecture agent that handles both
navigation and game mechanics. This architecture relies on the use of Influence
Maps, a widely used approach for tactical analysis. Several experiments were
performed using {\em League of Legends} as a testbed, and show promising
results in this highly dynamic real-time context.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2017 23:20:34 GMT"
}
] | 1,497,225,600,000 | [
[
"Silva",
"Victor do Nascimento",
""
],
[
"Chaimowicz",
"Luiz",
""
]
] |
1706.02792 | Liron Cohen | Liron Cohen, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven
Koenig, T.K. Satish Kumar | The FastMap Algorithm for Shortest Path Computations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new preprocessing algorithm for embedding the nodes of a given
edge-weighted undirected graph into a Euclidean space. The Euclidean distance
between any two nodes in this space approximates the length of the shortest
path between them in the given graph. Later, at runtime, a shortest path
between any two nodes can be computed with A* search using the Euclidean
distances as heuristic. Our preprocessing algorithm, called FastMap, is
inspired by the data mining algorithm of the same name and runs in near-linear
time. Hence, FastMap is orders of magnitude faster than competing approaches
that produce a Euclidean embedding using Semidefinite Programming. FastMap also
produces admissible and consistent heuristics and therefore guarantees the
generation of shortest paths. Moreover, FastMap applies to general undirected
graphs for which many traditional heuristics, such as the Manhattan Distance
heuristic, are not well defined. Empirically, we demonstrate that A* search
using the FastMap heuristic is competitive with A* search using other
state-of-the-art heuristics, such as the Differential heuristic.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2017 23:29:05 GMT"
},
{
"version": "v2",
"created": "Sat, 21 Oct 2017 19:11:06 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Dec 2017 19:57:53 GMT"
}
] | 1,514,160,000,000 | [
[
"Cohen",
"Liron",
""
],
[
"Uras",
"Tansel",
""
],
[
"Jahangiri",
"Shiva",
""
],
[
"Arunasalam",
"Aliyah",
""
],
[
"Koenig",
"Sven",
""
],
[
"Kumar",
"T. K. Satish",
""
]
] |
1706.02794 | Liron Cohen | Liron Cohen, Glenn Wagner, T.K. Satish Kumar, Howie Choset and Sven
Koenig | Rapid Randomized Restarts for Multi-Agent Path Finding Solvers | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in
artificial intelligence and robotics. It has many real-world applications for
which existing MAPF solvers use various heuristics. However, these solvers are
deterministic and perform poorly on "hard" instances typically characterized by
many agents interfering with each other in a small region. In this paper, we
enhance MAPF solvers with randomization and observe that they exhibit
heavy-tailed distributions of runtimes on hard instances. This leads us to
develop simple rapid randomized restart (RRR) strategies with the intuition
that, given a hard instance, multiple short runs have a better chance of
solving it compared to one long run. We validate this intuition through
experiments and show that our RRR strategies indeed boost the performance of
state-of-the-art MAPF solvers such as iECBS and M*.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2017 23:31:01 GMT"
}
] | 1,497,225,600,000 | [
[
"Cohen",
"Liron",
""
],
[
"Wagner",
"Glenn",
""
],
[
"Kumar",
"T. K. Satish",
""
],
[
"Choset",
"Howie",
""
],
[
"Koenig",
"Sven",
""
]
] |
1706.02897 | Djallel Bouneffouf | Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi | Bandit Models of Human Behavior: Reward Processing in Mental Disorders | Conference on Artificial General Intelligence, AGI-17 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drawing an inspiration from behavioral studies of human decision making, we
propose here a general parametric framework for multi-armed bandit problem,
which extends the standard Thompson Sampling approach to incorporate reward
processing biases associated with several neurological and psychiatric
conditions, including Parkinson's and Alzheimer's diseases,
attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
We demonstrate empirically that the proposed parametric approach can often
outperform the baseline Thompson Sampling on a variety of datasets. Moreover,
from the behavioral modeling perspective, our parametric framework can be
viewed as a first step towards a unifying computational model capturing reward
processing abnormalities across multiple mental conditions.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2017 18:36:12 GMT"
}
] | 1,497,225,600,000 | [
[
"Bouneffouf",
"Djallel",
""
],
[
"Rish",
"Irina",
""
],
[
"Cecchi",
"Guillermo A.",
""
]
] |
1706.02929 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek and Andrzej Matuszewski | Evidence Against Evidence Theory (?!) | 30 pages. arXiv admin note: substantial text overlap with
arXiv:1704.04000 | null | null | IPI PAN report 759, 1994 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is concerned with the apparent greatest weakness of the
Mathematical Theory of Evidence (MTE) of Shafer \cite{Shafer:76}, which has
been strongly criticized by Wasserman \cite{Wasserman:92ijar} - the
relationship to frequencies.
Weaknesses of various proposals of probabilistic interpretation of MTE belief
functions are demonstrated.
A new frequency-based interpretation is presented overcoming various
drawbacks of earlier interpretations.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2017 17:23:34 GMT"
}
] | 1,497,225,600,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
],
[
"Matuszewski",
"Andrzej",
""
]
] |
1706.03122 | Michael Cook | Michael Cook, Adam Summerville and Simon Colton | Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MOBAs represent a huge segment of online gaming and are growing as both an
eSport and a casual genre. The natural starting point for AI researchers
interested in MOBAs is to develop an AI to play the game better than a human -
but MOBAs have many more challenges besides adversarial AI. In this paper we
introduce the reader to the wider context of MOBA culture, propose a range of
challenges faced by the community today, and posit concrete AI projects that
can be undertaken to begin solving them.
| [
{
"version": "v1",
"created": "Fri, 9 Jun 2017 20:57:18 GMT"
}
] | 1,497,312,000,000 | [
[
"Cook",
"Michael",
""
],
[
"Summerville",
"Adam",
""
],
[
"Colton",
"Simon",
""
]
] |
1706.03144 | Pei Cao | Pei Cao, Zhaoyan Fan, Robert X. Gao, Jiong Tang | A Focal Any-Angle Path-finding Algorithm Based on A* on Visibility
Graphs | null | null | 10.1115/1.4040320 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this research, we investigate the subject of path-finding. A pruned
version of visibility graph based on Candidate Vertices is formulated, followed
by a new visibility check technique. Such combination enables us to quickly
identify the useful vertices and thus find the optimal path more efficiently.
The algorithm proposed is demonstrated on various path-finding cases. The
performance of the new technique on visibility graphs is compared to the
traditional A* on Grids, Theta* and A* on Visibility Graphs in terms of path
length, number of nodes evaluated, as well as computational time. The key
algorithmic contribution is that the new approach combines the merits of
grid-based method and visibility graph-based method and thus yields better
overall performance.
| [
{
"version": "v1",
"created": "Fri, 9 Jun 2017 22:19:12 GMT"
}
] | 1,540,857,600,000 | [
[
"Cao",
"Pei",
""
],
[
"Fan",
"Zhaoyan",
""
],
[
"Gao",
"Robert X.",
""
],
[
"Tang",
"Jiong",
""
]
] |
1706.03304 | Neil Newman | Neil Newman and Alexandre Fr\'echette and Kevin Leyton-Brown | Deep Optimization for Spectrum Repacking | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over 13 months in 2016-17 the FCC conducted an "incentive auction" to
repurpose radio spectrum from broadcast television to wireless internet. In the
end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175
broadcasters for voluntarily relinquishing their licenses across 14 UHF
channels. Stations that continued broadcasting were assigned potentially new
channels to fit as densely as possible into the channels that remained. The
government netted more than $7 billion (used to pay down the national debt)
after covering costs. A crucial element of the auction design was the
construction of a solver, dubbed SATFC, that determined whether sets of
stations could be "repacked" in this way; it needed to run every time a station
was given a price quote. This paper describes the process by which we built
SATFC. We adopted an approach we dub "deep optimization", taking a data-driven,
highly parametric, and computationally intensive approach to solver design.
More specifically, to build SATFC we designed software that could pair both
complete and local-search SAT-encoded feasibility checking with a wide range of
domain-specific techniques. We then used automatic algorithm configuration
techniques to construct a portfolio of eight complementary algorithms to be run
in parallel, aiming to achieve good performance on instances that arose in
proprietary auction simulations. To evaluate the impact of our solver in this
paper, we built an open-source reverse auction simulator. We found that within
the short time budget required in practice, SATFC solved more than 95% of the
problems it encountered. Furthermore, the incentive auction paired with SATFC
produced nearly optimal allocations in a restricted setting and substantially
outperformed other alternatives at national scale.
| [
{
"version": "v1",
"created": "Sun, 11 Jun 2017 03:15:20 GMT"
}
] | 1,497,312,000,000 | [
[
"Newman",
"Neil",
""
],
[
"Fréchette",
"Alexandre",
""
],
[
"Leyton-Brown",
"Kevin",
""
]
] |
1706.03469 | Josiah Hanna | Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum | Data-Efficient Policy Evaluation Through Behavior Policy Search | Accepted to ICML 2017; Extended version; 15 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the task of evaluating a policy for a Markov decision process
(MDP). The standard unbiased technique for evaluating a policy is to deploy the
policy and observe its performance. We show that the data collected from
deploying a different policy, commonly called the behavior policy, can be used
to produce unbiased estimates with lower mean squared error than this standard
technique. We derive an analytic expression for the optimal behavior policy ---
the behavior policy that minimizes the mean squared error of the resulting
estimates. Because this expression depends on terms that are unknown in
practice, we propose a novel policy evaluation sub-problem, behavior policy
search: searching for a behavior policy that reduces mean squared error. We
present a behavior policy search algorithm and empirically demonstrate its
effectiveness in lowering the mean squared error of policy performance
estimates.
| [
{
"version": "v1",
"created": "Mon, 12 Jun 2017 05:19:47 GMT"
}
] | 1,497,312,000,000 | [
[
"Hanna",
"Josiah P.",
""
],
[
"Thomas",
"Philip S.",
""
],
[
"Stone",
"Peter",
""
],
[
"Niekum",
"Scott",
""
]
] |
1706.03576 | Martin Biehl | Martin Biehl, Daniel Polani | Action and perception for spatiotemporal patterns | 8 pages, 2 figures, accepted at the European Conference on Artificial
Life 2017, Lyon, France | Proceedings of The Fourteenth European Conference on Artificial
Life (September 2017) p.68-75 | 10.7551/ecal_a_015 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is a contribution to the formalization of the concept of agents in
multivariate Markov chains. Agents are commonly defined as entities that act,
perceive, and are goal-directed. In a multivariate Markov chain (e.g. a
cellular automaton) the transition matrix completely determines the dynamics.
This seems to contradict the possibility of acting entities within such a
system. Here we present definitions of actions and perceptions within
multivariate Markov chains based on entity-sets. Entity-sets represent a
largely independent choice of a set of spatiotemporal patterns that are
considered as all the entities within the Markov chain. For example, the
entity-set can be chosen according to operational closure conditions or
complete specific integration. Importantly, the perception-action loop also
induces an entity-set and is a multivariate Markov chain. We then show that our
definition of actions leads to non-heteronomy and that of perceptions
specialize to the usual concept of perception in the perception-action loop.
| [
{
"version": "v1",
"created": "Mon, 12 Jun 2017 11:44:24 GMT"
}
] | 1,534,118,400,000 | [
[
"Biehl",
"Martin",
""
],
[
"Polani",
"Daniel",
""
]
] |
1706.03906 | Cunjing Ge | Cunjing Ge, Feifei Ma, Tian Liu, Jian Zhang | A New Probabilistic Algorithm for Approximate Model Counting | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constrained counting is important in domains ranging from artificial
intelligence to software analysis. There are already a few approaches for
counting models over various types of constraints. Recently, hashing-based
approaches achieve both theoretical guarantees and scalability, but still rely
on solution enumeration. In this paper, a new probabilistic polynomial time
approximate model counter is proposed, which is also a hashing-based universal
framework, but with only satisfiability queries. A variant with a dynamic
stopping criterion is also presented. Empirical evaluation over benchmarks on
propositional logic formulas and SMT(BV) formulas shows that the approach is
promising.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2017 05:26:02 GMT"
}
] | 1,497,398,400,000 | [
[
"Ge",
"Cunjing",
""
],
[
"Ma",
"Feifei",
""
],
[
"Liu",
"Tian",
""
],
[
"Zhang",
"Jian",
""
]
] |
1706.03940 | Julia Sidorova | S. Podapati, L. Lundberg, L. Skold, O. Rosander, J. Sidorova | Fuzzy Recommendations in Marketing Campaigns | conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The population in Sweden is growing rapidly due to immigration. In this
light, the issue of infrastructure upgrades to provide telecommunication
services is of importance. New antennas can be installed at hot spots of user
demand, which will require an investment, and/or the clientele expansion can be
carried out in a planned manner to promote the exploitation of the
infrastructure in the less loaded geographical zones. In this paper, we explore
the second alternative. Informally speaking, the term Infrastructure-Stressing
describes a user who stays in the zones of high demand, which are prone to
produce service failures, if further loaded. We have studied the
Infrastructure-Stressing population in the light of their correlation with
geo-demographic segments. This is motivated by the fact that specific
geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic
is applied to create an interface between big data, numeric methods for
processing big data and a manager.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2017 07:56:18 GMT"
}
] | 1,497,398,400,000 | [
[
"Podapati",
"S.",
""
],
[
"Lundberg",
"L.",
""
],
[
"Skold",
"L.",
""
],
[
"Rosander",
"O.",
""
],
[
"Sidorova",
"J.",
""
]
] |
1706.04033 | Federico Cerutti | Federico Cerutti and Alice Toniolo and Timothy J. Norman | On Natural Language Generation of Formal Argumentation | 17 pages, 4 figures, technical report | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we provide a first analysis of the research questions that
arise when dealing with the problem of communicating pieces of formal
argumentation through natural language interfaces. It is a generally held
opinion that formal models of argumentation naturally capture human argument,
and some preliminary studies have focused on justifying this view.
Unfortunately, the results are not only inconclusive, but seem to suggest that
explaining formal argumentation to humans is a rather articulated task.
Graphical models for expressing argumentation-based reasoning are appealing,
but often humans require significant training to use these tools effectively.
We claim that natural language interfaces to formal argumentation systems offer
a real alternative, and may be the way forward for systems that capture human
argument.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2017 13:01:53 GMT"
}
] | 1,497,398,400,000 | [
[
"Cerutti",
"Federico",
""
],
[
"Toniolo",
"Alice",
""
],
[
"Norman",
"Timothy J.",
""
]
] |
1706.04317 | Ken Kansky | Ken Kansky, Tom Silver, David A. M\'ely, Mohamed Eldawy, Miguel
L\'azaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix,
Dileep George | Schema Networks: Zero-shot Transfer with a Generative Causal Model of
Intuitive Physics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent adaptation of deep neural network-based methods to reinforcement
learning and planning domains has yielded remarkable progress on individual
tasks. Nonetheless, progress on task-to-task transfer remains limited. In
pursuit of efficient and robust generalization, we introduce the Schema
Network, an object-oriented generative physics simulator capable of
disentangling multiple causes of events and reasoning backward through causes
to achieve goals. The richly structured architecture of the Schema Network can
learn the dynamics of an environment directly from data. We compare Schema
Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a
suite of Breakout variations, reporting results on training efficiency and
zero-shot generalization, consistently demonstrating faster, more robust
learning and better transfer. We argue that generalizing from limited data and
learning causal relationships are essential abilities on the path toward
generally intelligent systems.
| [
{
"version": "v1",
"created": "Wed, 14 Jun 2017 05:11:08 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Aug 2017 23:37:54 GMT"
}
] | 1,503,273,600,000 | [
[
"Kansky",
"Ken",
""
],
[
"Silver",
"Tom",
""
],
[
"Mély",
"David A.",
""
],
[
"Eldawy",
"Mohamed",
""
],
[
"Lázaro-Gredilla",
"Miguel",
""
],
[
"Lou",
"Xinghua",
""
],
[
"Dorfman",
"Nimrod",
""
],
[
"Sidor",
"Szymon",
""
],
[
"Phoenix",
"Scott",
""
],
[
"George",
"Dileep",
""
]
] |
1706.04825 | Lucas Bechberger | Lucas Bechberger and Kai-Uwe K\"uhnberger | Towards Grounding Conceptual Spaces in Neural Representations | accepted at NeSy 2017; The final version of this paper is available
at http://ceur-ws.org/Vol-2003/ | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. It aims at bridging the gap between symbolic and
subsymbolic processing. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. In this paper, we present our approach towards grounding the dimensions
of a conceptual space in latent spaces learned by an InfoGAN from unlabeled
data.
| [
{
"version": "v1",
"created": "Thu, 15 Jun 2017 11:59:06 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Nov 2017 07:27:49 GMT"
}
] | 1,511,308,800,000 | [
[
"Bechberger",
"Lucas",
""
],
[
"Kühnberger",
"Kai-Uwe",
""
]
] |
1706.05171 | Peter Sch\"uller | Mishal Kazmi and Peter Sch\"uller and Y\"ucel Sayg{\i}n | Improving Scalability of Inductive Logic Programming via Pruning and
Best-Effort Optimisation | 24 pages, preprint of article accepted at Expert Systems With
Applications | Expert Systems With Applications 87, pages 291-303, 2017 | 10.1016/j.eswa.2017.06.013 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inductive Logic Programming (ILP) combines rule-based and statistical
artificial intelligence methods, by learning a hypothesis comprising a set of
rules given background knowledge and constraints for the search space. We focus
on extending the XHAIL algorithm for ILP which is based on Answer Set
Programming and we evaluate our extensions using the Natural Language
Processing application of sentence chunking. With respect to processing natural
language, ILP can cater for the constant change in how we use language on a
daily basis. At the same time, ILP does not require huge amounts of training
examples such as other statistical methods and produces interpretable results,
that means a set of rules, which can be analysed and tweaked if necessary. As
contributions we extend XHAIL with (i) a pruning mechanism within the
hypothesis generalisation algorithm which enables learning from larger
datasets, (ii) a better usage of modern solver technology using recently
developed optimisation methods, and (iii) a time budget that permits the usage
of suboptimal results. We evaluate these improvements on the task of sentence
chunking using three datasets from a recent SemEval competition. Results show
that our improvements allow for learning on bigger datasets with results that
are of similar quality to state-of-the-art systems on the same task. Moreover,
we compare the hypotheses obtained on datasets to gain insights on the
structure of each dataset.
| [
{
"version": "v1",
"created": "Fri, 16 Jun 2017 08:02:55 GMT"
}
] | 1,517,443,200,000 | [
[
"Kazmi",
"Mishal",
""
],
[
"Schüller",
"Peter",
""
],
[
"Saygın",
"Yücel",
""
]
] |
1706.05296 | Peter Sunehag | Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki,
Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z.
Leibo, Karl Tuyls, Thore Graepel | Value-Decomposition Networks For Cooperative Multi-Agent Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of cooperative multi-agent reinforcement learning with a
single joint reward signal. This class of learning problems is difficult
because of the often large combined action and observation spaces. In the fully
centralized and decentralized approaches, we find the problem of spurious
rewards and a phenomenon we call the "lazy agent" problem, which arises due to
partial observability. We address these problems by training individual agents
with a novel value decomposition network architecture, which learns to
decompose the team value function into agent-wise value functions. We perform
an experimental evaluation across a range of partially-observable multi-agent
domains and show that learning such value-decompositions leads to superior
results, in particular when combined with weight sharing, role information and
information channels.
| [
{
"version": "v1",
"created": "Fri, 16 Jun 2017 14:47:21 GMT"
}
] | 1,497,830,400,000 | [
[
"Sunehag",
"Peter",
""
],
[
"Lever",
"Guy",
""
],
[
"Gruslys",
"Audrunas",
""
],
[
"Czarnecki",
"Wojciech Marian",
""
],
[
"Zambaldi",
"Vinicius",
""
],
[
"Jaderberg",
"Max",
""
],
[
"Lanctot",
"Marc",
""
],
[
"Sonnerat",
"Nicolas",
""
],
[
"Leibo",
"Joel Z.",
""
],
[
"Tuyls",
"Karl",
""
],
[
"Graepel",
"Thore",
""
]
] |
1706.05518 | Jes\'us Ib\'a\~nez Ruiz | Jes\'us Ib\'a\~nez-Ruiz, Laura Sebasti\'a, Eva Onaindia | Evaluating the quality of tourist agendas customized to different travel
styles | Twenty-seventh Workshop on Constraint Satisfaction Techniques for
Planning and Scheduling Problems (COPLAS'17) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many tourist applications provide a personalized tourist agenda with the list
of recommended activities to the user. These applications must undoubtedly deal
with the constraints and preferences that define the user interests. Among
these preferences, we can find those that define the travel style of the user,
such as the rhythm of the trip, the number of visits to include in the tour or
the priority to visits of special interest for the user. In this paper, we deal
with the task of creating a customized tourist agenda as a planning and
scheduling application capable of conveniently scheduling the most appropriate
goals (visits) so as to maximize the user satisfaction with the tourist route.
This paper makes an analysis of the meaning of the travel style preferences and
compares the quality of the solutions obtained by two different solvers, a
PDDL-based planner and a Constraint Satisfaction Problem solver. We also define
several quality metrics and perform extensive experiments in order to evaluate
the results obtained with both solvers.
| [
{
"version": "v1",
"created": "Sat, 17 Jun 2017 11:59:40 GMT"
}
] | 1,497,916,800,000 | [
[
"Ibáñez-Ruiz",
"Jesús",
""
],
[
"Sebastiá",
"Laura",
""
],
[
"Onaindia",
"Eva",
""
]
] |
1706.05733 | Georgios Feretzakis | Dimitris Kalles, Vassilios S. Verykios, Georgios Feretzakis,
Athanasios Papagelis | Data set operations to hide decision tree rules | 7 pages, 4 figures and 2 tables. ECAI 2016 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper focuses on preserving the privacy of sensitive patterns when
inducing decision trees. We adopt a record augmentation approach for hiding
sensitive classification rules in binary datasets. Such a hiding methodology is
preferred over other heuristic solutions like output perturbation or
cryptographic techniques - which restrict the usability of the data - since the
raw data itself is readily available for public use. We show some key lemmas
which are related to the hiding process and we also demonstrate the methodology
with an example and an indicative experiment using a prototype hiding tool.
| [
{
"version": "v1",
"created": "Sun, 18 Jun 2017 21:57:36 GMT"
}
] | 1,497,916,800,000 | [
[
"Kalles",
"Dimitris",
""
],
[
"Verykios",
"Vassilios S.",
""
],
[
"Feretzakis",
"Georgios",
""
],
[
"Papagelis",
"Athanasios",
""
]
] |
1706.06051 | Hanan Rosemarin | Hanan Rosemarin and John P. Dickerson and Sarit Kraus | Learning to Schedule Deadline- and Operator-Sensitive Tasks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of semi-autonomous and autonomous robotic assistants to aid in care
of the elderly is expected to ease the burden on human caretakers, with
small-stage testing already occurring in a variety of countries. Yet, it is
likely that these robots will need to request human assistance via
teleoperation when domain expertise is needed for a specific task. As
deployment of robotic assistants moves to scale, mapping these requests for
human aid to the teleoperators themselves will be a difficult online
optimization problem. In this paper, we design a system that allocates requests
to a limited number of teleoperators, each with different specialities, in an
online fashion. We generalize a recent model of online job scheduling with a
worst-case competitive-ratio bound to our setting. Next, we design a scalable
machine-learning-based teleoperator-aware task scheduling algorithm and show,
experimentally, that it performs well when compared to an omniscient optimal
scheduling algorithm.
| [
{
"version": "v1",
"created": "Mon, 19 Jun 2017 16:42:23 GMT"
}
] | 1,497,916,800,000 | [
[
"Rosemarin",
"Hanan",
""
],
[
"Dickerson",
"John P.",
""
],
[
"Kraus",
"Sarit",
""
]
] |
1706.06133 | Nicholas Cheney | Nick Cheney, Josh Bongard, Vytas SunSpiral, Hod Lipson | Scalable Co-Optimization of Morphology and Control in Embodied Machines | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology, the theory of
embodied cognition posits that behavior arises from a close coupling between
body plan and sensorimotor control, which suggests why co-optimizing these two
subsystems is so difficult: most evolutionary changes to morphology tend to
adversely impact sensorimotor control, leading to an overall decrease in
behavioral performance. Here, we further examine this hypothesis and
demonstrate a technique for "morphological innovation protection", which
temporarily reduces selection pressure on recently morphologically-changed
individuals, thus enabling evolution some time to "readapt" to the new
morphology with subsequent control policy mutations. We show the potential for
this method to avoid local optima and converge to similar highly fit
morphologies across widely varying initial conditions, while sustaining fitness
improvements further into optimization. While this technique is admittedly only
the first of many steps that must be taken to achieve scalable optimization of
embodied machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable the automation
of robot design and behavioral training -- while simultaneously providing a
testbed to investigate the theory of embodied cognition.
| [
{
"version": "v1",
"created": "Mon, 19 Jun 2017 18:47:57 GMT"
},
{
"version": "v2",
"created": "Tue, 12 Dec 2017 20:10:09 GMT"
}
] | 1,513,209,600,000 | [
[
"Cheney",
"Nick",
""
],
[
"Bongard",
"Josh",
""
],
[
"SunSpiral",
"Vytas",
""
],
[
"Lipson",
"Hod",
""
]
] |
1706.06160 | Arjun Bhardwaj | Arjun Bhardwaj, Alexander Rudnicky | User Intent Classification using Memory Networks: A Comparative Analysis
for a Limited Data Scenario | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this report, we provide a comparative analysis of different techniques for
user intent classification towards the task of app recommendation. We analyse
the performance of different models and architectures for multi-label
classification over a dataset with a relative large number of classes and only
a handful examples of each class. We focus, in particular, on memory network
architectures, and compare how well the different versions perform under the
task constraints. Since the classifier is meant to serve as a module in a
practical dialog system, it needs to be able to work with limited training data
and incorporate new data on the fly. We devise a 1-shot learning task to test
the models under the above constraint. We conclude that relatively simple
versions of memory networks perform better than other approaches. Although, for
tasks with very limited data, simple non-parametric methods perform comparably,
without needing the extra training data.
| [
{
"version": "v1",
"created": "Mon, 19 Jun 2017 20:12:07 GMT"
}
] | 1,498,003,200,000 | [
[
"Bhardwaj",
"Arjun",
""
],
[
"Rudnicky",
"Alexander",
""
]
] |
1706.06328 | Reuth Mirsky | Reuth Mirsky, Ya'akov Gal, David Tolpin | Session Analysis using Plan Recognition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents preliminary results of our work with a major financial
company, where we try to use methods of plan recognition in order to
investigate the interactions of a costumer with the company's online interface.
In this paper, we present the first steps of integrating a plan recognition
algorithm in a real-world application for detecting and analyzing the
interactions of a costumer. It uses a novel approach for plan recognition from
bare-bone UI data, which reasons about the plan library at the lowest
recognition level in order to define the relevancy of actions in our domain,
and then uses it to perform plan recognition.
We present preliminary results of inference on three different use-cases
modeled by domain experts from the company, and show that this approach manages
to decrease the overload of information required from an analyst to evaluate a
costumer's session - whether this is a malicious or benign session, whether the
intended tasks were completed, and if not - what actions are expected next.
| [
{
"version": "v1",
"created": "Tue, 20 Jun 2017 09:03:53 GMT"
}
] | 1,498,003,200,000 | [
[
"Mirsky",
"Reuth",
""
],
[
"Gal",
"Ya'akov",
""
],
[
"Tolpin",
"David",
""
]
] |
1706.06366 | Lucas Bechberger | Lucas Bechberger and Kai-Uwe K\"uhnberger | A Thorough Formalization of Conceptual Spaces | accepted at KI 2017 (http://ki2017.tu-dortmund.de/), final
publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-67190-1_5 | null | 10.1007/978-3-319-67190-1_5 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. After pointing out a problem with the convexity requirement, we propose
a formalization of conceptual spaces based on fuzzy star-shaped sets. Our
formalization uses a parametric definition of concepts and extends the original
framework by adding means to represent correlations between different domains
in a geometric way. Moreover, we define computationally efficient operations on
concepts (intersection, union, and projection onto a subspace) and show that
these operations can support both learning and reasoning processes.
| [
{
"version": "v1",
"created": "Tue, 20 Jun 2017 11:19:28 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Sep 2017 07:48:49 GMT"
}
] | 1,506,038,400,000 | [
[
"Bechberger",
"Lucas",
""
],
[
"Kühnberger",
"Kai-Uwe",
""
]
] |
1706.06827 | Ari Weinstein | Ari Weinstein and Matthew M. Botvinick | Structure Learning in Motor Control:A Deep Reinforcement Learning Model | 39th Annual Meeting of the Cognitive Science Society, to appear | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motor adaptation displays a structure-learning effect: adaptation to a new
perturbation occurs more quickly when the subject has prior exposure to
perturbations with related structure. Although this `learning-to-learn' effect
is well documented, its underlying computational mechanisms are poorly
understood. We present a new model of motor structure learning, approaching it
from the point of view of deep reinforcement learning. Previous work outside of
motor control has shown how recurrent neural networks can account for
learning-to-learn effects. We leverage this insight to address motor learning,
by importing it into the setting of model-based reinforcement learning. We
apply the resulting processing architecture to empirical findings from a
landmark study of structure learning in target-directed reaching (Braun et al.,
2009), and discuss its implications for a wider range of learning-to-learn
phenomena.
| [
{
"version": "v1",
"created": "Wed, 21 Jun 2017 11:20:43 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jul 2017 14:31:27 GMT"
}
] | 1,499,990,400,000 | [
[
"Weinstein",
"Ari",
""
],
[
"Botvinick",
"Matthew M.",
""
]
] |
1706.06906 | Toby Walsh | Toby Walsh | Expert and Non-Expert Opinion about Technological Unemployment | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is significant concern that technological advances, especially in
Robotics and Artificial Intelligence (AI), could lead to high levels of
unemployment in the coming decades. Studies have estimated that around half of
all current jobs are at risk of automation. To look into this issue in more
depth, we surveyed experts in Robotics and AI about the risk, and compared
their views with those of non-experts. Whilst the experts predicted a
significant number of occupations were at risk of automation in the next two
decades, they were more cautious than people outside the field in predicting
occupations at risk. Their predictions were consistent with their estimates for
when computers might be expected to reach human level performance across a wide
range of skills. These estimates were typically decades later than those of the
non-experts. Technological barriers may therefore provide society with more
time to prepare for an automated future than the public fear. In addition,
public expectations may need to be dampened about the speed of progress to be
expected in Robotics and AI.
| [
{
"version": "v1",
"created": "Wed, 21 Jun 2017 13:51:57 GMT"
}
] | 1,498,089,600,000 | [
[
"Walsh",
"Toby",
""
]
] |
1706.06952 | Philip Rodgers | Philip Rodgers, John Levine | Ensemble Framework for Real-time Decision Making | 7 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a new framework for real-time decision making in video
games. An Ensemble agent is a compound agent composed of multiple agents, each
with its own tasks or goals to achieve. Usually when dealing with real-time
decision making, reactive agents are used; that is agents that return a
decision based on the current state. While reactive agents are very fast, most
games require more than just a rule-based agent to achieve good results.
Deliberative agents---agents that use a forward model to search future
states---are very useful in games with no hard time limit, such as Go or
Backgammon, but generally take too long for real-time games. The Ensemble
framework addresses this issue by allowing the agent to be both deliberative
and reactive at the same time. This is achieved by breaking up the game-play
into logical roles and having highly focused components for each role, with
each component disregarding anything outwith its own role. Reactive agents can
be used where a reactive agent is suited to the role, and where a deliberative
approach is required, branching is kept to a minimum by the removal of all
extraneous factors, enabling an informed decision to be made within a much
smaller time-frame. An Arbiter is used to combine the component results,
allowing high performing agents to be created from simple, efficient
components.
| [
{
"version": "v1",
"created": "Wed, 21 Jun 2017 15:17:57 GMT"
}
] | 1,498,089,600,000 | [
[
"Rodgers",
"Philip",
""
],
[
"Levine",
"John",
""
]
] |
1706.06975 | Mark Stalzer | Mark A. Stalzer | On the enumeration of sentences by compactness | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Presented is a Julia meta-program that discovers compact theories from data
if they exist. It writes candidate theories in Julia and then validates:
tossing the bad theories and keeping the good theories. Compactness is measured
by a metric: such as the number of space-time derivatives. The underlying
algorithm is applicable to a wide variety of combinatorics problems and
compactness serves to cut down the search space.
| [
{
"version": "v1",
"created": "Thu, 15 Jun 2017 22:57:06 GMT"
}
] | 1,498,089,600,000 | [
[
"Stalzer",
"Mark A.",
""
]
] |
1706.07068 | Ahmed Elgammal | Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone | CAN: Creative Adversarial Networks, Generating "Art" by Learning About
Styles and Deviating from Style Norms | This paper is an extended version of a paper published on the eighth
International Conference on Computational Creativity (ICCC), held in Atlanta,
GA, June 20th-June 22nd, 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new system for generating art. The system generates art by
looking at art and learning about style; and becomes creative by increasing the
arousal potential of the generated art by deviating from the learned styles. We
build over Generative Adversarial Networks (GAN), which have shown the ability
to learn to generate novel images simulating a given distribution. We argue
that such networks are limited in their ability to generate creative products
in their original design. We propose modifications to its objective to make it
capable of generating creative art by maximizing deviation from established
styles and minimizing deviation from art distribution. We conducted experiments
to compare the response of human subjects to the generated art with their
response to art created by artists. The results show that human subjects could
not distinguish art generated by the proposed system from art generated by
contemporary artists and shown in top art fairs. Human subjects even rated the
generated images higher on various scales.
| [
{
"version": "v1",
"created": "Wed, 21 Jun 2017 18:05:13 GMT"
}
] | 1,498,176,000,000 | [
[
"Elgammal",
"Ahmed",
""
],
[
"Liu",
"Bingchen",
""
],
[
"Elhoseiny",
"Mohamed",
""
],
[
"Mazzone",
"Marian",
""
]
] |
1706.07160 | Nikaash Puri | Nikaash Puri, Piyush Gupta, Pratiksha Agarwal, Sukriti Verma, and
Balaji Krishnamurthy | MAGIX: Model Agnostic Globally Interpretable Explanations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explaining the behavior of a black box machine learning model at the instance
level is useful for building trust. However, it is also important to understand
how the model behaves globally. Such an understanding provides insight into
both the data on which the model was trained and the patterns that it learned.
We present here an approach that learns if-then rules to globally explain the
behavior of black box machine learning models that have been used to solve
classification problems. The approach works by first extracting conditions that
were important at the instance level and then evolving rules through a genetic
algorithm with an appropriate fitness function. Collectively, these rules
represent the patterns followed by the model for decisioning and are useful for
understanding its behavior. We demonstrate the validity and usefulness of the
approach by interpreting black box models created using publicly available data
sets as well as a private digital marketing data set.
| [
{
"version": "v1",
"created": "Thu, 22 Jun 2017 03:55:28 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Oct 2017 04:45:15 GMT"
},
{
"version": "v3",
"created": "Fri, 15 Jun 2018 10:46:29 GMT"
}
] | 1,529,280,000,000 | [
[
"Puri",
"Nikaash",
""
],
[
"Gupta",
"Piyush",
""
],
[
"Agarwal",
"Pratiksha",
""
],
[
"Verma",
"Sukriti",
""
],
[
"Krishnamurthy",
"Balaji",
""
]
] |
1706.07269 | Tim Miller | Tim Miller | Explanation in Artificial Intelligence: Insights from the Social
Sciences | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been a recent resurgence in the area of explainable artificial
intelligence as researchers and practitioners seek to make their algorithms
more understandable. Much of this research is focused on explicitly explaining
decisions or actions to a human observer, and it should not be controversial to
say that looking at how humans explain to each other can serve as a useful
starting point for explanation in artificial intelligence. However, it is fair
to say that most work in explainable artificial intelligence uses only the
researchers' intuition of what constitutes a `good' explanation. There exists
vast and valuable bodies of research in philosophy, psychology, and cognitive
science of how people define, generate, select, evaluate, and present
explanations, which argues that people employ certain cognitive biases and
social expectations towards the explanation process. This paper argues that the
field of explainable artificial intelligence should build on this existing
research, and reviews relevant papers from philosophy, cognitive
psychology/science, and social psychology, which study these topics. It draws
out some important findings, and discusses ways that these can be infused with
work on explainable artificial intelligence.
| [
{
"version": "v1",
"created": "Thu, 22 Jun 2017 11:46:11 GMT"
},
{
"version": "v2",
"created": "Thu, 24 May 2018 02:43:30 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Aug 2018 00:50:00 GMT"
}
] | 1,534,377,600,000 | [
[
"Miller",
"Tim",
""
]
] |
1706.07527 | Hemanth Venkateswara | Hemanth Venkateswara, Shayok Chakraborty, Troy McDaniel, Sethuraman
Panchanathan | Model Selection with Nonlinear Embedding for Unsupervised Domain
Adaptation | AAAI Workshops 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain adaptation deals with adapting classifiers trained on data from a
source distribution, to work effectively on data from a target distribution. In
this paper, we introduce the Nonlinear Embedding Transform (NET) for
unsupervised domain adaptation. The NET reduces cross-domain disparity through
nonlinear domain alignment. It also embeds the domain-aligned data such that
similar data points are clustered together. This results in enhanced
classification. To determine the parameters in the NET model (and in other
unsupervised domain adaptation models), we introduce a validation procedure by
sampling source data points that are similar in distribution to the target
data. We test the NET and the validation procedure using popular image datasets
and compare the classification results across competitive procedures for
unsupervised domain adaptation.
| [
{
"version": "v1",
"created": "Fri, 23 Jun 2017 00:04:38 GMT"
}
] | 1,498,435,200,000 | [
[
"Venkateswara",
"Hemanth",
""
],
[
"Chakraborty",
"Shayok",
""
],
[
"McDaniel",
"Troy",
""
],
[
"Panchanathan",
"Sethuraman",
""
]
] |
1706.08090 | Jarryd Martin | Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter | Count-Based Exploration in Feature Space for Reinforcement Learning | Conference: Twenty-sixth International Joint Conference on Artificial
Intelligence (IJCAI-17), 8 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new count-based optimistic exploration algorithm for
Reinforcement Learning (RL) that is feasible in environments with
high-dimensional state-action spaces. The success of RL algorithms in these
domains depends crucially on generalisation from limited training experience.
Function approximation techniques enable RL agents to generalise in order to
estimate the value of unvisited states, but at present few methods enable
generalisation regarding uncertainty. This has prevented the combination of
scalable RL algorithms with efficient exploration strategies that drive the
agent to reduce its uncertainty. We present a new method for computing a
generalised state visit-count, which allows the agent to estimate the
uncertainty associated with any state. Our \phi-pseudocount achieves
generalisation by exploiting same feature representation of the state space
that is used for value function approximation. States that have less frequently
observed features are deemed more uncertain. The \phi-Exploration-Bonus
algorithm rewards the agent for exploring in feature space rather than in the
untransformed state space. The method is simpler and less computationally
expensive than some previous proposals, and achieves near state-of-the-art
results on high-dimensional RL benchmarks.
| [
{
"version": "v1",
"created": "Sun, 25 Jun 2017 12:39:44 GMT"
}
] | 1,498,521,600,000 | [
[
"Martin",
"Jarryd",
""
],
[
"Sasikumar",
"Suraj Narayanan",
""
],
[
"Everitt",
"Tom",
""
],
[
"Hutter",
"Marcus",
""
]
] |
1706.08100 | Fabio Patrizi | Ronen Brafman, Giuseppe De Giacomo, Fabio Patrizi | Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces
(Preliminary Version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Markov Decision Processes (MDPs), the reward obtained in a state depends
on the properties of the last state and action. This state dependency makes it
difficult to reward more interesting long-term behaviors, such as always
closing a door after it has been opened, or providing coffee only following a
request. Extending MDPs to handle such non-Markovian reward function was the
subject of two previous lines of work, both using variants of LTL to specify
the reward function and then compiling the new model back into a Markovian
model. Building upon recent progress in the theories of temporal logics over
finite traces, we adopt LDLf for specifying non-Markovian rewards and provide
an elegant automata construction for building a Markovian model, which extends
that of previous work and offers strong minimality and compositionality
guarantees.
| [
{
"version": "v1",
"created": "Sun, 25 Jun 2017 13:37:00 GMT"
}
] | 1,498,521,600,000 | [
[
"Brafman",
"Ronen",
""
],
[
"De Giacomo",
"Giuseppe",
""
],
[
"Patrizi",
"Fabio",
""
]
] |
1706.08106 | Christophe Guyeux | Wiem Elghazel, Kamal Medjaher, Nourredine Zerhouni, Jacques Bahi,
Ahamd Farhat, Christophe Guyeux, and Mourad Hakem | Random Forests for Industrial Device Functioning Diagnostics Using
Wireless Sensor Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, random forests are proposed for operating devices diagnostics
in the presence of a variable number of features. In various contexts, like
large or difficult-to-access monitored areas, wired sensor networks providing
features to achieve diagnostics are either very costly to use or totally
impossible to spread out. Using a wireless sensor network can solve this
problem, but this latter is more subjected to flaws. Furthermore, the networks'
topology often changes, leading to a variability in quality of coverage in the
targeted area. Diagnostics at the sink level must take into consideration that
both the number and the quality of the provided features are not constant, and
that some politics like scheduling or data aggregation may be developed across
the network. The aim of this article is ($1$) to show that random forests are
relevant in this context, due to their flexibility and robustness, and ($2$) to
provide first examples of use of this method for diagnostics based on data
provided by a wireless sensor network.
| [
{
"version": "v1",
"created": "Sun, 25 Jun 2017 13:54:33 GMT"
}
] | 1,498,521,600,000 | [
[
"Elghazel",
"Wiem",
""
],
[
"Medjaher",
"Kamal",
""
],
[
"Zerhouni",
"Nourredine",
""
],
[
"Bahi",
"Jacques",
""
],
[
"Farhat",
"Ahamd",
""
],
[
"Guyeux",
"Christophe",
""
],
[
"Hakem",
"Mourad",
""
]
] |
1706.08317 | Eliseo Marzal | Eliseo Marzal, Mohannad Babli, Eva Onaindia, Laura Sebastia | Handling PDDL3.0 State Trajectory Constraints with Temporal Landmarks | Workshop on Constraint Satisfaction Techniques for Planning and
Scheduling (COPLAS), (2017) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal landmarks have been proved to be a helpful mechanism to deal with
temporal planning problems, specifically to improve planners performance and
handle problems with deadline constraints. In this paper, we show the strength
of using temporal landmarks to handle the state trajectory constraints of
PDDL3.0. We analyze the formalism of TempLM, a temporal planner particularly
aimed at solving planning problems with deadlines, and we present a detailed
study that exploits the underlying temporal landmark-based mechanism of TempLM
for representing and reasoning with trajectory constraints.
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2017 10:56:57 GMT"
}
] | 1,498,521,600,000 | [
[
"Marzal",
"Eliseo",
""
],
[
"Babli",
"Mohannad",
""
],
[
"Onaindia",
"Eva",
""
],
[
"Sebastia",
"Laura",
""
]
] |
1706.08439 | Marina Sapir | Marina Sapir | Optimal choice: new machine learning problem and its solution | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The task of learning to pick a single preferred example out a finite set of
examples, an "optimal choice problem", is a supervised machine learning problem
with complex, structured input. Problems of optimal choice emerge often in
various practical applications. We formalize the problem, show that it does not
satisfy the assumptions of statistical learning theory, yet it can be solved
efficiently in some cases. We propose two approaches to solve the problem. Both
of them reach good solutions on real life data from a signal processing
application.
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2017 15:32:33 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Jul 2017 17:28:23 GMT"
}
] | 1,499,385,600,000 | [
[
"Sapir",
"Marina",
""
]
] |
1706.08611 | Edward Zulkoski | Edward Zulkoski, Ruben Martins, Christoph Wintersteiger, Robert
Robere, Jia Liang, Krzysztof Czarnecki, Vijay Ganesh | Relating Complexity-theoretic Parameters with SAT Solver Performance | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the years complexity theorists have proposed many structural parameters
to explain the surprising efficiency of conflict-driven clause-learning (CDCL)
SAT solvers on a wide variety of large industrial Boolean instances. While some
of these parameters have been studied empirically, until now there has not been
a unified comparative study of their explanatory power on a comprehensive
benchmark. We correct this state of affairs by conducting a large-scale
empirical evaluation of CDCL SAT solver performance on nearly 7000 industrial
and crafted formulas against several structural parameters such as backdoors,
treewidth, backbones, and community structure.
Our study led us to several results. First, we show that while such
parameters only weakly correlate with CDCL solving time, certain combinations
of them yield much better regression models. Second, we show how some
parameters can be used as a "lens" to better understand the efficiency of
different solving heuristics. Finally, we propose a new complexity-theoretic
parameter, which we call learning-sensitive with restarts (LSR) backdoors, that
extends the notion of learning-sensitive (LS) backdoors to incorporate restarts
and discuss algorithms to compute them. We mathematically prove that for
certain class of instances minimal LSR-backdoors are exponentially smaller than
minimal-LS backdoors.
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2017 21:40:30 GMT"
}
] | 1,498,608,000,000 | [
[
"Zulkoski",
"Edward",
""
],
[
"Martins",
"Ruben",
""
],
[
"Wintersteiger",
"Christoph",
""
],
[
"Robere",
"Robert",
""
],
[
"Liang",
"Jia",
""
],
[
"Czarnecki",
"Krzysztof",
""
],
[
"Ganesh",
"Vijay",
""
]
] |
1706.08627 | Roberto Amadini | Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro | SUNNY-CP and the MiniZinc Challenge | Under consideration in Theory and Practice of Logic Programming
(TPLP) | Theory and Practice of Logic Programming, Volume 18, Issue 1,
January 2018 , pp. 81-96 | 10.1017/S1471068417000205 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of the portfolio solver sunny-cp, and we discuss its performance
in the MiniZinc Challenge---the annual international competition for CP
solvers---where it won two gold medals in 2015 and 2016. Under consideration in
Theory and Practice of Logic Programming (TPLP)
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2017 23:48:14 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Jun 2017 00:23:05 GMT"
},
{
"version": "v3",
"created": "Wed, 5 Jul 2017 23:49:34 GMT"
}
] | 1,569,456,000,000 | [
[
"Amadini",
"Roberto",
""
],
[
"Gabbrielli",
"Maurizio",
""
],
[
"Mauro",
"Jacopo",
""
]
] |
1706.09278 | Bhushan Kotnis | Bhushan Kotnis and Vivi Nastase | Learning Knowledge Graph Embeddings with Type Regularizer | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning relations based on evidence from knowledge bases relies on
processing the available relation instances. Many relations, however, have
clear domain and range, which we hypothesize could help learn a better, more
generalizing, model. We include such information in the RESCAL model in the
form of a regularization factor added to the loss function that takes into
account the types (categories) of the entities that appear as arguments to
relations in the knowledge base. We note increased performance compared to the
baseline model in terms of mean reciprocal rank and hits@N, N = 1, 3, 10.
Furthermore, we discover scenarios that significantly impact the effectiveness
of the type regularizer.
| [
{
"version": "v1",
"created": "Wed, 28 Jun 2017 13:24:55 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Mar 2018 12:41:59 GMT"
}
] | 1,520,208,000,000 | [
[
"Kotnis",
"Bhushan",
""
],
[
"Nastase",
"Vivi",
""
]
] |
1706.09737 | Yohanes Khosiawan | Yohanes Khosiawan and Izabela Nielsen | Indoor UAV scheduling with Restful Task Assignment Algorithm | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Research in UAV scheduling has obtained an emerging interest from scientists
in the optimization field. When the scheduling itself has established a strong
root since the 19th century, works on UAV scheduling in indoor environment has
come forth in the latest decade. Several works on scheduling UAV operations in
indoor (two and three dimensional) and outdoor environments are reported. In
this paper, a further study on UAV scheduling in three dimensional indoor
environment is investigated. Dealing with indoor environment\textemdash where
humans, UAVs, and other elements or infrastructures are likely to coexist in
the same space\textemdash draws attention towards the safety of the operations.
In relation to the battery level, a preserved battery level leads to safer
operations, promoting the UAV to have a decent remaining power level. A
methodology which consists of a heuristic approach based on Restful Task
Assignment Algorithm, incorporated with Particle Swarm Optimization Algorithm,
is proposed. The motivation is to preserve the battery level throughout the
operations, which promotes less possibility in having failed UAVs on duty. This
methodology is tested with 54 benchmark datasets stressing on 4 different
aspects: geographical distance, number of tasks, number of predecessors, and
slack time. The test results and their characteristics in regard to the
proposed methodology are discussed and presented.
| [
{
"version": "v1",
"created": "Thu, 29 Jun 2017 13:11:39 GMT"
}
] | 1,498,780,800,000 | [
[
"Khosiawan",
"Yohanes",
""
],
[
"Nielsen",
"Izabela",
""
]
] |
1706.10117 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | Restricted Causal Inference Algorithm | M.A. K{\l}opotek: Restricted Causal Inference Algorithm. [in:] B.
Pehrson, I. Simon Eds.: Proc. World Computer Congress of IFIP . Hamburg 28
August - 2 September 1994, Vol.1, Elsevier Scientific Publishers
(North-Holland), Amsterdam, pp. 342-347 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a new algorithm for recovery of belief network structure
from data handling hidden variables. It consists essentially in an extension of
the CI algorithm of Spirtes et al. by restricting the number of conditional
dependencies checked up to k variables and in an extension of the original CI
by additional steps transforming so called partial including path graph into a
belief network. Its correctness is demonstrated.
| [
{
"version": "v1",
"created": "Fri, 30 Jun 2017 10:57:53 GMT"
}
] | 1,499,040,000,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
1707.00112 | Rachit Agarwal | Garvita Bajaj, Rachit Agarwal, Pushpendra Singh, Nikolaos Georgantas,
Valerie Issarny | A study of existing Ontologies in the IoT-domain | Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of
Things | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several domains have adopted the increasing use of IoT-based devices to
collect sensor data for generating abstractions and perceptions of the real
world. This sensor data is multi-modal and heterogeneous in nature. This
heterogeneity induces interoperability issues while developing cross-domain
applications, thereby restricting the possibility of reusing sensor data to
develop new applications. As a solution to this, semantic approaches have been
proposed in the literature to tackle problems related to interoperability of
sensor data. Several ontologies have been proposed to handle different aspects
of IoT-based sensor data collection, ranging from discovering the IoT sensors
for data collection to applying reasoning on the collected sensor data for
drawing inferences. In this paper, we survey these existing semantic ontologies
to provide an overview of the recent developments in this field. We highlight
the fundamental ontological concepts (e.g., sensor-capabilities and
context-awareness) required for an IoT-based application, and survey the
existing ontologies which include these concepts. Based on our study, we also
identify the shortcomings of currently available ontologies, which serves as a
stepping stone to state the need for a common unified ontology for the IoT
domain.
| [
{
"version": "v1",
"created": "Sat, 1 Jul 2017 08:31:28 GMT"
}
] | 1,499,126,400,000 | [
[
"Bajaj",
"Garvita",
""
],
[
"Agarwal",
"Rachit",
""
],
[
"Singh",
"Pushpendra",
""
],
[
"Georgantas",
"Nikolaos",
""
],
[
"Issarny",
"Valerie",
""
]
] |
1707.00228 | Pavel Surynek | Pavel Surynek, Ariel Felner, Roni Stern, Eli Boyarski | Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem
to Suboptimal Variants | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multi-agent path finding (MAPF) the task is to find non-conflicting paths
for multiple agents. In this paper we focus on finding suboptimal solutions for
MAPF for the sum-of-costs variant. Recently, a SAT-based approached was
developed to solve this problem and proved beneficial in many cases when
compared to other search-based solvers. In this paper, we present SAT-based
unbounded- and bounded-suboptimal algorithms and compare them to relevant
algorithms. Experimental results show that in many case the SAT-based solver
significantly outperforms the search-based solvers.
| [
{
"version": "v1",
"created": "Sun, 2 Jul 2017 03:08:26 GMT"
}
] | 1,499,126,400,000 | [
[
"Surynek",
"Pavel",
""
],
[
"Felner",
"Ariel",
""
],
[
"Stern",
"Roni",
""
],
[
"Boyarski",
"Eli",
""
]
] |
1707.00614 | J. G. Wolff | J Gerard Wolff | A Roadmap for the Development of the "SP Machine" for Artificial
Intelligence | Accepted for publication in the journal "Complexity" | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a roadmap for the development of the "SP Machine", based
on the "SP Theory of Intelligence" and its realisation in the "SP Computer
Model". The SP Machine will be developed initially as a software virtual
machine with high levels of parallel processing, hosted on a high-performance
computer. The system should help users visualise knowledge structures and
processing. Research is needed into how the system may discover low-level
features in speech and in images. Strengths of the SP System in the processing
of natural language may be augmented, in conjunction with the further
development of the SP System's strengths in unsupervised learning. Strengths of
the SP System in pattern recognition may be developed for computer vision. Work
is needed on the representation of numbers and the performance of arithmetic
processes. A computer model is needed of "SP-Neural", the version of the SP
Theory expressed in terms of neurons and their inter-connections. The SP
Machine has potential in many areas of application, several of which may be
realised on short-to-medium timescales.
| [
{
"version": "v1",
"created": "Wed, 28 Jun 2017 11:01:16 GMT"
},
{
"version": "v2",
"created": "Sat, 4 Aug 2018 09:19:39 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Dec 2018 22:25:47 GMT"
}
] | 1,545,177,600,000 | [
[
"Wolff",
"J Gerard",
""
]
] |
1707.00790 | Hamid Mirzaei Buini | Hamid Mirzaei, Mona Fathollahi, Tony Givargis | OPEB: Open Physical Environment Benchmark for Artificial Intelligence | Accepted in 3rd IEEE International Forum on Research and Technologies
for Society and Industry 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence methods to solve continuous- control tasks have made
significant progress in recent years. However, these algorithms have important
limitations and still need significant improvement to be used in industry and
real- world applications. This means that this area is still in an active
research phase. To involve a large number of research groups, standard
benchmarks are needed to evaluate and compare proposed algorithms. In this
paper, we propose a physical environment benchmark framework to facilitate
collaborative research in this area by enabling different research groups to
integrate their designed benchmarks in a unified cloud-based repository and
also share their actual implemented benchmarks via the cloud. We demonstrate
the proposed framework using an actual implementation of the classical
mountain-car example and present the results obtained using a Reinforcement
Learning algorithm.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2017 00:42:57 GMT"
}
] | 1,499,212,800,000 | [
[
"Mirzaei",
"Hamid",
""
],
[
"Fathollahi",
"Mona",
""
],
[
"Givargis",
"Tony",
""
]
] |
1707.00791 | Clifford Champion | Clifford Champion and Charles Elkan | Visualizing the Consequences of Evidence in Bayesian Networks | 9 pages, 11 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the challenge of viewing and navigating Bayesian
networks as their structural size and complexity grow. Starting with a review
of the state of the art of visualizing Bayesian networks, an area which has
largely been passed over, we improve upon existing visualizations in three
ways. First, we apply a disciplined approach to the graphic design of the basic
elements of the Bayesian network. Second, we propose a technique for direct,
visual comparison of posterior distributions resulting from alternative
evidence sets. Third, we leverage a central mathematical tool in information
theory, to assist the user in finding variables of interest in the network, and
to reduce visual complexity where unimportant. We present our methods applied
to two modestly large Bayesian networks constructed from real-world data sets.
Results suggest the new techniques can be a useful tool for discovering
information flow phenomena, and also for qualitative comparisons of different
evidence configurations, especially in large probabilistic networks.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2017 00:43:16 GMT"
}
] | 1,499,212,800,000 | [
[
"Champion",
"Clifford",
""
],
[
"Elkan",
"Charles",
""
]
] |
1707.00936 | Amiram Moshaiov | Eliran Farhi and Amiram Moshaiov | Window-of-interest based Multi-objective Evolutionary Search for
Satisficing Concepts | To be published in the proceedings of the IEEE SMC 2017 Conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The set-based concept approach has been suggested as a means to
simultaneously explore different design concepts, which are meaningful sub-sets
of the entire set of solutions. Previous efforts concerning the suggested
approach focused on either revealing the global front (s-Pareto front), of all
the concepts, or on finding the concepts' fronts, within a relaxation zone. In
contrast, here the aim is to reveal which of the concepts have at least one
solution with a performance vector within a pre-defined window-of-interest
(WOI). This paper provides the rational for this new concept-based exploration
problem, and suggests a WOI-based rather than Pareto-based multi-objective
evolutionary algorithm. The proposed algorithm, which simultaneously explores
different concepts, is tested using a recently suggested concept-based
benchmarking approach. The numerical study of this paper shows that the
algorithm can cope with various numerical difficulties in a simultaneous way,
which outperforms a sequential exploration approach.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2017 12:14:18 GMT"
}
] | 1,499,212,800,000 | [
[
"Farhi",
"Eliran",
""
],
[
"Moshaiov",
"Amiram",
""
]
] |
1707.01067 | Yuandong Tian | Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu and C. Lawrence
Zitnick | ELF: An Extensive, Lightweight and Flexible Research Platform for
Real-time Strategy Games | NIPS 2017 oral | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose ELF, an Extensive, Lightweight and Flexible
platform for fundamental reinforcement learning research. Using ELF, we
implement a highly customizable real-time strategy (RTS) engine with three game
environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a
miniature version of StarCraft, captures key game dynamics and runs at 40K
frame-per-second (FPS) per core on a Macbook Pro notebook. When coupled with
modern reinforcement learning methods, the system can train a full-game bot
against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition,
our platform is flexible in terms of environment-agent communication
topologies, choices of RL methods, changes in game parameters, and can host
existing C/C++-based game environments like Arcade Learning Environment. Using
ELF, we thoroughly explore training parameters and show that a network with
Leaky ReLU and Batch Normalization coupled with long-horizon training and
progressive curriculum beats the rule-based built-in AI more than $70\%$ of the
time in the full game of Mini-RTS. Strong performance is also achieved on the
other two games. In game replays, we show our agents learn interesting
strategies. ELF, along with its RL platform, is open-sourced at
https://github.com/facebookresearch/ELF.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2017 16:48:56 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Nov 2017 06:21:02 GMT"
}
] | 1,510,531,200,000 | [
[
"Tian",
"Yuandong",
""
],
[
"Gong",
"Qucheng",
""
],
[
"Shang",
"Wenling",
""
],
[
"Wu",
"Yuxin",
""
],
[
"Zitnick",
"C. Lawrence",
""
]
] |
1707.01154 | Himabindu Lakkaraju | Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec | Interpretable & Explorable Approximations of Black Box Models | Presented as a poster at the 2017 Workshop on Fairness,
Accountability, and Transparency in Machine Learning | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose Black Box Explanations through Transparent Approximations (BETA),
a novel model agnostic framework for explaining the behavior of any black-box
classifier by simultaneously optimizing for fidelity to the original model and
interpretability of the explanation. To this end, we develop a novel objective
function which allows us to learn (with optimality guarantees), a small number
of compact decision sets each of which explains the behavior of the black box
model in unambiguous, well-defined regions of feature space. Furthermore, our
framework also is capable of accepting user input when generating these
approximations, thus allowing users to interactively explore how the black-box
model behaves in different subspaces that are of interest to the user. To the
best of our knowledge, this is the first approach which can produce global
explanations of the behavior of any given black box model through joint
optimization of unambiguity, fidelity, and interpretability, while also
allowing users to explore model behavior based on their preferences.
Experimental evaluation with real-world datasets and user studies demonstrates
that our approach can generate highly compact, easy-to-understand, yet accurate
approximations of various kinds of predictive models compared to
state-of-the-art baselines.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2017 21:10:40 GMT"
}
] | 1,499,299,200,000 | [
[
"Lakkaraju",
"Himabindu",
""
],
[
"Kamar",
"Ece",
""
],
[
"Caruana",
"Rich",
""
],
[
"Leskovec",
"Jure",
""
]
] |
1707.01283 | Ruichu Cai | Ruichu Cai, Zhenjie Zhang, Zhifeng Hao | SADA: A General Framework to Support Robust Causation Discovery with
Theoretical Guarantee | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causation discovery without manipulation is considered a crucial problem to a
variety of applications. The state-of-the-art solutions are applicable only
when large numbers of samples are available or the problem domain is
sufficiently small. Motivated by the observations of the local sparsity
properties on causal structures, we propose a general Split-and-Merge
framework, named SADA, to enhance the scalability of a wide class of causation
discovery algorithms. In SADA, the variables are partitioned into subsets, by
finding causal cut on the sparse causal structure over the variables. By
running mainstream causation discovery algorithms as basic causal solvers on
the subproblems, complete causal structure can be reconstructed by combining
the partial results. SADA benefits from the recursive division technique, since
each small subproblem generates more accurate result under the same number of
samples. We theoretically prove that SADA always reduces the scales of problems
without sacrifice on accuracy, under the condition of local causal sparsity and
reliable conditional independence tests. We also present sufficient condition
to accuracy enhancement by SADA, even when the conditional independence tests
are vulnerable. Extensive experiments on both simulated and real-world datasets
verify the improvements on scalability and accuracy by applying SADA together
with existing causation discovery algorithms.
| [
{
"version": "v1",
"created": "Wed, 5 Jul 2017 09:37:00 GMT"
}
] | 1,499,299,200,000 | [
[
"Cai",
"Ruichu",
""
],
[
"Zhang",
"Zhenjie",
""
],
[
"Hao",
"Zhifeng",
""
]
] |
1707.01310 | Haifeng Zhang | Haifeng Zhang, Jun Wang, Zhiming Zhou, Weinan Zhang, Ying Wen, Yong
Yu, Wenxin Li | Learning to Design Games: Strategic Environments in Reinforcement
Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In typical reinforcement learning (RL), the environment is assumed given and
the goal of the learning is to identify an optimal policy for the agent taking
actions through its interactions with the environment. In this paper, we extend
this setting by considering the environment is not given, but controllable and
learnable through its interaction with the agent at the same time. This
extension is motivated by environment design scenarios in the real-world,
including game design, shopping space design and traffic signal design.
Theoretically, we find a dual Markov decision process (MDP) w.r.t. the
environment to that w.r.t. the agent, and derive a policy gradient solution to
optimizing the parametrized environment. Furthermore, discontinuous
environments are addressed by a proposed general generative framework. Our
experiments on a Maze game design task show the effectiveness of the proposed
algorithms in generating diverse and challenging Mazes against various agent
settings.
| [
{
"version": "v1",
"created": "Wed, 5 Jul 2017 10:45:43 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Sep 2017 15:58:40 GMT"
},
{
"version": "v3",
"created": "Thu, 12 Oct 2017 08:41:39 GMT"
},
{
"version": "v4",
"created": "Wed, 23 May 2018 08:56:12 GMT"
},
{
"version": "v5",
"created": "Wed, 23 Oct 2019 18:03:48 GMT"
}
] | 1,571,961,600,000 | [
[
"Zhang",
"Haifeng",
""
],
[
"Wang",
"Jun",
""
],
[
"Zhou",
"Zhiming",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Wen",
"Ying",
""
],
[
"Yu",
"Yong",
""
],
[
"Li",
"Wenxin",
""
]
] |
1707.01415 | Paul Rozdeba | Henry Abarbanel, Paul Rozdeba, Sasha Shirman | Machine Learning, Deepest Learning: Statistical Data Assimilation
Problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formulate a strong equivalence between machine learning, artificial
intelligence methods and the formulation of statistical data assimilation as
used widely in physical and biological sciences. The correspondence is that
layer number in the artificial network setting is the analog of time in the
data assimilation setting. Within the discussion of this equivalence we show
that adding more layers (making the network deeper) is analogous to adding
temporal resolution in a data assimilation framework.
How one can find a candidate for the global minimum of the cost functions in
the machine learning context using a method from data assimilation is
discussed. Calculations on simple models from each side of the equivalence are
reported.
Also discussed is a framework in which the time or layer label is taken to be
continuous, providing a differential equation, the Euler-Lagrange equation,
which shows that the problem being solved is a two point boundary value problem
familiar in the discussion of variational methods. The use of continuous layers
is denoted "deepest learning". These problems respect a symplectic symmetry in
continuous time/layer phase space. Both Lagrangian versions and Hamiltonian
versions of these problems are presented. Their well-studied implementation in
a discrete time/layer, while respected the symplectic structure, is addressed.
The Hamiltonian version provides a direct rationale for back propagation as a
solution method for the canonical momentum.
| [
{
"version": "v1",
"created": "Wed, 5 Jul 2017 14:23:00 GMT"
}
] | 1,499,299,200,000 | [
[
"Abarbanel",
"Henry",
""
],
[
"Rozdeba",
"Paul",
""
],
[
"Shirman",
"Sasha",
""
]
] |
1707.01423 | Mario Alviano | Mario Alviano | Model enumeration in propositional circumscription via unsatisfiable
core analysis | 15 pages, 2 algorithms, 2 tables, 2 figures, ICLP | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many practical problems are characterized by a preference relation over
admissible solutions, where preferred solutions are minimal in some sense. For
example, a preferred diagnosis usually comprises a minimal set of reasons that
is sufficient to cause the observed anomaly. Alternatively, a minimal
correction subset comprises a minimal set of reasons whose deletion is
sufficient to eliminate the observed anomaly. Circumscription formalizes such
preference relations by associating propositional theories with minimal models.
The resulting enumeration problem is addressed here by means of a new algorithm
taking advantage of unsatisfiable core analysis. Empirical evidence of the
efficiency of the algorithm is given by comparing the performance of the
resulting solver, CIRCUMSCRIPTINO, with HCLASP, CAMUS MCS, LBX and MCSLS on the
enumeration of minimal models for problems originating from practical
applications.
This paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Wed, 5 Jul 2017 14:39:00 GMT"
}
] | 1,499,299,200,000 | [
[
"Alviano",
"Mario",
""
]
] |
1707.01727 | Mehrdad J. Bani | Shoele Jamali and Mehrdad J. Bani | Application of Fuzzy Assessing for Reliability Decision Making | Submitted to Proceedings of the World Congress on Engineering and
Computer Science 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a new fuzzy assessing procedure with application in
management decision making. The proposed fuzzy approach build the membership
functions for system characteristics of a standby repairable system. This
method is used to extract a family of conventional crisp intervals from the
fuzzy repairable system for the desired system characteristics. This can be
determined with a set of nonlinear parametric programing using the membership
functions. When system characteristics are governed by the membership
functions, more information is provided for use by management, and because the
redundant system is extended to the fuzzy environment, general repairable
systems are represented more accurately and the analytic results are more
useful for designers and practitioners. Also beside standby, active redundancy
systems are used in many cases so this article has many practical instances.
Different from other studies, our model provides, a good estimated value based
on uncertain environments, a comparison discussion of using fuzzy theory and
conventional method and also a comparison between parallel (active redundancy)
and series system in fuzzy world when we have standby redundancy. When the
membership function intervals cannot be inverted explicitly, system management
or designers can specify the system characteristics of interest, perform
numerical calculations, examine the corresponding {\alpha}-cuts, and use this
information to develop or improve system processes.
| [
{
"version": "v1",
"created": "Thu, 6 Jul 2017 10:47:08 GMT"
}
] | 1,499,385,600,000 | [
[
"Jamali",
"Shoele",
""
],
[
"Bani",
"Mehrdad J.",
""
]
] |
1707.01891 | Ofir Nachum | Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans | Trust-PCL: An Off-Policy Trust Region Method for Continuous Control | ICLR 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Trust region methods, such as TRPO, are often used to stabilize policy
optimization algorithms in reinforcement learning (RL). While current trust
region strategies are effective for continuous control, they typically require
a prohibitively large amount of on-policy interaction with the environment. To
address this problem, we propose an off-policy trust region method, Trust-PCL.
The algorithm is the result of observing that the optimal policy and state
values of a maximum reward objective with a relative-entropy regularizer
satisfy a set of multi-step pathwise consistencies along any path. Thus,
Trust-PCL is able to maintain optimization stability while exploiting
off-policy data to improve sample efficiency. When evaluated on a number of
continuous control tasks, Trust-PCL improves the solution quality and sample
efficiency of TRPO.
| [
{
"version": "v1",
"created": "Thu, 6 Jul 2017 17:50:19 GMT"
},
{
"version": "v2",
"created": "Thu, 12 Oct 2017 16:16:27 GMT"
},
{
"version": "v3",
"created": "Thu, 22 Feb 2018 21:28:57 GMT"
}
] | 1,519,603,200,000 | [
[
"Nachum",
"Ofir",
""
],
[
"Norouzi",
"Mohammad",
""
],
[
"Xu",
"Kelvin",
""
],
[
"Schuurmans",
"Dale",
""
]
] |
1707.01959 | Jianmin Ji | Jianmin Ji, Fangfang Liu, Jia-Huai You | Well-Founded Operators for Normal Hybrid MKNF Knowledge Bases | Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1,
2017. Total 20 pages, Main part 16 pages, LaTeX | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hybrid MKNF knowledge bases have been considered one of the dominant
approaches to combining open world ontology languages with closed world
rule-based languages. Currently, the only known inference methods are based on
the approach of guess-and-verify, while most modern SAT/ASP solvers are built
under the DPLL architecture. The central impediment here is that it is not
clear what constitutes a constraint propagator, a key component employed in any
DPLL-based solver. In this paper, we address this problem by formulating the
notion of unfounded sets for nondisjunctive hybrid MKNF knowledge bases, based
on which we propose and study two new well-founded operators. We show that by
employing a well-founded operator as a constraint propagator, a sound and
complete DPLL search engine can be readily defined. We compare our approach
with the operator based on the alternating fixpoint construction by Knorr et al
[2011] and show that, when applied to arbitrary partial partitions, the new
well-founded operators not only propagate more truth values but also circumvent
the non-converging behavior of the latter. In addition, we study the
possibility of simplifying a given hybrid MKNF knowledge base by employing a
well-founded operator, and show that, out of the two operators proposed in this
paper, the weaker one can be applied for this purpose and the stronger one
cannot. These observations are useful in implementing a grounder for hybrid
MKNF knowledge bases, which can be applied before the computation of MKNF
models.
The paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Thu, 6 Jul 2017 20:38:35 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jul 2017 22:50:14 GMT"
}
] | 1,499,990,400,000 | [
[
"Ji",
"Jianmin",
""
],
[
"Liu",
"Fangfang",
""
],
[
"You",
"Jia-Huai",
""
]
] |
1707.02286 | Nicolas Heess | Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel,
Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin
Riedmiller, David Silver | Emergence of Locomotion Behaviours in Rich Environments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reinforcement learning paradigm allows, in principle, for complex
behaviours to be learned directly from simple reward signals. In practice,
however, it is common to carefully hand-design the reward function to encourage
a particular solution, or to derive it from demonstration data. In this paper
explore how a rich environment can help to promote the learning of complex
behavior. Specifically, we train agents in diverse environmental contexts, and
find that this encourages the emergence of robust behaviours that perform well
across a suite of tasks. We demonstrate this principle for locomotion --
behaviours that are known for their sensitivity to the choice of reward. We
train several simulated bodies on a diverse set of challenging terrains and
obstacles, using a simple reward function based on forward progress. Using a
novel scalable variant of policy gradient reinforcement learning, our agents
learn to run, jump, crouch and turn as required by the environment without
explicit reward-based guidance. A visual depiction of highlights of the learned
behavior can be viewed following https://youtu.be/hx_bgoTF7bs .
| [
{
"version": "v1",
"created": "Fri, 7 Jul 2017 17:56:57 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Jul 2017 18:52:12 GMT"
}
] | 1,499,817,600,000 | [
[
"Heess",
"Nicolas",
""
],
[
"TB",
"Dhruva",
""
],
[
"Sriram",
"Srinivasan",
""
],
[
"Lemmon",
"Jay",
""
],
[
"Merel",
"Josh",
""
],
[
"Wayne",
"Greg",
""
],
[
"Tassa",
"Yuval",
""
],
[
"Erez",
"Tom",
""
],
[
"Wang",
"Ziyu",
""
],
[
"Eslami",
"S. M. Ali",
""
],
[
"Riedmiller",
"Martin",
""
],
[
"Silver",
"David",
""
]
] |
1707.02292 | Lucas Bechberger | Lucas Bechberger and Kai-Uwe K\"uhnberger | Measuring Relations Between Concepts In Conceptual Spaces | Accepted at SGAI 2017 (http://www.bcs-sgai.org/ai2017/). The final
publication is available at Springer via
https://doi.org/10.1007/978-3-319-71078-5_7. arXiv admin note: substantial
text overlap with arXiv:1707.05165, arXiv:1706.06366 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
Our recent mathematical formalization of this framework is capable of
representing correlations between different domains in a geometric way. In this
paper, we extend our formalization by providing quantitative mathematical
definitions for the notions of concept size, subsethood, implication,
similarity, and betweenness. This considerably increases the representational
power of our formalization by introducing measurable ways of describing
relations between concepts.
| [
{
"version": "v1",
"created": "Fri, 7 Jul 2017 09:01:00 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Dec 2017 13:57:33 GMT"
}
] | 1,512,691,200,000 | [
[
"Bechberger",
"Lucas",
""
],
[
"Kühnberger",
"Kai-Uwe",
""
]
] |
1707.03069 | Arthur Van Camp | Arthur Van Camp, Gert de Cooman, Enrique Miranda | Lexicographic choice functions | 27 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate a generalisation of the coherent choice functions considered
by Seidenfeld et al. (2010), by sticking to the convexity axiom but imposing no
Archimedeanity condition. We define our choice functions on vector spaces of
options, which allows us to incorporate as special cases both Seidenfeld et
al.'s (2010) choice functions on horse lotteries and sets of desirable gambles
(Quaeghebeur, 2014), and to investigate their connections. We show that choice
functions based on sets of desirable options (gambles) satisfy Seidenfeld's
convexity axiom only for very particular types of sets of desirable options,
which are in a one-to-one relationship with the lexicographic probabilities. We
call them lexicographic choice functions. Finally, we prove that these choice
functions can be used to determine the most conservative convex choice function
associated with a given binary relation.
| [
{
"version": "v1",
"created": "Mon, 10 Jul 2017 21:39:03 GMT"
}
] | 1,499,817,600,000 | [
[
"Van Camp",
"Arthur",
""
],
[
"de Cooman",
"Gert",
""
],
[
"Miranda",
"Enrique",
""
]
] |
1707.03098 | Ole-Christoffer Granmo | Sondre Glimsdal and Ole-Christoffer Granmo | An Optimal Bayesian Network Based Solution Scheme for the Constrained
Stochastic On-line Equi-Partitioning Problem | 15 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A number of intriguing decision scenarios revolve around partitioning a
collection of objects to optimize some application specific objective function.
This problem is generally referred to as the Object Partitioning Problem (OPP)
and is known to be NP-hard. We here consider a particularly challenging version
of OPP, namely, the Stochastic On-line Equi-Partitioning Problem (SO-EPP). In
SO-EPP, the target partitioning is unknown and has to be inferred purely from
observing an on-line sequence of object pairs. The paired objects belong to the
same partition with probability $p$ and to different partitions with
probability $1-p$, with $p$ also being unknown. As an additional complication,
the partitions are required to be of equal cardinality. Previously, only
sub-optimal solution strategies have been proposed for SO- EPP. In this paper,
we propose the first optimal solution strategy. In brief, the scheme that we
propose, BN-EPP, is founded on a Bayesian network representation of SO-EPP
problems. Based on probabilistic reasoning, we are not only able to infer the
underlying object partitioning with optimal accuracy. We are also able to
simultaneously infer $p$, allowing us to accelerate learning as object pairs
arrive. Furthermore, our scheme is the first to support arbitrary constraints
on the partitioning (Constrained SO-EPP). Being optimal, BN-EPP provides
superior performance compared to existing solution schemes. We additionally
introduce Walk-BN-EPP, a novel WalkSAT inspired algorithm for solving large
scale BN-EPP problems. Finally, we provide a BN-EPP based solution to the
problem of order picking, a representative real-life application of BN-EPP.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2017 01:48:47 GMT"
}
] | 1,499,817,600,000 | [
[
"Glimsdal",
"Sondre",
""
],
[
"Granmo",
"Ole-Christoffer",
""
]
] |
1707.03232 | Douglas Summers Stay | Douglas Summers-Stay | Deductive and Analogical Reasoning on a Semantically Embedded Knowledge
Graph | AGI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Representing knowledge as high-dimensional vectors in a continuous semantic
vector space can help overcome the brittleness and incompleteness of
traditional knowledge bases. We present a method for performing deductive
reasoning directly in such a vector space, combining analogy, association, and
deduction in a straightforward way at each step in a chain of reasoning,
drawing on knowledge from diverse sources and ontologies.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2017 11:49:52 GMT"
}
] | 1,499,817,600,000 | [
[
"Summers-Stay",
"Douglas",
""
]
] |
1707.03300 | Serkan Cabi | Serkan Cabi, Sergio G\'omez Colmenarejo, Matthew W. Hoffman, Misha
Denil, Ziyu Wang, Nando de Freitas | The Intentional Unintentional Agent: Learning to Solve Many Continuous
Control Tasks Simultaneously | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the Intentional Unintentional (IU) agent. This agent
endows the deep deterministic policy gradients (DDPG) agent for continuous
control with the ability to solve several tasks simultaneously. Learning to
solve many tasks simultaneously has been a long-standing, core goal of
artificial intelligence, inspired by infant development and motivated by the
desire to build flexible robot manipulators capable of many diverse behaviours.
We show that the IU agent not only learns to solve many tasks simultaneously
but it also learns faster than agents that target a single task at-a-time. In
some cases, where the single task DDPG method completely fails, the IU agent
successfully solves the task. To demonstrate this, we build a playroom
environment using the MuJoCo physics engine, and introduce a grounded formal
language to automatically generate tasks.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2017 14:30:06 GMT"
}
] | 1,499,817,600,000 | [
[
"Cabi",
"Serkan",
""
],
[
"Colmenarejo",
"Sergio Gómez",
""
],
[
"Hoffman",
"Matthew W.",
""
],
[
"Denil",
"Misha",
""
],
[
"Wang",
"Ziyu",
""
],
[
"de Freitas",
"Nando",
""
]
] |
1707.03333 | Joseph Osborn | Joseph C Osborn, Adam Summerville and Michael Mateas | Automated Game Design Learning | 8 pages, 2 figures. Accepted for CIG 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2017 15:43:45 GMT"
}
] | 1,499,817,600,000 | [
[
"Osborn",
"Joseph C",
""
],
[
"Summerville",
"Adam",
""
],
[
"Mateas",
"Michael",
""
]
] |
1707.03336 | Joseph Osborn | Adam Summerville, Joseph Osborn, Michael Mateas | CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis | 7 pages, 2 figures. Accepted for IJCAI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose and evaluate a new technique for learning hybrid automata
automatically by observing the runtime behavior of a dynamical system. Working
from a sequence of continuous state values and predicates about the
environment, CHARDA recovers the distinct dynamic modes, learns a model for
each mode from a given set of templates, and postulates causal guard conditions
which trigger transitions between modes. Our main contribution is the use of
information-theoretic measures (1)~as a cost function for data segmentation and
model selection to penalize over-fitting and (2)~to determine the likely causes
of each transition. CHARDA is easily extended with different classes of model
templates, fitting methods, or predicates. In our experiments on a complex
videogame character, CHARDA successfully discovers a reasonable
over-approximation of the character's true behaviors. Our results also compare
favorably against recent work in automatically learning probabilistic timed
automata in an aircraft domain: CHARDA exactly learns the modes of these
simpler automata.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2017 15:50:09 GMT"
}
] | 1,499,817,600,000 | [
[
"Summerville",
"Adam",
""
],
[
"Osborn",
"Joseph",
""
],
[
"Mateas",
"Michael",
""
]
] |
1707.03471 | Suju Rajan | Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee,
Kun Liu, Vladan Radosavljevic and Suju Rajan | Proceedings of the 2017 AdKDD & TargetAd Workshop | Workshop Proceedings with links to the accepted papers | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with
the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax,
Nova Scotia, Canada.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2017 21:43:14 GMT"
}
] | 1,499,904,000,000 | [
[
"Bagherjeiran",
"Abraham",
""
],
[
"Djuric",
"Nemanja",
""
],
[
"Grbovic",
"Mihajlo",
""
],
[
"Lee",
"Kuang-Chih",
""
],
[
"Liu",
"Kun",
""
],
[
"Radosavljevic",
"Vladan",
""
],
[
"Rajan",
"Suju",
""
]
] |
1707.03743 | Niels Justesen | Niels Justesen and Sebastian Risi | Learning Macromanagement in StarCraft from Replays using Deep Learning | 8 pages, to appear in the proceedings of the IEEE Conference on
Computational Intelligence and Games (CIG 2017) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The real-time strategy game StarCraft has proven to be a challenging
environment for artificial intelligence techniques, and as a result, current
state-of-the-art solutions consist of numerous hand-crafted modules. In this
paper, we show how macromanagement decisions in StarCraft can be learned
directly from game replays using deep learning. Neural networks are trained on
789,571 state-action pairs extracted from 2,005 replays of highly skilled
players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting
the next build action. By integrating the trained network into UAlbertaBot, an
open source StarCraft bot, the system can significantly outperform the game's
built-in Terran bot, and play competitively against UAlbertaBot with a fixed
rush strategy. To our knowledge, this is the first time macromanagement tasks
are learned directly from replays in StarCraft. While the best hand-crafted
strategies are still the state-of-the-art, the deep network approach is able to
express a wide range of different strategies and thus improving the network's
performance further with deep reinforcement learning is an immediately
promising avenue for future research. Ultimately this approach could lead to
strong StarCraft bots that are less reliant on hard-coded strategies.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2017 14:40:00 GMT"
}
] | 1,499,904,000,000 | [
[
"Justesen",
"Niels",
""
],
[
"Risi",
"Sebastian",
""
]
] |
1707.03744 | Christian Oesch | Christian Oesch | P-Tree Programming | Submitted to IEEE SSCI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel method for automatic program synthesis. P-Tree Programming
represents the program search space through a single probabilistic prototype
tree. From this prototype tree we form program instances which we evaluate on a
given problem. The error values from the evaluations are propagated through the
prototype tree. We use them to update the probability distributions that
determine the symbol choices of further instances. The iterative method is
applied to several symbolic regression benchmarks from the literature. It
outperforms standard Genetic Programming to a large extend. Furthermore, it
relies on a concise set of parameters which are held constant for all problems.
The algorithm can be employed for most of the typical computational
intelligence tasks such as classification, automatic program induction, and
symbolic regression.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2017 14:40:06 GMT"
}
] | 1,499,904,000,000 | [
[
"Oesch",
"Christian",
""
]
] |
1707.03865 | Joseph Osborn | Adam Summerville, Joseph C. Osborn, Christoffer Holmg{\aa}rd, Daniel
W. Zhang | Mechanics Automatically Recognized via Interactive Observation: Jumping | 10 pages, 12 figures. Accepted at Foundations of Digital Games 2017 | null | 10.1145/3102071.3102104 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jumping has been an important mechanic since its introduction in Donkey Kong.
It has taken a variety of forms and shown up in numerous games, with each jump
having a different feel. In this paper, we use a modified Nintendo
Entertainment System (NES) emulator to semi-automatically run experiments on a
large subset (30%) of NES platform games. We use these experiments to build
models of jumps from different developers, series, and games across the history
of the console. We then examine these models to gain insights into different
forms of jumping and their associated feel.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2017 18:49:15 GMT"
}
] | 1,499,990,400,000 | [
[
"Summerville",
"Adam",
""
],
[
"Osborn",
"Joseph C.",
""
],
[
"Holmgård",
"Christoffer",
""
],
[
"Zhang",
"Daniel W.",
""
]
] |
1707.03872 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | Independence, Conditionality and Structure of Dempster-Shafer Belief
Functions | 1994 internal report | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several approaches of structuring (factorization, decomposition) of
Dempster-Shafer joint belief functions from literature are reviewed with
special emphasis on their capability to capture independence from the point of
view of the claim that belief functions generalize bayes notion of probability.
It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets'
{Smets:93} directed acyclic graphs are unable to capture statistical
dependence/independence of bayesian networks {Pearl:88}. On the other hand,
though Shenoy and Shafer's hypergraphs can explicitly represent bayesian
network factorization of bayesian belief functions, they disclaim any need for
representation of independence of variables in belief functions.
Cano et al. {Cano:93} reject the hypergraph representation of Shenoy and
Shafer just on grounds of missing representation of variable independence, but
in their frameworks some belief functions factorizable in Shenoy/Shafer
framework cannot be factored.
The approach in {Klopotek:93f} on the other hand combines the merits of both
Cano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach no
simpler factorization than that in {Klopotek:93f} approach exists and on the
other hand all independences among variables captured in Cano et al. framework
and many more are captured in {Klopotek:93f} approach.%
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2017 19:06:35 GMT"
}
] | 1,499,990,400,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
1707.03881 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | Identification and Interpretation of Belief Structure in Dempster-Shafer
Theory | An internal report 1994 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical Theory of Evidence called also Dempster-Shafer Theory (DST) is
known as a foundation for reasoning when knowledge is expressed at various
levels of detail. Though much research effort has been committed to this theory
since its foundation, many questions remain open. One of the most important
open questions seems to be the relationship between frequencies and the
Mathematical Theory of Evidence. The theory is blamed to leave frequencies
outside (or aside of) its framework. The seriousness of this accusation is
obvious: (1) no experiment may be run to compare the performance of DST-based
models of real world processes against real world data, (2) data may not serve
as foundation for construction of an appropriate belief model.
In this paper we develop a frequentist interpretation of the DST bringing to
fall the above argument against DST. An immediate consequence of it is the
possibility to develop algorithms acquiring automatically DST belief models
from data. We propose three such algorithms for various classes of belief model
structures: for tree structured belief networks, for poly-tree belief networks
and for general type belief networks.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2017 19:24:26 GMT"
}
] | 1,499,990,400,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
1707.03886 | Amit Dhurandhar | Amit Dhurandhar, Vijay Iyengar, Ronny Luss and Karthikeyan Shanmugam | A Formal Framework to Characterize Interpretability of Procedures | presented at 2017 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2017), Sydney, NSW, Australia | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide a novel notion of what it means to be interpretable, looking past
the usual association with human understanding. Our key insight is that
interpretability is not an absolute concept and so we define it relative to a
target model, which may or may not be a human. We define a framework that
allows for comparing interpretable procedures by linking it to important
practical aspects such as accuracy and robustness. We characterize many of the
current state-of-the-art interpretable methods in our framework portraying its
general applicability.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2017 19:42:08 GMT"
}
] | 1,499,990,400,000 | [
[
"Dhurandhar",
"Amit",
""
],
[
"Iyengar",
"Vijay",
""
],
[
"Luss",
"Ronny",
""
],
[
"Shanmugam",
"Karthikeyan",
""
]
] |
1707.03908 | Joseph Osborn | Joseph C. Osborn and Adam Summerville and Michael Mateas | Automatic Mapping of NES Games with Mappy | 9 pages, 7 figures. Appearing at Procedural Content Generation
Workshop 2017 | null | 10.1145/3102071.3110576 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to be mapped. The results can still be hard for humans or
computational systems to make use of, privileging visual appearance over
semantic information. We describe a software system, Mappy, that produces a
good approximation of a linked map of rooms given a Nintendo Entertainment
System game program and a sequence of button inputs exploring its world. In
addition to visual maps, Mappy outputs grids of tiles (and how they change over
time), positions of non-tile objects, clusters of similar rooms that might in
fact be the same room, and a set of links between these rooms. We believe this
is a necessary step towards developing larger corpora of high-quality
semantically-annotated maps for PCG via machine learning and other
applications.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2017 21:02:19 GMT"
}
] | 1,499,990,400,000 | [
[
"Osborn",
"Joseph C.",
""
],
[
"Summerville",
"Adam",
""
],
[
"Mateas",
"Michael",
""
]
] |
1707.04016 | Jerry Swan | Zoltan A. Kocsis and Jerry Swan | Dependency Injection for Programming by Optimization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Programming by Optimization tools perform automatic software configuration
according to the specification supplied by a software developer. Developers
specify design spaces for program components, and the onerous task of
determining which configuration best suits a given use case is determined using
automated analysis tools and optimization heuristics. However, in current
approaches to Programming by Optimization, design space specification and
exploration relies on external configuration algorithms, executable wrappers
and fragile, preprocessed programming language extensions.
Here we show that the architectural pattern of Dependency Injection provides
a superior alternative to the traditional Programming by Optimization pipeline.
We demonstrate that configuration tools based on Dependency Injection fit
naturally into the software development process, while requiring less overhead
than current wrapper-based mechanisms. Furthermore, the structural
correspondence between Dependency Injection and context-free grammars yields a
new class of evolutionary metaheuristics for automated algorithm configuration.
We found that the new heuristics significantly outperform existing
configuration algorithms on many problems of interest (in one case by two
orders of magnitude). We anticipate that these developments will make
Programming by Optimization immediately applicable to a large number of
enterprise software projects.
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2017 08:02:23 GMT"
}
] | 1,499,990,400,000 | [
[
"Kocsis",
"Zoltan A.",
""
],
[
"Swan",
"Jerry",
""
]
] |
1707.04027 | Peter Sch\"uller | Bernardo Cuteri, Carmine Dodaro, Francesco Ricca, Peter Sch\"uller | Constraints, Lazy Constraints, or Propagators in ASP Solving: An
Empirical Analysis | Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1,
2017. 16 pages | Theory and Practice of Logic Programming 17 (5-6), pages 780-799,
2017 | 10.1017/S1471068417000254 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Answer Set Programming (ASP) is a well-established declarative paradigm. One
of the successes of ASP is the availability of efficient systems.
State-of-the-art systems are based on the ground+solve approach. In some
applications this approach is infeasible because the grounding of one or few
constraints is expensive. In this paper, we systematically compare alternative
strategies to avoid the instantiation of problematic constraints, that are
based on custom extensions of the solver. Results on real and synthetic
benchmarks highlight some strengths and weaknesses of the different strategies.
(Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2017 08:41:30 GMT"
}
] | 1,517,443,200,000 | [
[
"Cuteri",
"Bernardo",
""
],
[
"Dodaro",
"Carmine",
""
],
[
"Ricca",
"Francesco",
""
],
[
"Schüller",
"Peter",
""
]
] |
1707.04053 | Torsten Schaub | Tomi Janhunen and Roland Kaminski and Max Ostrowski and Torsten Schaub
and Sebastian Schellhorn and Philipp Wanko | Clingo goes Linear Constraints over Reals and Integers | Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017
16 pages, LaTeX | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent series 5 of the ASP system clingo provides generic means to
enhance basic Answer Set Programming (ASP) with theory reasoning capabilities.
We instantiate this framework with different forms of linear constraints,
discuss the respective implementations, and present techniques of how to use
these constraints in a reactive context. More precisely, we introduce
extensions to clingo with difference and linear constraints over integers and
reals, respectively, and realize them in complementary ways. Finally, we
empirically evaluate the resulting clingo derivatives clingo[dl] and clingo[lp]
on common fragments and contrast them to related ASP systems.
This paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2017 10:18:12 GMT"
}
] | 1,499,990,400,000 | [
[
"Janhunen",
"Tomi",
""
],
[
"Kaminski",
"Roland",
""
],
[
"Ostrowski",
"Max",
""
],
[
"Schaub",
"Torsten",
""
],
[
"Schellhorn",
"Sebastian",
""
],
[
"Wanko",
"Philipp",
""
]
] |
1707.04277 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | On (Anti)Conditional Independence in Dempster-Shafer Theory | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper verifies a result of {Shenoy:94} concerning graphoidal structure
of Shenoy's notion of independence for Dempster-Shafer theory of belief
functions. Shenoy proved that his notion of independence has graphoidal
properties for positive normal valuations.
The requirement of strict positive normal valuations as prerequisite for
application of graphoidal properties excludes a wide class of DS belief
functions. It excludes especially so-called probabilistic belief functions. It
is demonstrated that the requirement of positiveness of valuation may be
weakened in that it may be required that commonality function is non-zero for
singleton sets instead, and the graphoidal properties for independence of
belief function variables are then preserved. This means especially that
probabilistic belief functions with all singleton sets as focal points possess
graphoidal properties for independence.
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2017 18:33:34 GMT"
}
] | 1,500,249,600,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
1707.04352 | Vasant Honavar | Gregory D. Hager, Randal Bryant, Eric Horvitz, Maja Mataric, and
Vasant Honavar | Advances in Artificial Intelligence Require Progress Across all of
Computer Science | 7 pages, Computing Community Consortium White Paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advances in Artificial Intelligence require progress across all of computer
science.
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2017 23:11:18 GMT"
}
] | 1,500,249,600,000 | [
[
"Hager",
"Gregory D.",
""
],
[
"Bryant",
"Randal",
""
],
[
"Horvitz",
"Eric",
""
],
[
"Mataric",
"Maja",
""
],
[
"Honavar",
"Vasant",
""
]
] |
1707.04506 | Hossein Sangrody | Ahmad Shokrollahi, Hossein Sangrody, Mahdi Motalleb, Mandana
Rezaeiahari, Elham Foruzan, Fattah Hassanzadeh | Reliability Assessment of Distribution System Using Fuzzy Logic for
Modelling of Transformer and Line Uncertainties | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reliability assessment of distribution system, based on historical data and
probabilistic methods, leads to an unreliable estimation of reliability indices
since the data for the distribution components are usually inaccurate or
unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in
reliability inputs. In this paper, the ENS index along with other commonly used
indices in reliability assessment are evaluated for the distribution system
using fuzzy logic. Accordingly, the influential variables on the failure rate
and outage duration time of the distribution components, which are natural or
human-made, are explained using proposed fuzzy membership functions. The
reliability indices are calculated and compared for different cases of the
system operations by simulation on the IEEE RBTS Bus 2. The results of
simulation show how utilities can significantly improve the reliability of
their distribution system by considering the risk of the influential variables.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2017 18:39:37 GMT"
}
] | 1,500,249,600,000 | [
[
"Shokrollahi",
"Ahmad",
""
],
[
"Sangrody",
"Hossein",
""
],
[
"Motalleb",
"Mahdi",
""
],
[
"Rezaeiahari",
"Mandana",
""
],
[
"Foruzan",
"Elham",
""
],
[
"Hassanzadeh",
"Fattah",
""
]
] |
1707.04584 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | Fast Restricted Causal Inference | 1995 internal report. arXiv admin note: substantial text overlap with
arXiv:1705.10308, arXiv:1706.10117; text overlap with arXiv:1707.03881 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hidden variables are well known sources of disturbance when recovering belief
networks from data based only on measurable variables. Hence models assuming
existence of hidden variables are under development.
This paper presents a new algorithm "accelerating" the known CI algorithm of
Spirtes, Glymour and Scheines {Spirtes:93}. We prove that this algorithm does
not produces (conditional) independencies not present in the data if
statistical independence test is reliable.
This result is to be considered as non-trivial since e.g. the same claim
fails to be true for FCI algorithm, another "accelerator" of CI, developed in
{Spirtes:93}.
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2017 18:11:40 GMT"
}
] | 1,500,336,000,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
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