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1909.00442 | Alexander Dockhorn | Alexander Dockhorn, Simon M. Lucas, Vanessa Volz, Ivan Bravi, Raluca
D. Gaina, Diego Perez-Liebana | Learning Local Forward Models on Unforgiving Games | 4 pages, 3 figures, 3 tables, accepted at IEEE COG 2019 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper examines learning approaches for forward models based on local
cell transition functions. We provide a formal definition of local forward
models for which we propose two basic learning approaches. Our analysis is
based on the game Sokoban, where a wrong action can lead to an unsolvable game
state. Therefore, an accurate prediction of an action's resulting state is
necessary to avoid this scenario.
In contrast to learning the complete state transition function, local forward
models allow extracting multiple training examples from a single state
transition. In this way, the Hash Set model, as well as the Decision Tree
model, quickly learn to predict upcoming state transitions of both the training
and the test set. Applying the model using a statistical forward planner showed
that the best models can be used to satisfying degree even in cases in which
the test levels have not yet been seen.
Our evaluation includes an analysis of various local neighbourhood patterns
and sizes to test the learners' capabilities in case too few or too many
attributes are extracted, of which the latter has shown do degrade the
performance of the model learner.
| [
{
"version": "v1",
"created": "Sun, 1 Sep 2019 18:25:14 GMT"
}
] | 1,567,555,200,000 | [
[
"Dockhorn",
"Alexander",
""
],
[
"Lucas",
"Simon M.",
""
],
[
"Volz",
"Vanessa",
""
],
[
"Bravi",
"Ivan",
""
],
[
"Gaina",
"Raluca D.",
""
],
[
"Perez-Liebana",
"Diego",
""
]
] |
1909.00621 | Sarah Alice Gaggl | Sarah A. Gaggl and Thomas Linsbichler and Marco Maratea and Stefan
Woltran | Design and Results of the Second International Competition on
Computational Models of Argumentation | submitted to Artificial Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Argumentation is a major topic in the study of Artificial Intelligence. Since
the first edition in 2015, advancements in solving (abstract) argumentation
frameworks are assessed in competition events, similar to other closely related
problem solving technologies. In this paper, we report about the design and
results of the Second International Competition on Computational Models of
Argumentation, which has been jointly organized by TU Dresden (Germany), TU
Wien (Austria), and the University of Genova (Italy), in affiliation with the
2017 International Workshop on Theory and Applications of Formal Argumentation.
This second edition maintains some of the design choices made in the first
event, e.g. the I/O formats, the basic reasoning problems, and the organization
into tasks and tracks. At the same time, it introduces significant novelties,
e.g. three additional prominent semantics, and an instance selection stage for
classifying instances according to their empirical hardness.
| [
{
"version": "v1",
"created": "Mon, 2 Sep 2019 09:23:48 GMT"
}
] | 1,567,555,200,000 | [
[
"Gaggl",
"Sarah A.",
""
],
[
"Linsbichler",
"Thomas",
""
],
[
"Maratea",
"Marco",
""
],
[
"Woltran",
"Stefan",
""
]
] |
1909.00690 | Maria Maleshkova | Sebastian R. Bader and Maria Maleshkova | The Semantic Asset Administration Shell | 15 pages, pre-print of Semantics 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The disruptive potential of the upcoming digital transformations for the
industrial manufacturing domain have led to several reference frameworks and
numerous standardization approaches. On the other hand, the Semantic Web
community has made significant contributions in the field, for instance on data
and service description, integration of heterogeneous sources and devices, and
AI techniques in distributed systems. These two streams of work are, however,
mostly unrelated and only briefly regard each others requirements, practices
and terminology. We contribute to closing this gap by providing the Semantic
Asset Administration Shell, an RDF-based representation of the Industrie 4.0
Component. We provide an ontology for the latest data model specification,
created a RML mapping, supply resources to validate the RDF entities and
introduce basic reasoning on the Asset Administration Shell data model.
Furthermore, we discuss the different assumptions and presentation patterns,
and analyze the implications of a semantic representation on the original data.
We evaluate the thereby created overheads, and conclude that the semantic
lifting is manageable, also for restricted or embedded devices, and therefore
meets the needs of Industrie 4.0 scenarios.
| [
{
"version": "v1",
"created": "Mon, 2 Sep 2019 12:38:58 GMT"
}
] | 1,567,555,200,000 | [
[
"Bader",
"Sebastian R.",
""
],
[
"Maleshkova",
"Maria",
""
]
] |
1909.01128 | Tomi Janhunen | Tomi Janhunen, Michael Sioutis | Allen's Interval Algebra Makes the Difference | Part of DECLARE 19 proceedings | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Allen's Interval Algebra constitutes a framework for reasoning about temporal
information in a qualitative manner. In particular, it uses intervals, i.e.,
pairs of endpoints, on the timeline to represent entities corresponding to
actions, events, or tasks, and binary relations such as precedes and overlaps
to encode the possible configurations between those entities. Allen's calculus
has found its way in many academic and industrial applications that involve,
most commonly, planning and scheduling, temporal databases, and healthcare. In
this paper, we present a novel encoding of Interval Algebra using answer-set
programming (ASP) extended by difference constraints, i.e., the fragment
abbreviated as ASP(DL), and demonstrate its performance via a preliminary
experimental evaluation. Although our ASP encoding is presented in the case of
Allen's calculus for the sake of clarity, we suggest that analogous encodings
can be devised for other point-based calculi, too.
| [
{
"version": "v1",
"created": "Tue, 3 Sep 2019 12:56:15 GMT"
}
] | 1,567,555,200,000 | [
[
"Janhunen",
"Tomi",
""
],
[
"Sioutis",
"Michael",
""
]
] |
1909.01645 | Antonio Lieto | Antonio Lieto | Heterogeneous Proxytypes Extended: Integrating Theory-like
Representations and Mechanisms with Prototypes and Exemplars | 10 pages, 1 figure, BICA Conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper introduces an extension of the proposal according to which
conceptual representations in cognitive agents should be intended as
heterogeneous proxytypes. The main contribution of this paper is in that it
details how to reconcile, under a heterogeneous representational perspective,
different theories of typicality about conceptual representation and reasoning.
In particular, it provides a novel theoretical hypothesis - as well as a novel
categorization algorithm called DELTA - showing how to integrate the
representational and reasoning assumptions of the theory-theory of concepts
with the those ascribed to the prototype and exemplars-based theories.
| [
{
"version": "v1",
"created": "Wed, 4 Sep 2019 09:30:54 GMT"
}
] | 1,567,641,600,000 | [
[
"Lieto",
"Antonio",
""
]
] |
1909.01794 | Albert Harm Schrotenboer | Albert H. Schrotenboer, Susanne Wruck, Iris F. A. Vis, Kees Jan
Roodbergen | Integration of returns and decomposition of customer orders in
e-commerce warehouses | Authors' preprint | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In picker-to-parts warehouses, order picking is a cost- and labor-intensive
operation that must be designed efficiently. It comprises the construction of
order batches and the associated order picker routes, and the assignment and
sequencing of those batches to multiple order pickers. The ever-increasing
competitiveness among e-commerce companies has made the joint optimization of
this order picking process inevitable. Inspired by the large number of product
returns and the many but small-sized customer orders, we address a new
integrated order picking process problem. We integrate the restocking of
returned products into regular order picking routes and we allow for the
decomposition of customer orders so that multiple batches may contain products
from the same customer order. We thereby generalize the existing models on
order picking processing. We provide Mixed Integer Programming (MIP)
formulations and a tailored adaptive large neighborhood search heuristic that,
amongst others, exploits these MIPs. We propose a new set of practically-sized
benchmark instances, consisting of up to 5547 to be picked products and 2491 to
be restocked products. On those large-scale instances, we show that integrating
the restocking of returned products into regular order picker routes results in
cost-savings of 10 to 15%. Allowing for the decomposition of the customer
orders' products results in cost savings of up to 44% compared to not allowing
this. Finally, we show that on average cost-savings of 17.4% can be obtained by
using our ALNS instead of heuristics typically used in practice.
| [
{
"version": "v1",
"created": "Sun, 1 Sep 2019 11:12:26 GMT"
}
] | 1,567,641,600,000 | [
[
"Schrotenboer",
"Albert H.",
""
],
[
"Wruck",
"Susanne",
""
],
[
"Vis",
"Iris F. A.",
""
],
[
"Roodbergen",
"Kees Jan",
""
]
] |
1909.01801 | Paul Kantor | Paul B. Kantor | Soft Triangles for Expert Aggregation | 10 pp. 5 figures. 1 Table. Research Technical Report. This is really
about elicitation -- the MSC class is not a very good fit This revision
corrects and error in Eq 14, and adds one missing citation | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of eliciting expert assessments of an uncertain
parameter. The context is risk control, where there are, in fact, three
uncertain parameters to be estimates. Two of these are probabilities, requiring
the that the experts be guided in the concept of "uncertainty about
uncertainty." We propose a novel formulation for expert estimates, which relies
on the range and the median, rather than the variance and the mean. We discuss
the process of elicitation, and provide precise formulas for these new
distributions.
| [
{
"version": "v1",
"created": "Mon, 2 Sep 2019 18:33:40 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Oct 2020 16:27:40 GMT"
}
] | 1,603,411,200,000 | [
[
"Kantor",
"Paul B.",
""
]
] |
1909.02810 | Henry Prakken | Alejandro J. Garcia, Henry Prakken, Guillermo R. Simari | A Comparative Study of Some Central Notions of ASPIC+ and DeLP | To appear in Theory and Practice of Logic Programming (TPLP). In the
second uploaded version a small typo was corrected in Example 8 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper formally compares some central notions from two well-known
formalisms for rule-based argumentation, DeLP and ASPIC+. The comparisons
especially focus on intuitive adequacy and inter-translatability, consistency,
and closure properties. As for differences in the definitions of arguments and
attack, it turns out that DeLP's definitions are intuitively appealing but that
they may not fully comply with Caminada and Amgoud's rationality postulates of
strict closure and indirect consistency. For some special cases, the DeLP
definitions are shown to fare better than ASPIC+. Next, it is argued that there
are reasons to consider a variant of DeLP with grounded semantics, since in
some examples its current notion of warrant arguably has counterintuitive
consequences and may lead to sets of warranted arguments that are not
admissible. Finally, under some minimality and consistency assumptions on
ASPIC+ arguments, a one-to-many correspondence between ASPIC+ arguments and
DeLP arguments is identified in such a way that if the DeLP warranting
procedure is changed to grounded semantics, then DeLP notion of warrant and
ASPIC+'s notion of justification are equivalent. This result is proven for
three alternative definitions of attack.
| [
{
"version": "v1",
"created": "Fri, 6 Sep 2019 10:37:48 GMT"
},
{
"version": "v2",
"created": "Wed, 11 Sep 2019 06:43:01 GMT"
}
] | 1,568,246,400,000 | [
[
"Garcia",
"Alejandro J.",
""
],
[
"Prakken",
"Henry",
""
],
[
"Simari",
"Guillermo R.",
""
]
] |
1909.03094 | Michael Green | Michael Cerny Green, Ahmed Khalifa, Gabriella A.B. Barros, Tiago
Machado and Julian Togelius | Automatic Critical Mechanic Discovery Using Playtraces in Video Games | 15 pages, 4 figures, 2 tables, 1 algorithm, 1 equation | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new method of automatic critical mechanic discovery for video
games using a combination of game description parsing and playtrace
information. This method is applied to several games within the General Video
Game Artificial Intelligence (GVG-AI) framework. In a user study,
human-identified mechanics are compared against system-identified critical
mechanics to verify alignment between humans and the system. The results of the
study demonstrate that the new method is able to match humans with higher
consistency than baseline. Our system is further validated by comparing MCTS
agents augmented with critical mechanics and vanilla MCTS agents on $4$ games
from GVG-AI. Our new playtrace method shows a significant performance
improvement over the baseline for all 4 tested games. The proposed method also
shows either matched or improved performance over the old method, demonstrating
that playtrace information is responsible for more complete critical mechanic
discovery.
| [
{
"version": "v1",
"created": "Fri, 6 Sep 2019 19:12:45 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Feb 2020 23:15:48 GMT"
},
{
"version": "v3",
"created": "Tue, 15 Sep 2020 12:36:34 GMT"
}
] | 1,600,214,400,000 | [
[
"Green",
"Michael Cerny",
""
],
[
"Khalifa",
"Ahmed",
""
],
[
"Barros",
"Gabriella A. B.",
""
],
[
"Machado",
"Tiago",
""
],
[
"Togelius",
"Julian",
""
]
] |
1909.03373 | Dong Li | Dong Li, Bo Ouyang, Duanpo Wu, Yaonan Wang | Artificial intelligence empowered multi-AGVs in manufacturing systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AGVs are driverless robotic vehicles that picks up and delivers materials.
How to improve the efficiency while preventing deadlocks is the core issue in
designing AGV systems. In this paper, we propose an approach to tackle this
problem.The proposed approach includes a traditional AGV scheduling algorithm,
which aims at solving deadlock problems, and an artificial neural network based
component, which predict future tasks of the AGV system, and make decisions on
whether to send an AGV to the predicted starting location of the upcoming
task,so as to save the time of waiting for an AGV to go to there first when the
upcoming task is created. Simulation results show that the proposed method
significantly improves the efficiency as against traditional method, up to 20%
to 30%.
| [
{
"version": "v1",
"created": "Sun, 8 Sep 2019 02:41:19 GMT"
}
] | 1,568,073,600,000 | [
[
"Li",
"Dong",
""
],
[
"Ouyang",
"Bo",
""
],
[
"Wu",
"Duanpo",
""
],
[
"Wang",
"Yaonan",
""
]
] |
1909.03616 | Ryuta Arisaka | Ryuta Arisaka, Makoto Hagiwara, Takayuki Ito | Formulating Manipulable Argumentation with Intra-/Inter-Agent
Preferences | No major change except for some stylistic change | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From marketing to politics, exploitation of incomplete information through
selective communication of arguments is ubiquitous. In this work, we focus on
development of an argumentation-theoretic model for manipulable multi-agent
argumentation, where each agent may transmit deceptive information to others
for tactical motives. In particular, we study characterisation of epistemic
states, and their roles in deception/honesty detection and (mis)trust-building.
To this end, we propose the use of intra-agent preferences to handle
deception/honesty detection and inter-agent preferences to determine which
agent(s) to believe in more. We show how deception/honesty in an argumentation
of an agent, if detected, would alter the agent's perceived trustworthiness,
and how that may affect their judgement as to which arguments should be
acceptable.
| [
{
"version": "v1",
"created": "Mon, 9 Sep 2019 03:29:11 GMT"
},
{
"version": "v2",
"created": "Sun, 15 Sep 2019 03:45:12 GMT"
}
] | 1,568,678,400,000 | [
[
"Arisaka",
"Ryuta",
""
],
[
"Hagiwara",
"Makoto",
""
],
[
"Ito",
"Takayuki",
""
]
] |
1909.03983 | Debarpita Santra | Debarpita Santra, S. K. Basu, J. K. Mondal, Subrata Goswami | Lattice-Based Fuzzy Medical Expert System for Low Back Pain Management | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Low Back Pain (LBP) is a common medical condition that deprives many
individuals worldwide of their normal routine activities. In the absence of
external biomarkers, diagnosis of LBP is quite challenging. It requires dealing
with several clinical variables, which have no precisely quantified values.
Aiming at the development of a fuzzy medical expert system for LBP management,
this research proposes an attractive lattice-based knowledge representation
scheme for handling imprecision in knowledge, offering a suitable design
methodology for a fuzzy knowledge base and a fuzzy inference system. The fuzzy
knowledge base is constructed in modular fashion, with each module capturing
interrelated medical knowledge about the relevant clinical history, clinical
examinations and laboratory investigation results. This approach in design
ensures optimality, consistency and preciseness in the knowledge base and
scalability. The fuzzy inference system, which uses the Mamdani method, adopts
the triangular membership function for fuzzification and the Centroid of Area
technique for defuzzification. A prototype of this system has been built using
the knowledge extracted from the domain expert physicians. The inference of the
system against a few available patient records at the ESI Hospital, Sealdah has
been checked. It was found to be acceptable by the verifying medical experts.
| [
{
"version": "v1",
"created": "Mon, 9 Sep 2019 16:44:51 GMT"
}
] | 1,568,073,600,000 | [
[
"Santra",
"Debarpita",
""
],
[
"Basu",
"S. K.",
""
],
[
"Mondal",
"J. K.",
""
],
[
"Goswami",
"Subrata",
""
]
] |
1909.03987 | Debarpita Santra | Debarpita Santra, Jyotsna Kumar Mandal, Swapan Kumar Basu, Subrata
Goswami | Addressing Design Issues in Medical Expert System for Low Back Pain
Management: Knowledge Representation, Inference Mechanism, and Conflict
Resolution Using Bayesian Network | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Aiming at developing a medical expert system for low back pain management,
the paper proposes an efficient knowledge representation scheme using frame
data structures, and also derives a reliable resolution logic through Bayesian
Network. When a patient comes to the intended expert system for diagnosis, the
proposed inference engine outputs a number of probable diseases in sorted
order, with each disease being associated with a numeric measure to indicate
its possibility of occurrence. When two or more diseases in the list have the
same or closer possibility of occurrence, Bayesian Network is used for conflict
resolution. The proposed scheme has been validated with cases of empirically
selected thirty patients. Considering the expected value 0.75 as level of
acceptance, the proposed system offers the diagnostic inference with the
standard deviation of 0.029. The computational value of Chi-Squared test has
been obtained as 11.08 with 12 degree of freedom, implying that the derived
results from the designed system conform the homogeneity with the expected
outcomes. Prior to any clinical investigations on the selected low back pain
patients, the accuracy level (average) of 73.89% has been achieved by the
proposed system, which is quite close to the expected clinical accuracy level
of 75%.
| [
{
"version": "v1",
"created": "Mon, 9 Sep 2019 16:55:30 GMT"
}
] | 1,568,073,600,000 | [
[
"Santra",
"Debarpita",
""
],
[
"Mandal",
"Jyotsna Kumar",
""
],
[
"Basu",
"Swapan Kumar",
""
],
[
"Goswami",
"Subrata",
""
]
] |
1909.04171 | Joshua Bertram | Joshua R. Bertram and Peng Wei | An Efficient Algorithm for Multiple-Pursuer-Multiple-Evader
Pursuit/Evasion Game | submitted to ACC2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for pursuit/evasion that is highly efficient and and
scales to large teams of aircraft. The underlying algorithm is an efficient
algorithm for solving Markov Decision Processes (MDPs) that supports fully
continuous state spaces. We demonstrate the algorithm in a team pursuit/evasion
setting in a 3D environment using a pseudo-6DOF model and study performance by
varying sizes of team members. We show that as the number of aircraft in the
simulation grows, computational performance remains efficient and is suitable
for real-time systems. We also define probability-to-win and survivability
metrics that describe the teams' performance over multiple trials, and show
that the algorithm performs consistently. We provide numerical results showing
control inputs for a typical 1v1 encounter and provide videos for 1v1, 2v2,
3v3, 4v4, and 10v10 contests to demonstrate the ability of the algorithm to
adapt seamlessly to complex environments.
| [
{
"version": "v1",
"created": "Mon, 9 Sep 2019 21:46:31 GMT"
}
] | 1,568,160,000,000 | [
[
"Bertram",
"Joshua R.",
""
],
[
"Wei",
"Peng",
""
]
] |
1909.04256 | Zhe Xu | Zhe Xu and Ufuk Topcu | Transfer of Temporal Logic Formulas in Reinforcement Learning | IJCAI'19 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transferring high-level knowledge from a source task to a target task is an
effective way to expedite reinforcement learning (RL). For example,
propositional logic and first-order logic have been used as representations of
such knowledge. We study the transfer of knowledge between tasks in which the
timing of the events matters. We call such tasks temporal tasks. We concretize
similarity between temporal tasks through a notion of logical transferability,
and develop a transfer learning approach between different yet similar temporal
tasks. We first propose an inference technique to extract metric interval
temporal logic (MITL) formulas in sequential disjunctive normal form from
labeled trajectories collected in RL of the two tasks. If logical
transferability is identified through this inference, we construct a timed
automaton for each sequential conjunctive subformula of the inferred MITL
formulas from both tasks. We perform RL on the extended state which includes
the locations and clock valuations of the timed automata for the source task.
We then establish mappings between the corresponding components (clocks,
locations, etc.) of the timed automata from the two tasks, and transfer the
extended Q-functions based on the established mappings. Finally, we perform RL
on the extended state for the target task, starting with the transferred
extended Q-functions. Our results in two case studies show, depending on how
similar the source task and the target task are, that the sampling efficiency
for the target task can be improved by up to one order of magnitude by
performing RL in the extended state space, and further improved by up to
another order of magnitude using the transferred extended Q-functions.
| [
{
"version": "v1",
"created": "Tue, 10 Sep 2019 03:11:05 GMT"
}
] | 1,568,160,000,000 | [
[
"Xu",
"Zhe",
""
],
[
"Topcu",
"Ufuk",
""
]
] |
1909.04307 | Thommen George Karimpanal | Thommen George Karimpanal, Santu Rana, Sunil Gupta, Truyen Tran and
Svetha Venkatesh | Learning Transferable Domain Priors for Safe Exploration in
Reinforcement Learning | IJCNN, 2020 (To appear) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prior access to domain knowledge could significantly improve the performance
of a reinforcement learning agent. In particular, it could help agents avoid
potentially catastrophic exploratory actions, which would otherwise have to be
experienced during learning. In this work, we identify consistently undesirable
actions in a set of previously learned tasks, and use pseudo-rewards associated
with them to learn a prior policy. In addition to enabling safer exploratory
behaviors in subsequent tasks in the domain, we show that these priors are
transferable to similar environments, and can be learned off-policy and in
parallel with the learning of other tasks in the domain. We compare our
approach to established, state-of-the-art algorithms in both discrete as well
as continuous environments, and demonstrate that it exhibits a safer
exploratory behavior while learning to perform arbitrary tasks in the domain.
We also present a theoretical analysis to support these results, and briefly
discuss the implications and some alternative formulations of this approach,
which could also be useful in certain scenarios.
| [
{
"version": "v1",
"created": "Tue, 10 Sep 2019 06:03:52 GMT"
},
{
"version": "v2",
"created": "Wed, 11 Sep 2019 13:09:56 GMT"
},
{
"version": "v3",
"created": "Wed, 5 Feb 2020 00:02:49 GMT"
},
{
"version": "v4",
"created": "Mon, 10 Feb 2020 23:46:32 GMT"
},
{
"version": "v5",
"created": "Sun, 13 Sep 2020 05:15:00 GMT"
}
] | 1,600,128,000,000 | [
[
"Karimpanal",
"Thommen George",
""
],
[
"Rana",
"Santu",
""
],
[
"Gupta",
"Sunil",
""
],
[
"Tran",
"Truyen",
""
],
[
"Venkatesh",
"Svetha",
""
]
] |
1909.04405 | Damien Pellier | D. H\"oller, G. Behnke, P. Bercher, S. Biundo, H. Fiorino, D. Pellier,
R. Alford | Hierarchical Planning in the IPC | null | Workshop on the International Planning Competition (ICAPS), 2019 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the last year, the amount of research in hierarchical planning has
increased, leading to significant improvements in the performance of planners.
However, the research is diverging and planners are somewhat hard to compare
against each other. This is mostly caused by the fact that there is no standard
set of benchmark domains, nor even a common description language for
hierarchical planning problems. As a consequence, the available planners
support a widely varying set of features and (almost) none of them can solve
(or even parse) any problem developed for another planner. With this paper, we
propose to create a new track for the IPC in which hierarchical planners will
compete. This competition will result in a standardised description language,
broader support for core features of that language among planners, a set of
benchmark problems, a means to fairly and objectively compare HTN planners, and
for new challenges for planners.
| [
{
"version": "v1",
"created": "Tue, 10 Sep 2019 11:02:56 GMT"
}
] | 1,568,160,000,000 | [
[
"Höller",
"D.",
""
],
[
"Behnke",
"G.",
""
],
[
"Bercher",
"P.",
""
],
[
"Biundo",
"S.",
""
],
[
"Fiorino",
"H.",
""
],
[
"Pellier",
"D.",
""
],
[
"Alford",
"R.",
""
]
] |
1909.05546 | Blai Bonet | Blai Bonet, Hector Geffner | Learning First-Order Symbolic Representations for Planning from the
Structure of the State Space | Proc. ECAI-2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the main obstacles for developing flexible AI systems is the split
between data-based learners and model-based solvers. Solvers such as classical
planners are very flexible and can deal with a variety of problem instances and
goals but require first-order symbolic models. Data-based learners, on the
other hand, are robust but do not produce such representations. In this work we
address this split by showing how the first-order symbolic representations that
are used by planners can be learned from non-symbolic inputs that encode the
structure of the state space. The representation learning problem is formulated
as the problem of inferring planning instances over a common but unknown
first-order domain that account for the structure of the observed state space.
This means to infer a complete first-order representation (i.e. general action
schemas, relational symbols, and objects) that explains the observed state
space structures. The inference problem is cast as a two-level combinatorial
search where the outer level searches for values of a small set of
hyperparameters and the inner level, solved via SAT, searches for a first-order
symbolic model. The framework is shown to produce general and correct
first-order representations for standard problems like Gripper, Blocksworld,
and Hanoi from input graphs that encode the flat state-space structure of a
single instance.
| [
{
"version": "v1",
"created": "Thu, 12 Sep 2019 10:13:08 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Nov 2019 23:11:44 GMT"
},
{
"version": "v3",
"created": "Thu, 20 Feb 2020 15:45:42 GMT"
}
] | 1,582,243,200,000 | [
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
1909.05628 | Theophanes Raptis Mr | T. E. Raptis | Hidden Structure in the Solutions Set of the N Queens Problem | 16 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Some preliminary results are reported on the equivalence of any n-queens
problem with the roots of a Boolean valued quadratic form via a generic
dimensional reduction scheme. It is then proven that the solutions set is
encoded in the entries of a special matrix. Further examination reveals a
direct association with pointwise Boolean fractal operators applied on certain
integer sequences associated with this matrix suggesting the presence of an
underlying special geometry of the solutions set.
| [
{
"version": "v1",
"created": "Fri, 26 Jul 2019 17:16:37 GMT"
}
] | 1,568,332,800,000 | [
[
"Raptis",
"T. E.",
""
]
] |
1909.05912 | Daniel Neider | Zhe Xu, Ivan Gavran, Yousef Ahmad, Rupak Majumdar, Daniel Neider, Ufuk
Topcu and Bo Wu | Joint Inference of Reward Machines and Policies for Reinforcement
Learning | Fixed incorrect references in proof of Lemma 4 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Incorporating high-level knowledge is an effective way to expedite
reinforcement learning (RL), especially for complex tasks with sparse rewards.
We investigate an RL problem where the high-level knowledge is in the form of
reward machines, i.e., a type of Mealy machine that encodes the reward
functions. We focus on a setting in which this knowledge is a priori not
available to the learning agent. We develop an iterative algorithm that
performs joint inference of reward machines and policies for RL (more
specifically, q-learning). In each iteration, the algorithm maintains a
hypothesis reward machine and a sample of RL episodes. It derives q-functions
from the current hypothesis reward machine, and performs RL to update the
q-functions. While performing RL, the algorithm updates the sample by adding RL
episodes along which the obtained rewards are inconsistent with the rewards
based on the current hypothesis reward machine. In the next iteration, the
algorithm infers a new hypothesis reward machine from the updated sample. Based
on an equivalence relationship we defined between states of reward machines, we
transfer the q-functions between the hypothesis reward machines in consecutive
iterations. We prove that the proposed algorithm converges almost surely to an
optimal policy in the limit if a minimal reward machine can be inferred and the
maximal length of each RL episode is sufficiently long. The experiments show
that learning high-level knowledge in the form of reward machines can lead to
fast convergence to optimal policies in RL, while standard RL methods such as
q-learning and hierarchical RL methods fail to converge to optimal policies
after a substantial number of training steps in many tasks.
| [
{
"version": "v1",
"created": "Thu, 12 Sep 2019 19:09:13 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Feb 2022 20:02:19 GMT"
}
] | 1,644,451,200,000 | [
[
"Xu",
"Zhe",
""
],
[
"Gavran",
"Ivan",
""
],
[
"Ahmad",
"Yousef",
""
],
[
"Majumdar",
"Rupak",
""
],
[
"Neider",
"Daniel",
""
],
[
"Topcu",
"Ufuk",
""
],
[
"Wu",
"Bo",
""
]
] |
1909.06017 | George Leu | George Leu and Jiangjun Tang | On educating machines | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine education is an emerging research field that focuses on the problem
which is inverse to machine learning. To date, the literature on educating
machines is still in its infancy. A fairly low number of methodology and method
papers are scattered throughout various formal and informal publication
avenues, mainly because the field is not yet well coalesced (with no well
established discussion forums or investigation pathways), but also due to the
breadth of its potential ramifications and research directions. In this study
we bring together the existing literature and organise the discussion into a
small number of research directions (out of many) which are to date
sufficiently explored to form a minimal critical mass that can push the machine
education concept further towards a standalone research field status.
| [
{
"version": "v1",
"created": "Fri, 13 Sep 2019 03:39:53 GMT"
}
] | 1,568,592,000,000 | [
[
"Leu",
"George",
""
],
[
"Tang",
"Jiangjun",
""
]
] |
1909.06427 | Richard Freedman | Richard G. Freedman, Yi Ren Fung, Roman Ganchin, Shlomo Zilberstein | Responsive Planning and Recognition for Closed-Loop Interaction | Accepted for presentation at the AAAI 2019 Fall Symposium Series, in
the symposium for Artificial Intelligence and Human-Robot Interaction for
Service Robots in Human Environments | null | null | AI-HRI/2019/24 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many intelligent systems currently interact with others using at least one of
fixed communication inputs or preset responses, resulting in rigid interaction
experiences and extensive efforts developing a variety of scenarios for the
system. Fixed inputs limit the natural behavior of the user in order to
effectively communicate, and preset responses prevent the system from adapting
to the current situation unless it was specifically implemented. Closed-loop
interaction instead focuses on dynamic responses that account for what the user
is currently doing based on interpretations of their perceived activity. Agents
employing closed-loop interaction can also monitor their interactions to ensure
that the user responds as expected. We introduce a closed-loop interactive
agent framework that integrates planning and recognition to predict what the
user is trying to accomplish and autonomously decide on actions to take in
response to these predictions. Based on a recent demonstration of such an
assistive interactive agent in a turn-based simulated game, we also discuss new
research challenges that are not present in the areas of artificial
intelligence planning or recognition alone.
| [
{
"version": "v1",
"created": "Fri, 13 Sep 2019 20:11:16 GMT"
}
] | 1,568,678,400,000 | [
[
"Freedman",
"Richard G.",
""
],
[
"Fung",
"Yi Ren",
""
],
[
"Ganchin",
"Roman",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1909.07893 | Rocsildes Canoy | Rocsildes Canoy and Tias Guns | Vehicle routing by learning from historical solutions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of this paper is to investigate a decision support system for
vehicle routing, where the routing engine learns from the subjective decisions
that human planners have made in the past, rather than optimizing a
distance-based objective criterion. This is an alternative to the practice of
formulating a custom VRP for every company with its own routing requirements.
Instead, we assume the presence of past vehicle routing solutions over similar
sets of customers, and learn to make similar choices. The approach is based on
the concept of learning a first-order Markov model, which corresponds to a
probabilistic transition matrix, rather than a deterministic distance matrix.
This nevertheless allows us to use existing arc routing VRP software in
creating the actual route plans. For the learning, we explore different schemes
to construct the probabilistic transition matrix. Our results on a use-case
with a small transportation company show that our method is able to generate
results that are close to the manually created solutions, without needing to
characterize all constraints and sub-objectives explicitly. Even in the case of
changes in the client sets, our method is able to find solutions that are
closer to the actual route plans than when using distances, and hence,
solutions that would require fewer manual changes to transform into the actual
route plan.
| [
{
"version": "v1",
"created": "Tue, 17 Sep 2019 15:36:10 GMT"
}
] | 1,568,764,800,000 | [
[
"Canoy",
"Rocsildes",
""
],
[
"Guns",
"Tias",
""
]
] |
1909.08234 | EPTCS | Sarthak Ghosh (Stony Brook University), C. R. Ramakrishnan (Stony
Brook University) | Value of Information in Probabilistic Logic Programs | In Proceedings ICLP 2019, arXiv:1909.07646 | EPTCS 306, 2019, pp. 71-84 | 10.4204/EPTCS.306.14 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In medical decision making, we have to choose among several expensive
diagnostic tests such that the certainty about a patient's health is maximized
while remaining within the bounds of resources like time and money. The
expected increase in certainty in the patient's condition due to performing a
test is called the value of information (VoI) for that test. In general, VoI
relates to acquiring additional information to improve decision-making based on
probabilistic reasoning in an uncertain system. This paper presents a framework
for acquiring information based on VoI in uncertain systems modeled as
Probabilistic Logic Programs (PLPs). Optimal decision-making in uncertain
systems modeled as PLPs have already been studied before. But, acquiring
additional information to further improve the results of making the optimal
decision has remained open in this context.
We model decision-making in an uncertain system with a PLP and a set of
top-level queries, with a set of utility measures over the distributions of
these queries. The PLP is annotated with a set of atoms labeled as
"observable"; in the medical diagnosis example, the observable atoms will be
results of diagnostic tests. Each observable atom has an associated cost. This
setting of optimally selecting observations based on VoI is more general than
that considered by any prior work. Given a limited budget, optimally choosing
observable atoms based on VoI is intractable in general. We give a greedy
algorithm for constructing a "conditional plan" of observations: a schedule
where the selection of what atom to observe next depends on earlier
observations. We show that, preempting the algorithm anytime before completion
provides a usable result, the result improves over time, and, in the absence of
a well-defined budget, converges to the optimal solution.
| [
{
"version": "v1",
"created": "Wed, 18 Sep 2019 07:01:28 GMT"
}
] | 1,568,851,200,000 | [
[
"Ghosh",
"Sarthak",
"",
"Stony Brook University"
],
[
"Ramakrishnan",
"C. R.",
"",
"Stony\n Brook University"
]
] |
1909.08235 | EPTCS | Craig Olson, Yuliya Lierler | Information Extraction Tool Text2ALM: From Narratives to Action Language
System Descriptions | In Proceedings ICLP 2019, arXiv:1909.07646 | EPTCS 306, 2019, pp. 87-100 | 10.4204/EPTCS.306.16 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we design a narrative understanding tool Text2ALM. This tool
uses an action language ALM to perform inferences on complex interactions of
events described in narratives. The methodology used to implement the Text2ALM
system was originally outlined by Lierler, Inclezan, and Gelfond (2017) via a
manual process of converting a narrative to an ALM model. It relies on a
conglomeration of resources and techniques from two distinct fields of
artificial intelligence, namely, natural language processing and knowledge
representation and reasoning. The effectiveness of system Text2ALM is measured
by its ability to correctly answer questions from the bAbI tasks published by
Facebook Research in 2015. This tool matched or exceeded the performance of
state-of-the-art machine learning methods in six of the seven tested tasks. We
also illustrate that the Text2ALM approach generalizes to a broader spectrum of
narratives.
| [
{
"version": "v1",
"created": "Wed, 18 Sep 2019 07:02:45 GMT"
}
] | 1,568,851,200,000 | [
[
"Olson",
"Craig",
""
],
[
"Lierler",
"Yuliya",
""
]
] |
1909.08240 | EPTCS | David Spies (University of Alberta), Jia-Huai You (University of
Alberta), Ryan Hayward (University of Alberta) | Mutex Graphs and Multicliques: Reducing Grounding Size for Planning | In Proceedings ICLP 2019, arXiv:1909.07646 | EPTCS 306, 2019, pp. 140-153 | 10.4204/EPTCS.306.20 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an approach to representing large sets of mutual exclusions, also
known as mutexes or mutex constraints. These are the types of constraints that
specify the exclusion of some properties, events, processes, and so on. They
are ubiquitous in many areas of applications. The size of these constraints for
a given problem can be overwhelming enough to present a bottleneck for the
solving efficiency of the underlying solver. In this paper, we propose a novel
graph-theoretic technique based on multicliques for a compact representation of
mutex constraints and apply it to domain-independent planning in ASP. As
computing a minimum multiclique covering from a mutex graph is NP-hard, we
propose an efficient approximation algorithm for multiclique covering and show
experimentally that it generates substantially smaller grounding size for mutex
constraints in ASP than the previously known work in SAT.
| [
{
"version": "v1",
"created": "Wed, 18 Sep 2019 07:04:54 GMT"
}
] | 1,568,851,200,000 | [
[
"Spies",
"David",
"",
"University of Alberta"
],
[
"You",
"Jia-Huai",
"",
"University of\n Alberta"
],
[
"Hayward",
"Ryan",
"",
"University of Alberta"
]
] |
1909.08252 | EPTCS | Liu Liu, Miroslaw Truszczynski | Encoding Selection for Solving Hamiltonian Cycle Problems with ASP | In Proceedings ICLP 2019, arXiv:1909.07646 | EPTCS 306, 2019, pp. 302-308 | 10.4204/EPTCS.306.35 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is common for search and optimization problems to have alternative
equivalent encodings in ASP. Typically none of them is uniformly better than
others when evaluated on broad classes of problem instances. We claim that one
can improve the solving ability of ASP by using machine learning techniques to
select encodings likely to perform well on a given instance. We substantiate
this claim by studying the hamiltonian cycle problem. We propose several
equivalent encodings of the problem and several classes of hard instances. We
build models to predict the behavior of each encoding, and then show that
selecting encodings for a given instance using the learned performance
predictors leads to significant performance gains.
| [
{
"version": "v1",
"created": "Wed, 18 Sep 2019 07:09:45 GMT"
}
] | 1,568,851,200,000 | [
[
"Liu",
"Liu",
""
],
[
"Truszczynski",
"Miroslaw",
""
]
] |
1909.08549 | Sandra Zimmer | Sebastian Fl\"ugge, Sandra Zimmer, Uwe Petersohn | Knowledge representation and diagnostic inference using Bayesian
networks in the medical discourse | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For the diagnostic inference under uncertainty Bayesian networks are
investigated. The method is based on an adequate uniform representation of the
necessary knowledge. This includes both generic and experience-based specific
knowledge, which is stored in a knowledge base. For knowledge processing, a
combination of the problem-solving methods of concept-based and case-based
reasoning is used. Concept-based reasoning is used for the diagnosis, therapy
and medication recommendation and evaluation of generic knowledge. Exceptions
in the form of specific patient cases are processed by case-based reasoning. In
addition, the use of Bayesian networks allows to deal with uncertainty,
fuzziness and incompleteness. Thus, the valid general concepts can be issued
according to their probability. To this end, various inference mechanisms are
introduced and subsequently evaluated within the context of a developed
prototype. Tests are employed to assess the classification of diagnoses by the
network.
| [
{
"version": "v1",
"created": "Wed, 18 Sep 2019 16:16:17 GMT"
},
{
"version": "v2",
"created": "Sat, 8 Oct 2022 10:45:02 GMT"
}
] | 1,665,446,400,000 | [
[
"Flügge",
"Sebastian",
""
],
[
"Zimmer",
"Sandra",
""
],
[
"Petersohn",
"Uwe",
""
]
] |
1909.08552 | Dries Van Daele | Dries Van Daele, Nicholas Decleyre, Herman Dubois, Wannes Meert | An Automated Engineering Assistant: Learning Parsers for Technical
Drawings | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From a set of technical drawings and expert knowledge, we automatically learn
a parser to interpret such a drawing. This enables automatic reasoning and
learning on top of a large database of technical drawings. In this work, we
develop a similarity based search algorithm to help engineers and designers
find or complete designs more easily and flexibly. This is part of an ongoing
effort to build an automated engineering assistant. The proposed methods make
use of both neural methods to learn to interpret images, and symbolic methods
to learn to interpret the structure in the technical drawing and incorporate
expert knowledge.
| [
{
"version": "v1",
"created": "Wed, 18 Sep 2019 16:22:08 GMT"
}
] | 1,568,851,200,000 | [
[
"Van Daele",
"Dries",
""
],
[
"Decleyre",
"Nicholas",
""
],
[
"Dubois",
"Herman",
""
],
[
"Meert",
"Wannes",
""
]
] |
1909.08794 | Rajabi Masoumi Mina | Mina Rajabi, Saeed Hossani, Fatemeh Dehghani | A literature review on current approaches and applications of fuzzy
expert systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main purposes of this study are to distinguish the trends of research in
publication exits for the utilisations of the fuzzy expert and knowledge-based
systems that is done based on the classification of studies in the last decade.
The present investigation covers 60 articles from related scholastic journals,
International conference proceedings and some major literature review papers.
Our outcomes reveal an upward trend in the up-to-date publications number, that
is evidence of growing notoriety on the various applications of fuzzy expert
systems. This raise in the reports is mainly in the medical neuro-fuzzy and
fuzzy expert systems. Moreover, another most critical observation is that many
modern industrial applications are extended, employing knowledge-based systems
by extracting the experts' knowledge.
| [
{
"version": "v1",
"created": "Thu, 19 Sep 2019 03:56:49 GMT"
}
] | 1,568,937,600,000 | [
[
"Rajabi",
"Mina",
""
],
[
"Hossani",
"Saeed",
""
],
[
"Dehghani",
"Fatemeh",
""
]
] |
1909.09291 | Monireh Dabaghchian | Monireh Dabaghchian, Amir Alipour-Fanid, Kai Zeng | Intelligent Policing Strategy for Traffic Violation Prevention | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Police officer presence at an intersection discourages a potential traffic
violator from violating the law. It also alerts the motorists' consciousness to
take precaution and follow the rules. However, due to the abundant
intersections and shortage of human resources, it is not possible to assign a
police officer to every intersection. In this paper, we propose an intelligent
and optimal policing strategy for traffic violation prevention. Our model
consists of a specific number of targeted intersections and two police officers
with no prior knowledge on the number of the traffic violations in the
designated intersections. At each time interval, the proposed strategy, assigns
the two police officers to different intersections such that at the end of the
time horizon, maximum traffic violation prevention is achieved. Our proposed
methodology adapts the PROLA (Play and Random Observe Learning Algorithm)
algorithm [1] to achieve an optimal traffic violation prevention strategy.
Finally, we conduct a case study to evaluate and demonstrate the performance of
the proposed method.
| [
{
"version": "v1",
"created": "Fri, 20 Sep 2019 01:39:17 GMT"
}
] | 1,569,196,800,000 | [
[
"Dabaghchian",
"Monireh",
""
],
[
"Alipour-Fanid",
"Amir",
""
],
[
"Zeng",
"Kai",
""
]
] |
1909.09362 | Zhe Zeng Miss | Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari, Guy Van den
Broeck | Hybrid Probabilistic Inference with Logical Constraints: Tractability
and Message Passing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Weighted model integration (WMI) is a very appealing framework for
probabilistic inference: it allows to express the complex dependencies of
real-world hybrid scenarios where variables are heterogeneous in nature (both
continuous and discrete) via the language of Satisfiability Modulo Theories
(SMT); as well as computing probabilistic queries with arbitrarily complex
logical constraints. Recent work has shown WMI inference to be reducible to a
model integration (MI) problem, under some assumptions, thus effectively
allowing hybrid probabilistic reasoning by volume computations. In this paper,
we introduce a novel formulation of MI via a message passing scheme that allows
to efficiently compute the marginal densities and statistical moments of all
the variables in linear time. As such, we are able to amortize inference for
arbitrarily rich MI queries when they conform to the problem structure, here
represented as the primal graph associated to the SMT formula. Furthermore, we
theoretically trace the tractability boundaries of exact MI. Indeed, we prove
that in terms of the structural requirements on the primal graph that make our
MI algorithm tractable - bounding its diameter and treewidth - the bounds are
not only sufficient, but necessary for tractable inference via MI.
| [
{
"version": "v1",
"created": "Fri, 20 Sep 2019 07:56:29 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Sep 2019 07:19:49 GMT"
}
] | 1,569,888,000,000 | [
[
"Zeng",
"Zhe",
""
],
[
"Yan",
"Fanqi",
""
],
[
"Morettin",
"Paolo",
""
],
[
"Vergari",
"Antonio",
""
],
[
"Broeck",
"Guy Van den",
""
]
] |
1909.09616 | Hankz Hankui Zhuo | Xinghua Zheng, Ming Tang, Hankz Hankui Zhuo, Kevin X. Wen | Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike
Sharing Systems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Bike Sharing Systems (BSSs) have been adopted in many major cities of the
world due to traffic congestion and carbon emissions. Although there have been
approaches to exploiting either bike trailers via crowdsourcing or carrier
vehicles to reposition bikes in the ``right'' stations in the ``right'' time,
they do not jointly consider the usage of both bike trailers and carrier
vehicles. In this paper, we aim to take advantage of both bike trailers and
carrier vehicles to reduce the loss of demand with regard to the crowdsourcing
of bike trailers and the fuel cost of carrier vehicles. In the experiment, we
exhibit that our approach outperforms baselines in several datasets from bike
sharing companies.
| [
{
"version": "v1",
"created": "Fri, 20 Sep 2019 17:09:29 GMT"
}
] | 1,569,196,800,000 | [
[
"Zheng",
"Xinghua",
""
],
[
"Tang",
"Ming",
""
],
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Wen",
"Kevin X.",
""
]
] |
1909.09742 | Paul Tarau | Paul Tarau and Eduardo Blanco | Dependency-based Text Graphs for Keyphrase and Summary Extraction with
Applications to Interactive Content Retrieval | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We build a bridge between neural network-based machine learning and
graph-based natural language processing and introduce a unified approach to
keyphrase, summary and relation extraction by aggregating dependency graphs
from links provided by a deep-learning based dependency parser.
We reorganize dependency graphs to focus on the most relevant content
elements of a sentence, integrate sentence identifiers as graph nodes and after
ranking the graph, we extract our keyphrases and summaries from its largest
strongly-connected component. We take advantage of the implicit structural
information that dependency links bring to extract subject-verb-object, is-a
and part-of relations.
We put it all together into a proof-of-concept dialog engine that specializes
the text graph with respect to a query and reveals interactively the document's
most relevant content elements. The open-source code of the integrated system
is available at https://github.com/ptarau/DeepRank .
Keywords: graph-based natural language processing, dependency graphs,
keyphrase, summary and relation extraction, query-driven salient sentence
extraction, logic-based dialog engine, synergies between neural and symbolic
processing.
| [
{
"version": "v1",
"created": "Fri, 20 Sep 2019 23:57:31 GMT"
}
] | 1,569,542,400,000 | [
[
"Tarau",
"Paul",
""
],
[
"Blanco",
"Eduardo",
""
]
] |
1909.09758 | Yue Ning | Ameya Vaidya, Feng Mai, Yue Ning | Empirical Analysis of Multi-Task Learning for Reducing Model Bias in
Toxic Comment Detection | ICWSM 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the recent rise of toxicity in online conversations on social media
platforms, using modern machine learning algorithms for toxic comment detection
has become a central focus of many online applications. Researchers and
companies have developed a variety of models to identify toxicity in online
conversations, reviews, or comments with mixed successes. However, many
existing approaches have learned to incorrectly associate non-toxic comments
that have certain trigger-words (e.g. gay, lesbian, black, muslim) as a
potential source of toxicity. In this paper, we evaluate several
state-of-the-art models with the specific focus of reducing model bias towards
these commonly-attacked identity groups. We propose a multi-task learning model
with an attention layer that jointly learns to predict the toxicity of a
comment as well as the identities present in the comments in order to reduce
this bias. We then compare our model to an array of shallow and deep-learning
models using metrics designed especially to test for unintended model bias
within these identity groups.
| [
{
"version": "v1",
"created": "Sat, 21 Sep 2019 01:27:32 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Sep 2019 14:49:42 GMT"
},
{
"version": "v3",
"created": "Fri, 27 Mar 2020 16:37:43 GMT"
}
] | 1,585,526,400,000 | [
[
"Vaidya",
"Ameya",
""
],
[
"Mai",
"Feng",
""
],
[
"Ning",
"Yue",
""
]
] |
1909.09964 | Sai Krishna Rallabandi | SaiKrishna Rallabandi | On Controlled DeEntanglement for Natural Language Processing | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Latest addition to the toolbox of human species is Artificial
Intelligence(AI). Thus far, AI has made significant progress in low stake low
risk scenarios such as playing Go and we are currently in a transition toward
medium stake scenarios such as Visual Dialog. In my thesis, I argue that we
need to incorporate controlled de-entanglement as first class object to succeed
in this transition. I present mathematical analysis from information theory to
show that employing stochasticity leads to controlled de-entanglement of
relevant factors of variation at various levels. Based on this, I highlight
results from initial experiments that depict efficacy of the proposed
framework. I conclude this writeup by a roadmap of experiments that show the
applicability of this framework to scalability, flexibility and
interpretibility.
| [
{
"version": "v1",
"created": "Sun, 22 Sep 2019 08:13:47 GMT"
}
] | 1,569,542,400,000 | [
[
"Rallabandi",
"SaiKrishna",
""
]
] |
1909.10031 | Peilun Wu | Peilun Wu and Hui Guo | LuNet: A Deep Neural Network for Network Intrusion Detection | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network attack is a significant security issue for modern society. From small
mobile devices to large cloud platforms, almost all computing products, used in
our daily life, are networked and potentially under the threat of network
intrusion. With the fast-growing network users, network intrusions become more
and more frequent, volatile and advanced. Being able to capture intrusions in
time for such a large scale network is critical and very challenging. To this
end, the machine learning (or AI) based network intrusion detection (NID), due
to its intelligent capability, has drawn increasing attention in recent years.
Compared to the traditional signature-based approaches, the AI-based solutions
are more capable of detecting variants of advanced network attacks. However,
the high detection rate achieved by the existing designs is usually accompanied
by a high rate of false alarms, which may significantly discount the overall
effectiveness of the intrusion detection system. In this paper, we consider the
existence of spatial and temporal features in the network traffic data and
propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the
convolutional neural network (CNN) and the recurrent neural network (RNN) learn
input traffic data in sync with a gradually increasing granularity such that
both spatial and temporal features of the data can be effectively extracted.
Our experiments on two network traffic datasets show that compared to the
state-of-the-art network intrusion detection techniques, LuNet not only offers
a high level of detection capability but also has a much low rate of false
positive-alarm.
| [
{
"version": "v1",
"created": "Sun, 22 Sep 2019 15:34:27 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Oct 2019 16:40:12 GMT"
}
] | 1,570,492,800,000 | [
[
"Wu",
"Peilun",
""
],
[
"Guo",
"Hui",
""
]
] |
1909.10090 | Amro Najjar | Yazan Mualla, Amro Najjar, Timotheus Kampik, Igor Tchappi, St\'ephane
Galland, Christophe Nicolle | Towards Explainability for a Civilian UAV Fleet Management using an
Agent-based Approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an initial design concept and specification of a civilian
Unmanned Aerial Vehicle (UAV) management simulation system that focuses on
explainability for the human-in-the-loop control of semi-autonomous UAVs. The
goal of the system is to facilitate the operator intervention in critical
scenarios (e.g. avoid safety issues or financial risks). Explainability is
supported via user-friendly abstractions on Belief-Desire-Intention agents. To
evaluate the effectiveness of the system, a human-computer interaction study is
proposed.
| [
{
"version": "v1",
"created": "Sun, 22 Sep 2019 20:34:09 GMT"
}
] | 1,569,542,400,000 | [
[
"Mualla",
"Yazan",
""
],
[
"Najjar",
"Amro",
""
],
[
"Kampik",
"Timotheus",
""
],
[
"Tchappi",
"Igor",
""
],
[
"Galland",
"Stéphane",
""
],
[
"Nicolle",
"Christophe",
""
]
] |
1909.10157 | Zhaoyi Pei Mr | Zhaoyi Pei, Piaosong Hao, Meixiang Quan, Muhammad Zuhair Qadir, Guo Li | Active collaboration in relative observation for Multi-agent visual SLAM
based on Deep Q Network | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a unique active relative localization mechanism for
multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be
observed are considered as a task, which is performed by others assisting that
agent by relative observation. A task allocation algorithm based on deep
reinforcement learning are proposed for this mechanism. Each agent can choose
whether to localize other agents or to continue independent SLAM on it own
initiative. By this way, the process of each agent SLAM will be interacted by
the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique
observation function which models the whole MAS is obtained. Secondly, a novel
type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence
between Q Value and state-action pair,abstract representation of agents in MAS
are learned in the process of collaboration among agents. Finally, each agent
must act with a certain degree of freedom according to MAS-DQN. The simulation
results of comparative experiments prove that this mechanism improves the
efficiency of cooperation in the process of multi-agent SLAM.
| [
{
"version": "v1",
"created": "Mon, 23 Sep 2019 04:58:19 GMT"
}
] | 1,569,283,200,000 | [
[
"Pei",
"Zhaoyi",
""
],
[
"Hao",
"Piaosong",
""
],
[
"Quan",
"Meixiang",
""
],
[
"Qadir",
"Muhammad Zuhair",
""
],
[
"Li",
"Guo",
""
]
] |
1909.10158 | Hamidreza Shahidi | Hamidreza Shahidi, Ming Li, and Jimmy Lin | Two Birds, One Stone: A Simple, Unified Model for Text Generation from
Structured and Unstructured Data | Accepted at ACL 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A number of researchers have recently questioned the necessity of
increasingly complex neural network (NN) architectures. In particular, several
recent papers have shown that simpler, properly tuned models are at least
competitive across several NLP tasks. In this work, we show that this is also
the case for text generation from structured and unstructured data. We consider
neural table-to-text generation and neural question generation (NQG) tasks for
text generation from structured and unstructured data, respectively.
Table-to-text generation aims to generate a description based on a given table,
and NQG is the task of generating a question from a given passage where the
generated question can be answered by a certain sub-span of the passage using
NN models. Experimental results demonstrate that a basic attention-based
seq2seq model trained with the exponential moving average technique achieves
the state of the art in both tasks. Code is available at
https://github.com/h-shahidi/2birds-gen.
| [
{
"version": "v1",
"created": "Mon, 23 Sep 2019 05:07:06 GMT"
},
{
"version": "v2",
"created": "Fri, 1 May 2020 01:29:26 GMT"
}
] | 1,588,550,400,000 | [
[
"Shahidi",
"Hamidreza",
""
],
[
"Li",
"Ming",
""
],
[
"Lin",
"Jimmy",
""
]
] |
1909.10622 | Behrouz Babaki | Behrouz Babaki, Golnoosh Farnadi, Gilles Pesant | Compiling Stochastic Constraint Programs to And-Or Decision Diagrams | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Factored stochastic constraint programming (FSCP) is a formalism to represent
multi-stage decision making problems under uncertainty. FSCP models support
factorized probabilistic models and involve constraints over decision and
random variables. These models have many applications in real-world problems.
However, solving these problems requires evaluating the best course of action
for each possible outcome of the random variables and hence is computationally
challenging. FSCP problems often involve repeated subproblems which ideally
should be solved once. In this paper we show how identifying and exploiting
these identical subproblems can simplify solving them and leads to a compact
representation of the solution. We compile an And-Or search tree to a compact
decision diagram. Preliminary experiments show that our proposed method
significantly improves the search efficiency by reducing the size of the
problem and outperforms the existing methods.
| [
{
"version": "v1",
"created": "Mon, 23 Sep 2019 21:25:17 GMT"
}
] | 1,569,369,600,000 | [
[
"Babaki",
"Behrouz",
""
],
[
"Farnadi",
"Golnoosh",
""
],
[
"Pesant",
"Gilles",
""
]
] |
1909.11025 | Nicholas Hoernle | Nicholas Hoernle, Kobi Gal, Barbara Grosz, Leilah Lyons, Ada Ren,
Andee Rubin | Interpretable Models for Understanding Immersive Simulations | To be published in IJCAI 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes methods for comparative evaluation of the
interpretability of models of high dimensional time series data inferred by
unsupervised machine learning algorithms. The time series data used in this
investigation were logs from an immersive simulation like those commonly used
in education and healthcare training. The structures learnt by the models
provide representations of participants' activities in the simulation which are
intended to be meaningful to people's interpretation. To choose the model that
induces the best representation, we designed two interpretability tests, each
of which evaluates the extent to which a model's output aligns with people's
expectations or intuitions of what has occurred in the simulation. We compared
the performance of the models on these interpretability tests to their
performance on statistical information criteria. We show that the models that
optimize interpretability quality differ from those that optimize (statistical)
information theoretic criteria. Furthermore, we found that a model using a
fully Bayesian approach performed well on both the statistical and
human-interpretability measures. The Bayesian approach is a good candidate for
fully automated model selection, i.e., when direct empirical investigations of
interpretability are costly or infeasible.
| [
{
"version": "v1",
"created": "Tue, 24 Sep 2019 16:22:09 GMT"
},
{
"version": "v2",
"created": "Mon, 4 May 2020 08:40:30 GMT"
}
] | 1,588,636,800,000 | [
[
"Hoernle",
"Nicholas",
""
],
[
"Gal",
"Kobi",
""
],
[
"Grosz",
"Barbara",
""
],
[
"Lyons",
"Leilah",
""
],
[
"Ren",
"Ada",
""
],
[
"Rubin",
"Andee",
""
]
] |
1909.11173 | Chris Amato | Christopher Amato and Andrea Baisero | Active Goal Recognition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To coordinate with other systems, agents must be able to determine what the
systems are currently doing and predict what they will be doing in the
future---plan and goal recognition. There are many methods for plan and goal
recognition, but they assume a passive observer that continually monitors the
target system. Real-world domains, where information gathering has a cost
(e.g., moving a camera or a robot, or time taken away from another task), will
often require a more active observer. We propose to combine goal recognition
with other observer tasks in order to obtain \emph{active goal recognition}
(AGR). We discuss this problem and provide a model and preliminary experimental
results for one form of this composite problem. As expected, the results show
that optimal behavior in AGR problems balance information gathering with other
actions (e.g., task completion) such as to achieve all tasks jointly and
efficiently. We hope that our formulation opens the door for extensive further
research on this interesting and realistic problem.
| [
{
"version": "v1",
"created": "Tue, 24 Sep 2019 21:00:13 GMT"
}
] | 1,569,456,000,000 | [
[
"Amato",
"Christopher",
""
],
[
"Baisero",
"Andrea",
""
]
] |
1909.11581 | Andrea Micheli | Alessandro Valentini, Andrea Micheli and Alessandro Cimatti | Temporal Planning with Intermediate Conditions and Effects | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated temporal planning is the technology of choice when controlling
systems that can execute more actions in parallel and when temporal
constraints, such as deadlines, are needed in the model. One limitation of
several action-based planning systems is that actions are modeled as intervals
having conditions and effects only at the extremes and as invariants, but no
conditions nor effects can be specified at arbitrary points or sub-intervals.
In this paper, we address this limitation by providing an effective
heuristic-search technique for temporal planning, allowing the definition of
actions with conditions and effects at any arbitrary time within the action
duration. We experimentally demonstrate that our approach is far better than
standard encodings in PDDL 2.1 and is competitive with other approaches that
can (directly or indirectly) represent intermediate action conditions or
effects.
| [
{
"version": "v1",
"created": "Wed, 25 Sep 2019 16:16:51 GMT"
}
] | 1,569,456,000,000 | [
[
"Valentini",
"Alessandro",
""
],
[
"Micheli",
"Andrea",
""
],
[
"Cimatti",
"Alessandro",
""
]
] |
1909.11604 | Xudong Liu | Xudong Liu, Christian Fritz, Matthew Klenk | An Extensible and Personalizable Multi-Modal Trip Planner | Published in the Proceedings of the 32nd International Florida
Artificial Intelligence Research Society Conference, 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite a tremendous amount of work in the literature and in the commercial
sectors, current approaches to multi-modal trip planning still fail to
consistently generate plans that users deem optimal in practice. We believe
that this is due to the fact that current planners fail to capture the true
preferences of users, e.g., their preferences depend on aspects that are not
modeled. An example of this could be a preference not to walk through an unsafe
area at night. We present a novel multi-modal trip planner that allows users to
upload auxiliary geographic data (e.g., crime rates) and to specify temporal
constraints and preferences over these data in combination with typical metrics
such as time and cost. Concretely, our planner supports the modes walking,
biking, driving, public transit, and taxi, uses linear temporal logic to
capture temporal constraints, and preferential cost functions to represent
preferences. We show by examples that this allows the expression of very
interesting preferences and constraints that, naturally, lead to quite diverse
optimal plans.
| [
{
"version": "v1",
"created": "Wed, 25 Sep 2019 16:38:49 GMT"
}
] | 1,569,456,000,000 | [
[
"Liu",
"Xudong",
""
],
[
"Fritz",
"Christian",
""
],
[
"Klenk",
"Matthew",
""
]
] |
1909.11994 | Filippo Bistaffa | Ewa Andrejczuk, Filippo Bistaffa, Christian Blum, Juan A.
Rodr\'iguez-Aguilar, Carles Sierra | Synergistic Team Composition: A Computational Approach to Foster
Diversity in Teams | Accepted version | Volume 182, 15 October 2019, page 104799 | 10.1016/j.knosys.2019.06.007 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Co-operative learning in heterogeneous teams refers to learning methods in
which teams are organised both to accomplish academic tasks and for individuals
to gain knowledge. Competencies, personality and the gender of team members are
key factors that influence team performance. Here, we introduce a team
composition problem, the so-called synergistic team composition problem (STCP),
which incorporates such key factors when arranging teams. Thus, the goal of the
STCP is to partition a set of individuals into a set of synergistic teams:
teams that are diverse in personality and gender and whose members cover all
required competencies to complete a task. Furthermore, the STCP requires that
all teams are balanced in that they are expected to exhibit similar
performances when completing the task. We propose two efficient algorithms to
solve the STCP. Our first algorithm is based on a linear programming
formulation and is appropriate to solve small instances of the problem. Our
second algorithm is an anytime heuristic that is effective for large instances
of the STCP. Finally, we thoroughly study the computational properties of both
algorithms in an educational context when grouping students in a classroom into
teams using actual-world data.
| [
{
"version": "v1",
"created": "Thu, 26 Sep 2019 09:24:25 GMT"
}
] | 1,569,542,400,000 | [
[
"Andrejczuk",
"Ewa",
""
],
[
"Bistaffa",
"Filippo",
""
],
[
"Blum",
"Christian",
""
],
[
"Rodríguez-Aguilar",
"Juan A.",
""
],
[
"Sierra",
"Carles",
""
]
] |
1909.12032 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek and S{\l}awomir T. Wierzcho\'n | Query Optimization Properties of Modified VBS | 7 pages, 2 figures; published as: M.A. K{\l}opotek, S.T. Wierzcho\'n:
Query optimization properties of modified Valuation-Based Systems. [in:] R.
Trappl Ed.: Cybernetics and Systems . Proc. 13th European Meeting on
Cybernetics and System Research, Vienna, 9-12 April 1996, Vol. I. Austrian
Society for Cybernetic Studies, 1996, pp. 335-340 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Valuation-Based~System can represent knowledge in different domains including
probability theory, Dempster-Shafer theory and possibility theory. More recent
studies show that the framework of VBS is also appropriate for representing and
solving Bayesian decision problems and optimization problems.
In this paper after introducing the valuation based system (VBS) framework,
we present Markov-like properties of VBS and a method for resolving queries to
VBS.
| [
{
"version": "v1",
"created": "Thu, 26 Sep 2019 11:23:41 GMT"
}
] | 1,569,542,400,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
],
[
"Wierzchoń",
"Sławomir T.",
""
]
] |
1909.12104 | Blai Bonet | Blai Bonet and Hector Geffner | Action Selection for MDPs: Anytime AO* vs. UCT | Proceedings AAAI-12 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the presence of non-admissible heuristics, A* and other best-first
algorithms can be converted into anytime optimal algorithms over OR graphs, by
simply continuing the search after the first solution is found. The same trick,
however, does not work for best-first algorithms over AND/OR graphs, that must
be able to expand leaf nodes of the explicit graph that are not necessarily
part of the best partial solution. Anytime optimal variants of AO* must thus
address an exploration-exploitation tradeoff: they cannot just "exploit", they
must keep exploring as well. In this work, we develop one such variant of AO*
and apply it to finite-horizon MDPs. This Anytime AO* algorithm eventually
delivers an optimal policy while using non-admissible random heuristics that
can be sampled, as when the heuristic is the cost of a base policy that can be
sampled with rollouts. We then test Anytime AO* for action selection over large
infinite-horizon MDPs that cannot be solved with existing off-line heuristic
search and dynamic programming algorithms, and compare it with UCT.
| [
{
"version": "v1",
"created": "Thu, 26 Sep 2019 13:51:26 GMT"
}
] | 1,569,542,400,000 | [
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
1909.12142 | Blai Bonet | Florian Pommerening, Malte Helmert, Blai Bonet | Higher-Dimensional Potential Heuristics for Optimal Classical Planning | Proceedings AAAI-17 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Potential heuristics for state-space search are defined as weighted sums over
simple state features. Atomic features consider the value of a single state
variable in a factored state representation, while binary features consider
joint assignments to two state variables. Previous work showed that the set of
all admissible and consistent potential heuristics using atomic features can be
characterized by a compact set of linear constraints. We generalize this result
to binary features and prove a hardness result for features of higher
dimension. Furthermore, we prove a tractability result based on the treewidth
of a new graphical structure we call the context-dependency graph. Finally, we
study the relationship of potential heuristics to transition cost partitioning.
Experimental results show that binary potential heuristics are significantly
more informative than the previously considered atomic ones.
| [
{
"version": "v1",
"created": "Thu, 26 Sep 2019 14:24:17 GMT"
}
] | 1,569,542,400,000 | [
[
"Pommerening",
"Florian",
""
],
[
"Helmert",
"Malte",
""
],
[
"Bonet",
"Blai",
""
]
] |
1909.12465 | Hua Huang | Hua Huang, Adrian Barbu | Playing Atari Ball Games with Hierarchical Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human beings are particularly good at reasoning and inference from just a few
examples. When facing new tasks, humans will leverage knowledge and skills
learned before, and quickly integrate them with the new task. In addition to
learning by experimentation, human also learn socio-culturally through
instructions and learning by example. In this way humans can learn much faster
compared with most current artificial intelligence algorithms in many tasks. In
this paper, we test the idea of speeding up machine learning through social
learning. We argue that in solving real-world problems, especially when the
task is designed by humans, and/or for humans, there are typically instructions
from user manuals and/or human experts which give guidelines on how to better
accomplish the tasks. We argue that these instructions have tremendous value in
designing a reinforcement learning system which can learn in human fashion, and
we test the idea by playing the Atari games Tennis and Pong. We experimentally
demonstrate that the instructions provide key information about the task, which
can be used to decompose the learning task into sub-systems and construct
options for the temporally extended planning, and dramatically accelerate the
learning process.
| [
{
"version": "v1",
"created": "Fri, 27 Sep 2019 02:09:34 GMT"
}
] | 1,569,801,600,000 | [
[
"Huang",
"Hua",
""
],
[
"Barbu",
"Adrian",
""
]
] |
1909.13430 | Toby Walsh | Ian Gent, Toby Walsh | CSPLib: Twenty Years On | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 1999, we introduced CSPLib, a benchmark library for the constraints
community. Our CP-1999 poster paper about CSPLib discussed the advantages and
disadvantages of building such a library. Unlike some other domains such as
theorem proving, or machine learning, representation was then and remains today
a major issue in the success or failure to solve problems. Benchmarks in CSPLib
are therefore specified in natural language as this allows users to find good
representations for themselves. The community responded positively and CSPLib
has become a valuable resource but, as we discuss here, we cannot rest.
| [
{
"version": "v1",
"created": "Mon, 30 Sep 2019 02:24:09 GMT"
}
] | 1,569,888,000,000 | [
[
"Gent",
"Ian",
""
],
[
"Walsh",
"Toby",
""
]
] |
1909.13485 | Joseph Y. Halpern | Joseph Y. Halpern | The Book of Why: Review | To appear in "Artificial Intelligence" journal | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is a review of "The Book of Why", by Judea Pearl.
| [
{
"version": "v1",
"created": "Mon, 30 Sep 2019 07:11:50 GMT"
}
] | 1,569,888,000,000 | [
[
"Halpern",
"Joseph Y.",
""
]
] |
1909.13778 | Blai Bonet | Blai Bonet and Hector Geffner | Causal Belief Decomposition for Planning with Sensing: Completeness
Results and Practical Approximation | Proceedings IJCAI-13 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Belief tracking is a basic problem in planning with sensing. While the
problem is intractable, it has been recently shown that for both deterministic
and non-deterministic systems expressed in compact form, it can be done in time
and space that are exponential in the problem width. The width measures the
maximum number of state variables that are all relevant to a given precondition
or goal. In this work, we extend this result both theoretically and
practically. First, we introduce an alternative decomposition scheme and
algorithm with the same time complexity but different completeness guarantees,
whose space complexity is much smaller: exponential in the causal width of the
problem that measures the number of state variables that are causally relevant
to a given precondition, goal, or observable. Second, we introduce a fast,
meaningful, and powerful approximation that trades completeness by speed, and
is both time and space exponential in the problem causal width. It is then
shown empirically that the algorithm combined with simple heuristics yields
state-of-the-art real-time performance in domains with high widths but low
causal widths such as Minesweeper, Battleship, and Wumpus.
| [
{
"version": "v1",
"created": "Thu, 26 Sep 2019 13:53:21 GMT"
}
] | 1,569,888,000,000 | [
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
1909.13779 | Blai Bonet | Blai Bonet and Hector Geffner | Factored Probabilistic Belief Tracking | Proceedings IJCAI-13 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of belief tracking in the presence of stochastic actions and
observations is pervasive and yet computationally intractable. In this work we
show however that probabilistic beliefs can be maintained in factored form
exactly and efficiently across a number of causally closed beams, when the
state variables that appear in more than one beam obey a form of backward
determinism. Since computing marginals from the factors is still
computationally intractable in general, and variables appearing in several
beams are not always backward-deterministic, the basic formulation is extended
with two approximations: forms of belief propagation for computing marginals
from factors, and sampling of non-backward-deterministic variables for making
such variables backward-deterministic given their sampled history. Unlike,
Rao-Blackwellized particle-filtering, the sampling is not used for making
inference tractable but for making the factorization sound. The resulting
algorithm involves sampling and belief propagation or just one of them as
determined by the structure of the model.
| [
{
"version": "v1",
"created": "Thu, 26 Sep 2019 12:48:25 GMT"
}
] | 1,569,888,000,000 | [
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
1910.00057 | Goutham Ramakrishnan | Goutham Ramakrishnan, Yun Chan Lee, Aws Albarghouthi | Synthesizing Action Sequences for Modifying Model Decisions | null | null | 10.1609/aaai.v34i04.5996 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When a model makes a consequential decision, e.g., denying someone a loan, it
needs to additionally generate actionable, realistic feedback on what the
person can do to favorably change the decision. We cast this problem through
the lens of program synthesis, in which our goal is to synthesize an optimal
(realistically cheapest or simplest) sequence of actions that if a person
executes successfully can change their classification. We present a novel and
general approach that combines search-based program synthesis and test-time
adversarial attacks to construct action sequences over a domain-specific set of
actions. We demonstrate the effectiveness of our approach on a number of deep
neural networks.
| [
{
"version": "v1",
"created": "Mon, 30 Sep 2019 18:57:13 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Oct 2019 14:03:48 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Oct 2019 16:22:00 GMT"
}
] | 1,655,856,000,000 | [
[
"Ramakrishnan",
"Goutham",
""
],
[
"Lee",
"Yun Chan",
""
],
[
"Albarghouthi",
"Aws",
""
]
] |
1910.00089 | Marco Pegoraro | Marco Pegoraro and Wil M.P. van der Aalst | Mining Uncertain Event Data in Process Mining | 18 pages, 7 figures, 3 tables, 13 references | ICPM (2019) 89-96 | 10.1109/ICPM.2019.00023 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, more and more process data are automatically recorded by
information systems, and made available in the form of event logs. Process
mining techniques enable process-centric analysis of data, including
automatically discovering process models and checking if event data conform to
a certain model. In this paper we analyze the previously unexplored setting of
uncertain event logs: logs where quantified uncertainty is recorded together
with the corresponding data. We define a taxonomy of uncertain event logs and
models, and we examine the challenges that uncertainty poses on process
discovery and conformance checking. Finally, we show how upper and lower bounds
for conformance can be obtained aligning an uncertain trace onto a regular
process model.
| [
{
"version": "v1",
"created": "Fri, 20 Sep 2019 08:38:52 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Feb 2020 09:28:00 GMT"
},
{
"version": "v3",
"created": "Mon, 9 Mar 2020 15:53:22 GMT"
},
{
"version": "v4",
"created": "Fri, 8 Apr 2022 08:57:34 GMT"
}
] | 1,649,635,200,000 | [
[
"Pegoraro",
"Marco",
""
],
[
"van der Aalst",
"Wil M. P.",
""
]
] |
1910.00128 | Toby Walsh | Toby Walsh | SAT vs CSP: a commentary | See https://freuder.wordpress.com/cp-anniversary-project/ | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 2000, I published a relatively comprehensive study of mappings between
propositional satisfiability (SAT) and constraint satisfaction problems (CSPs)
[Wal00]. I analysed four different mappings of SAT problems into CSPs, and two
of CSPs into SAT problems. For each mapping, I compared the impact of achieving
arc-consistency on the CSP with unit propagation on the corresponding SAT
problems, and lifted these results to CSP algorithms that maintain (some level
of ) arc-consistency during search like FC and MAC, and to the Davis- Putnam
procedure (which performs unit propagation at each search node). These results
helped provide some insight into the relationship between propositional
satisfiability and constraint satisfaction that set the scene for an important
and valuable body of work that followed. I discuss here what prompted the
paper, and what followed.
| [
{
"version": "v1",
"created": "Fri, 27 Sep 2019 13:17:17 GMT"
}
] | 1,569,974,400,000 | [
[
"Walsh",
"Toby",
""
]
] |
1910.00309 | Marek Szyku{\l}a | Jakub Kowalski, Maksymilian Mika, Jakub Sutowicz, Marek Szyku{\l}a | A note on the empirical comparison of RBG and Ludii | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an experimental comparison of the efficiency of three General Game
Playing systems in their current versions: Regular Boardgames (RBG 1.0),
Ludii~0.3.0, and a Game Description Language (GDL) propnet. We show that in
general, RBG is currently the fastest GGP system. For example, for chess, we
demonstrate that RBG is about 37 times faster than Ludii, and Ludii is about 3
times slower than a GDL propnet. Referring to the recent comparison [An
Empirical Evaluation of Two General Game Systems: Ludii and RBG, CoG 2019], we
show evidences that the benchmark presented there contains a number of
significant flaws that lead to wrong conclusions.
| [
{
"version": "v1",
"created": "Tue, 1 Oct 2019 11:24:02 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Oct 2019 17:52:21 GMT"
}
] | 1,570,406,400,000 | [
[
"Kowalski",
"Jakub",
""
],
[
"Mika",
"Maksymilian",
""
],
[
"Sutowicz",
"Jakub",
""
],
[
"Szykuła",
"Marek",
""
]
] |
1910.00571 | Felix Hill Mr | Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew
Botvinick, James L. McClelland and Adam Santoro | Environmental drivers of systematicity and generalization in a situated
agent | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The question of whether deep neural networks are good at generalising beyond
their immediate training experience is of critical importance for
learning-based approaches to AI. Here, we consider tests of out-of-sample
generalisation that require an agent to respond to never-seen-before
instructions by manipulating and positioning objects in a 3D Unity simulated
room. We first describe a comparatively generic agent architecture that
exhibits strong performance on these tests. We then identify three aspects of
the training regime and environment that make a significant difference to its
performance: (a) the number of object/word experiences in the training set; (b)
the visual invariances afforded by the agent's perspective, or frame of
reference; and (c) the variety of visual input inherent in the perceptual
aspect of the agent's perception. Our findings indicate that the degree of
generalisation that networks exhibit can depend critically on particulars of
the environment in which a given task is instantiated. They further suggest
that the propensity for neural networks to generalise in systematic ways may
increase if, like human children, those networks have access to many frames of
richly varying, multi-modal observations as they learn.
| [
{
"version": "v1",
"created": "Tue, 1 Oct 2019 17:51:45 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Oct 2019 12:06:37 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Feb 2020 13:02:19 GMT"
},
{
"version": "v4",
"created": "Wed, 19 Feb 2020 13:16:22 GMT"
}
] | 1,582,156,800,000 | [
[
"Hill",
"Felix",
""
],
[
"Lampinen",
"Andrew",
""
],
[
"Schneider",
"Rosalia",
""
],
[
"Clark",
"Stephen",
""
],
[
"Botvinick",
"Matthew",
""
],
[
"McClelland",
"James L.",
""
],
[
"Santoro",
"Adam",
""
]
] |
1910.00614 | Murugeswari Issakkimuthu | Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli | The Choice Function Framework for Online Policy Improvement | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are notable examples of online search improving over hand-coded or
learned policies (e.g. AlphaZero) for sequential decision making. It is not
clear, however, whether or not policy improvement is guaranteed for many of
these approaches, even when given a perfect evaluation function and transition
model. Indeed, simple counter examples show that seemingly reasonable online
search procedures can hurt performance compared to the original policy. To
address this issue, we introduce the choice function framework for analyzing
online search procedures for policy improvement. A choice function specifies
the actions to be considered at every node of a search tree, with all other
actions being pruned. Our main contribution is to give sufficient conditions
for stationary and non-stationary choice functions to guarantee that the value
achieved by online search is no worse than the original policy. In addition, we
describe a general parametric class of choice functions that satisfy those
conditions and present an illustrative use case of the framework's empirical
utility.
| [
{
"version": "v1",
"created": "Tue, 1 Oct 2019 18:41:55 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Oct 2019 16:35:38 GMT"
}
] | 1,570,492,800,000 | [
[
"Issakkimuthu",
"Murugeswari",
""
],
[
"Fern",
"Alan",
""
],
[
"Tadepalli",
"Prasad",
""
]
] |
1910.01380 | Zhe Hou | Hadrien Bride, Jin Song Dong, Ryan Green, Zhe Hou, Brendan Mahony and
Martin Oxenham | GRAVITAS: A Model Checking Based Planning and Goal Reasoning Framework
for Autonomous Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While AI techniques have found many successful applications in autonomous
systems, many of them permit behaviours that are difficult to interpret and may
lead to uncertain results. We follow the "verification as planning" paradigm
and propose to use model checking techniques to solve planning and goal
reasoning problems for autonomous systems. We give a new formulation of Goal
Task Network (GTN) that is tailored for our model checking based framework. We
then provide a systematic method that models GTNs in the model checker Process
Analysis Toolkit (PAT). We present our planning and goal reasoning system as a
framework called Goal Reasoning And Verification for Independent Trusted
Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy
plans in an uncertain environment. Finally, we demonstrate the proposed ideas
in an experiment that simulates a survey mission performed by the REMUS-100
autonomous underwater vehicle.
| [
{
"version": "v1",
"created": "Thu, 3 Oct 2019 10:09:04 GMT"
}
] | 1,570,147,200,000 | [
[
"Bride",
"Hadrien",
""
],
[
"Dong",
"Jin Song",
""
],
[
"Green",
"Ryan",
""
],
[
"Hou",
"Zhe",
""
],
[
"Mahony",
"Brendan",
""
],
[
"Oxenham",
"Martin",
""
]
] |
1910.01423 | Zeynep Kiziltan | Alan M. Frisch, Brahim Hnich, Zeynep Kiziltan, Ian Miguel, Toby Walsh | A Commentary on "Breaking Row and Column Symmetries in Matrix Models" | Appeared in the virtual volume celebrating the first 25 years of the
CP conference (https://freuder.wordpress.com/cp-anniversary-project/) | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The CP 2002 paper entitled "Breaking Row and Column Symmetries in Matrix
Models" by Flener et al.
(https://link.springer.com/chapter/10.1007%2F3-540-46135-3_31) describes some
of the first work for identifying and analyzing row and column symmetry in
matrix models and for efficiently and effectively dealing with such symmetry
using static symmetry-breaking ordering constraints. This commentary provides a
retrospective on that work and highlights some of the subsequent work on the
topic.
| [
{
"version": "v1",
"created": "Thu, 3 Oct 2019 12:08:35 GMT"
}
] | 1,570,147,200,000 | [
[
"Frisch",
"Alan M.",
""
],
[
"Hnich",
"Brahim",
""
],
[
"Kiziltan",
"Zeynep",
""
],
[
"Miguel",
"Ian",
""
],
[
"Walsh",
"Toby",
""
]
] |
1910.01539 | Sandra Zimmer | Uwe Petersohn, Sandra Zimmer, Jens Lehmann | Method for the semantic indexing of concept hierarchies, uniform
representation, use of relational database systems and generic and case-based
reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a method for semantic indexing and describes its
application in the field of knowledge representation. Starting point of the
semantic indexing is the knowledge represented by concept hierarchies. The goal
is to assign keys to nodes (concepts) that are hierarchically ordered and
syntactically and semantically correct. With the indexing algorithm, keys are
computed such that concepts are partially unifiable with all more specific
concepts and only semantically correct concepts are allowed to be added. The
keys represent terminological relationships. Correctness and completeness of
the underlying indexing algorithm are proven. The use of classical relational
databases for the storage of instances is described. Because of the uniform
representation, inference can be done using case-based reasoning and generic
problem solving methods.
| [
{
"version": "v1",
"created": "Thu, 3 Oct 2019 14:54:13 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Jun 2023 17:02:57 GMT"
}
] | 1,686,614,400,000 | [
[
"Petersohn",
"Uwe",
""
],
[
"Zimmer",
"Sandra",
""
],
[
"Lehmann",
"Jens",
""
]
] |
1910.01806 | Ekaterina Nikonova | Ekaterina Nikonova, Jakub Gemrot | Deep Q-Network for Angry Birds | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Angry Birds is a popular video game in which the player is provided with a
sequence of birds to shoot from a slingshot. The task of the game is to destroy
all green pigs with maximum possible score. Angry Birds appears to be a
difficult task to solve for artificially intelligent agents due to the
sequential decision-making, non-deterministic game environment, enormous state
and action spaces and requirement to differentiate between multiple birds,
their abilities and optimum tapping times. We describe the application of Deep
Reinforcement learning by implementing Double Dueling Deep Q-network to play
Angry Birds game. One of our main goals was to build an agent that is able to
compete with previous participants and humans on the first 21 levels. In order
to do so, we have collected a dataset of game frames that we used to train our
agent on. We present different approaches and settings for DQN agent. We
evaluate our agent using results of the previous participants of AIBirds
competition, results of volunteer human players and present the results of
AIBirds 2018 competition.
| [
{
"version": "v1",
"created": "Fri, 4 Oct 2019 06:11:45 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Oct 2019 09:29:15 GMT"
}
] | 1,571,097,600,000 | [
[
"Nikonova",
"Ekaterina",
""
],
[
"Gemrot",
"Jakub",
""
]
] |
1910.02140 | Abhishek Naik | Abhishek Naik, Roshan Shariff, Niko Yasui, Hengshuai Yao, Richard S.
Sutton | Discounted Reinforcement Learning Is Not an Optimization Problem | Accepted for presentation at the Optimization Foundations of
Reinforcement Learning Workshop at NeurIPS 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discounted reinforcement learning is fundamentally incompatible with function
approximation for control in continuing tasks. It is not an optimization
problem in its usual formulation, so when using function approximation there is
no optimal policy. We substantiate these claims, then go on to address some
misconceptions about discounting and its connection to the average reward
formulation. We encourage researchers to adopt rigorous optimization
approaches, such as maximizing average reward, for reinforcement learning in
continuing tasks.
| [
{
"version": "v1",
"created": "Fri, 4 Oct 2019 20:52:39 GMT"
},
{
"version": "v2",
"created": "Sat, 16 Nov 2019 04:21:29 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Nov 2019 07:28:55 GMT"
}
] | 1,574,899,200,000 | [
[
"Naik",
"Abhishek",
""
],
[
"Shariff",
"Roshan",
""
],
[
"Yasui",
"Niko",
""
],
[
"Yao",
"Hengshuai",
""
],
[
"Sutton",
"Richard S.",
""
]
] |
1910.02227 | Richard Evans | Richard Evans, Jose Hernandez-Orallo, Johannes Welbl, Pushmeet Kohli,
Marek Sergot | Making sense of sensory input | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper attempts to answer a central question in unsupervised learning:
what does it mean to "make sense" of a sensory sequence? In our formalization,
making sense involves constructing a symbolic causal theory that both explains
the sensory sequence and also satisfies a set of unity conditions. The unity
conditions insist that the constituents of the causal theory -- objects,
properties, and laws -- must be integrated into a coherent whole. On our
account, making sense of sensory input is a type of program synthesis, but it
is unsupervised program synthesis.
Our second contribution is a computer implementation, the Apperception
Engine, that was designed to satisfy the above requirements. Our system is able
to produce interpretable human-readable causal theories from very small amounts
of data, because of the strong inductive bias provided by the unity conditions.
A causal theory produced by our system is able to predict future sensor
readings, as well as retrodict earlier readings, and impute (fill in the blanks
of) missing sensory readings, in any combination.
We tested the engine in a diverse variety of domains, including cellular
automata, rhythms and simple nursery tunes, multi-modal binding problems,
occlusion tasks, and sequence induction intelligence tests. In each domain, we
test our engine's ability to predict future sensor values, retrodict earlier
sensor values, and impute missing sensory data. The engine performs well in all
these domains, significantly out-performing neural net baselines. We note in
particular that in the sequence induction intelligence tests, our system
achieved human-level performance. This is notable because our system is not a
bespoke system designed specifically to solve intelligence tests, but a
general-purpose system that was designed to make sense of any sensory sequence.
| [
{
"version": "v1",
"created": "Sat, 5 Oct 2019 07:48:55 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jul 2020 03:16:30 GMT"
}
] | 1,594,771,200,000 | [
[
"Evans",
"Richard",
""
],
[
"Hernandez-Orallo",
"Jose",
""
],
[
"Welbl",
"Johannes",
""
],
[
"Kohli",
"Pushmeet",
""
],
[
"Sergot",
"Marek",
""
]
] |
1910.02240 | Mingyang Geng | Mingyang Geng, Kele Xu, Yiying Li, Shuqi Liu, Bo Ding, Huaimin Wang | Attention-based Fault-tolerant Approach for Multi-agent Reinforcement
Learning Systems | 13 pages. arXiv admin note: text overlap with arXiv:1812.00922 by
other authors | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of multi-agent reinforcement learning systems is to provide
interacting agents with the ability to collaboratively learn and adapt to the
behavior of other agents. In many real-world applications, the agents can only
acquire a partial view of the world. However, in realistic settings, one or
more agents that show arbitrarily faulty or malicious behavior may suffice to
let the current coordination mechanisms fail. In this paper, we study a
practical scenario considering the security issues in the presence of agents
with arbitrarily faulty or malicious behavior. Under these circumstances,
learning an optimal policy becomes particularly challenging, even in the
unrealistic case that an agent's policy can be made conditional upon all other
agents' observations. To overcome these difficulties, we present an
Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and
relevant information for each agent at every time-step. The multi-head
attention mechanism enables the agents to learn effective communication
policies through experience concurrently to the action policies. Empirical
results have shown that FT-Attn beats previous state-of-the-art methods in some
complex environments and can adapt to various kinds of noisy environments
without tuning the complexity of the algorithm. Furthermore, FT-Attn can
effectively deal with the complex situation where an agent needs to reach
multiple agents' correct observation at the same time.
| [
{
"version": "v1",
"created": "Sat, 5 Oct 2019 09:51:04 GMT"
}
] | 1,570,665,600,000 | [
[
"Geng",
"Mingyang",
""
],
[
"Xu",
"Kele",
""
],
[
"Li",
"Yiying",
""
],
[
"Liu",
"Shuqi",
""
],
[
"Ding",
"Bo",
""
],
[
"Wang",
"Huaimin",
""
]
] |
1910.02481 | Yuan Yang | Yuan Yang, Le Song | Learn to Explain Efficiently via Neural Logic Inductive Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The capability of making interpretable and self-explanatory decisions is
essential for developing responsible machine learning systems. In this work, we
study the learning to explain problem in the scope of inductive logic
programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an
efficient differentiable ILP framework that learns first-order logic rules that
can explain the patterns in the data. In experiments, compared with the
state-of-the-art methods, we find NLIL can search for rules that are x10 times
longer while remaining x3 times faster. We also show that NLIL can scale to
large image datasets, i.e. Visual Genome, with 1M entities.
| [
{
"version": "v1",
"created": "Sun, 6 Oct 2019 17:20:31 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Nov 2019 23:00:27 GMT"
},
{
"version": "v3",
"created": "Tue, 18 Feb 2020 19:01:05 GMT"
}
] | 1,582,156,800,000 | [
[
"Yang",
"Yuan",
""
],
[
"Song",
"Le",
""
]
] |
1910.02486 | Orsolya Csisz\'ar | Orsolya Csisz\'ar, G\'abor Csisz\'ar, J\'ozsef Dombi | Interpretable neural networks based on continuous-valued logic and
multicriteria decision operators | null | j.knosys.2020 | 10.1016/j.knosys.2020.105972 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Combining neural networks with continuous logic and multicriteria decision
making tools can reduce the black box nature of neural models. In this study,
we show that nilpotent logical systems offer an appropriate mathematical
framework for a hybridization of continuous nilpotent logic and neural models,
helping to improve the interpretability and safety of machine learning. In our
concept, perceptrons model soft inequalities; namely membership functions and
continuous logical operators. We design the network architecture before
training, using continuous logical operators and multicriteria decision tools
with given weights working in the hidden layers. Designing the structure
appropriately leads to a drastic reduction in the number of parameters to be
learned. The theoretical basis offers a straightforward choice of activation
functions (the cutting function or its differentiable approximation, the
squashing function), and also suggests an explanation to the great success of
the rectified linear unit (ReLU). In this study, we focus on the architecture
of a hybrid model and introduce the building blocks for future application in
deep neural networks. The concept is illustrated with some toy examples taken
from an extended version of the tensorflow playground.
| [
{
"version": "v1",
"created": "Sun, 6 Oct 2019 18:20:59 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Feb 2020 07:39:08 GMT"
}
] | 1,588,291,200,000 | [
[
"Csiszár",
"Orsolya",
""
],
[
"Csiszár",
"Gábor",
""
],
[
"Dombi",
"József",
""
]
] |
1910.03743 | Haoran Wei | Haoran Wei, Yuanbo Wang, Lidia Mangu, Keith Decker | Model-based Reinforcement Learning for Predictions and Control for Limit
Order Books | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We build a profitable electronic trading agent with Reinforcement Learning
that places buy and sell orders in the stock market. An environment model is
built only with historical observational data, and the RL agent learns the
trading policy by interacting with the environment model instead of with the
real-market to minimize the risk and potential monetary loss. Trained in
unsupervised and self-supervised fashion, our environment model learned a
temporal and causal representation of the market in latent space through deep
neural networks. We demonstrate that the trading policy trained entirely within
the environment model can be transferred back into the real market and maintain
its profitability. We believe that this environment model can serve as a robust
simulator that predicts market movement as well as trade impact for further
studies.
| [
{
"version": "v1",
"created": "Wed, 9 Oct 2019 01:42:27 GMT"
}
] | 1,570,665,600,000 | [
[
"Wei",
"Haoran",
""
],
[
"Wang",
"Yuanbo",
""
],
[
"Mangu",
"Lidia",
""
],
[
"Decker",
"Keith",
""
]
] |
1910.03990 | Mihai Boicu | Gheorghe Tecuci, Dorin Marcu, Mihai Boicu, Steven Meckl, Chirag
Uttamsingh | Toward a Computational Theory of Evidence-Based Reasoning for
Instructable Cognitive Agents | Presented at AAAI FSS-19: Artificial Intelligence in Government and
Public Sector, Arlington, Virginia, USA. (8 pages) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evidence-based reasoning is at the core of many problem-solving and
decision-making tasks in a wide variety of domains. Generalizing from the
research and development of cognitive agents in several such domains, this
paper presents progress toward a computational theory for the development of
instructable cognitive agents for evidence-based reasoning tasks. The paper
also illustrates the application of this theory to the development of four
prototype cognitive agents in domains that are critical to the government and
the public sector. Two agents function as cognitive assistants, one in
intelligence analysis, and the other in science education. The other two agents
operate autonomously, one in cybersecurity and the other in intelligence,
surveillance, and reconnaissance. The paper concludes with the directions of
future research on the proposed computational theory.
| [
{
"version": "v1",
"created": "Wed, 9 Oct 2019 13:52:03 GMT"
}
] | 1,570,665,600,000 | [
[
"Tecuci",
"Gheorghe",
""
],
[
"Marcu",
"Dorin",
""
],
[
"Boicu",
"Mihai",
""
],
[
"Meckl",
"Steven",
""
],
[
"Uttamsingh",
"Chirag",
""
]
] |
1910.04040 | Matthias Hutsebaut-Buysse | Matthias Hutsebaut-Buysse, Kevin Mets, Steven Latr\'e | Fast Task-Adaptation for Tasks Labeled Using Natural Language in
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over its lifetime, a reinforcement learning agent is often tasked with
different tasks. How to efficiently adapt a previously learned control policy
from one task to another, remains an open research question. In this paper, we
investigate how instructions formulated in natural language can enable faster
and more effective task adaptation. This can serve as the basis for developing
language instructed skills, which can be used in a lifelong learning setting.
Our method is capable of assessing, given a set of developed base control
policies, which policy will adapt best to a new unseen task.
| [
{
"version": "v1",
"created": "Wed, 9 Oct 2019 15:01:05 GMT"
}
] | 1,570,665,600,000 | [
[
"Hutsebaut-Buysse",
"Matthias",
""
],
[
"Mets",
"Kevin",
""
],
[
"Latré",
"Steven",
""
]
] |
1910.04376 | Daochen Zha | Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei,
Junyu Guo, Xia Hu | RLCard: A Toolkit for Reinforcement Learning in Card Games | AAAI-20 Workshop on Reinforcement Learning in Games | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RLCard is an open-source toolkit for reinforcement learning research in card
games. It supports various card environments with easy-to-use interfaces,
including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong.
The goal of RLCard is to bridge reinforcement learning and imperfect
information games, and push forward the research of reinforcement learning in
domains with multiple agents, large state and action space, and sparse reward.
In this paper, we provide an overview of the key components in RLCard, a
discussion of the design principles, a brief introduction of the interfaces,
and comprehensive evaluations of the environments. The codes and documents are
available at https://github.com/datamllab/rlcard
| [
{
"version": "v1",
"created": "Thu, 10 Oct 2019 05:56:16 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Feb 2020 17:23:55 GMT"
}
] | 1,581,897,600,000 | [
[
"Zha",
"Daochen",
""
],
[
"Lai",
"Kwei-Herng",
""
],
[
"Cao",
"Yuanpu",
""
],
[
"Huang",
"Songyi",
""
],
[
"Wei",
"Ruzhe",
""
],
[
"Guo",
"Junyu",
""
],
[
"Hu",
"Xia",
""
]
] |
1910.04404 | J\"org P. M\"uller | Sarit Kraus, Amos Azaria, Jelena Fiosina, Maike Greve, Noam Hazon,
Lutz Kolbe, Tim-Benjamin Lembcke, J\"org P. M\"uller, S\"oren Schleibaum,
Mark Vollrath | AI for Explaining Decisions in Multi-Agent Environments | This paper has been submitted to the Blue Sky Track of the AAAI 2020
conference. At the time of submission, it is under review. The tentative
notification date will be November 10, 2019. Current version: Name of first
author had been added in metadata | null | 10.1609/aaai.v34i09.7077 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explanation is necessary for humans to understand and accept decisions made
by an AI system when the system's goal is known. It is even more important when
the AI system makes decisions in multi-agent environments where the human does
not know the systems' goals since they may depend on other agents' preferences.
In such situations, explanations should aim to increase user satisfaction,
taking into account the system's decision, the user's and the other agents'
preferences, the environment settings and properties such as fairness, envy and
privacy. Generating explanations that will increase user satisfaction is very
challenging; to this end, we propose a new research direction: xMASE. We then
review the state of the art and discuss research directions towards efficient
methodologies and algorithms for generating explanations that will increase
users' satisfaction from AI system's decisions in multi-agent environments.
| [
{
"version": "v1",
"created": "Thu, 10 Oct 2019 07:37:29 GMT"
},
{
"version": "v2",
"created": "Sat, 12 Oct 2019 21:20:35 GMT"
}
] | 1,614,297,600,000 | [
[
"Kraus",
"Sarit",
""
],
[
"Azaria",
"Amos",
""
],
[
"Fiosina",
"Jelena",
""
],
[
"Greve",
"Maike",
""
],
[
"Hazon",
"Noam",
""
],
[
"Kolbe",
"Lutz",
""
],
[
"Lembcke",
"Tim-Benjamin",
""
],
[
"Müller",
"Jörg P.",
""
],
[
"Schleibaum",
"Sören",
""
],
[
"Vollrath",
"Mark",
""
]
] |
1910.04527 | Vaishak Belle | Vaishak Belle | The Quest for Interpretable and Responsible Artificial Intelligence | This is a slightly edited version of an article to appear in The
Biochemist, Portland Press, October 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) provides many opportunities to improve private
and public life. Discovering patterns and structures in large troves of data in
an automated manner is a core component of data science, and currently drives
applications in computational biology, finance, law and robotics. However, such
a highly positive impact is coupled with significant challenges: How do we
understand the decisions suggested by these systems in order that we can trust
them? How can they be held accountable for those decisions?
In this short survey, we cover some of the motivations and trends in the area
that attempt to address such questions.
| [
{
"version": "v1",
"created": "Thu, 10 Oct 2019 12:56:14 GMT"
}
] | 1,570,752,000,000 | [
[
"Belle",
"Vaishak",
""
]
] |
1910.04872 | Rodolfo Corona | Rodolfo Corona, Stephan Alaniz, Zeynep Akata | Modeling Conceptual Understanding in Image Reference Games | Published in NeurIPS 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An agent who interacts with a wide population of other agents needs to be
aware that there may be variations in their understanding of the world.
Furthermore, the machinery which they use to perceive may be inherently
different, as is the case between humans and machines. In this work, we present
both an image reference game between a speaker and a population of listeners
where reasoning about the concepts other agents can comprehend is necessary and
a model formulation with this capability. We focus on reasoning about the
conceptual understanding of others, as well as adapting to novel gameplay
partners and dealing with differences in perceptual machinery. Our experiments
on three benchmark image/attribute datasets suggest that our learner indeed
encodes information directly pertaining to the understanding of other agents,
and that leveraging this information is crucial for maximizing gameplay
performance.
| [
{
"version": "v1",
"created": "Thu, 10 Oct 2019 21:06:47 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Nov 2019 05:36:54 GMT"
}
] | 1,574,208,000,000 | [
[
"Corona",
"Rodolfo",
""
],
[
"Alaniz",
"Stephan",
""
],
[
"Akata",
"Zeynep",
""
]
] |
1910.04999 | Javier Segovia Aguas | Javier Segovia-Aguas, Sergio Jim\'enez, Anders Jonsson | Generalized Planning With Procedural Domain Control Knowledge | ICAPS 2016, 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generalized planning is the task of generating a single solution that is
valid for a set of planning problems. In this paper we show how to represent
and compute generalized plans using procedural Domain Control Knowledge (DCK).
We define a {\it divide and conquer} approach that first generates the
procedural DCK solving a set of planning problems representative of certain
subtasks and then compile it as callable procedures of the overall generalized
planning problem. Our procedure calling mechanism allows nested and recursive
procedure calls and is implemented in PDDL so that classical planners can
compute and exploit procedural DCK. Experiments show that an off-the-shelf
classical planner, using procedural DCK as callable procedures, can compute
generalized plans in a wide range of domains including non-trivial ones, such
as sorting variable-size lists or DFS traversal of binary trees with variable
size.
| [
{
"version": "v1",
"created": "Fri, 11 Oct 2019 07:16:04 GMT"
}
] | 1,571,011,200,000 | [
[
"Segovia-Aguas",
"Javier",
""
],
[
"Jiménez",
"Sergio",
""
],
[
"Jonsson",
"Anders",
""
]
] |
1910.05126 | Gyunam Park | Gyunam Park and Minseok Song | Prediction-based Resource Allocation using Bayesian Neural Networks and
Minimum Cost and Maximum Flow Algorithm | The design of effect analysis on prediction accuracy is incomplete | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predictive business process monitoring aims at providing predictions about
running instances by analyzing logs of completed cases in a business process.
Recently, a lot of research focuses on increasing productivity and efficiency
in a business process by forecasting potential problems during its executions.
However, most of the studies lack suggesting concrete actions to improve the
process. They leave it up to the subjective judgment of a user. In this paper,
we propose a novel method to connect the results from predictive business
process monitoring to actual business process improvements. More in detail, we
optimize the resource allocation in a non-clairvoyant online environment, where
we have limited information required for scheduling, by exploiting the
predictions. The proposed method integrates the offline prediction model
construction that predicts the processing time and the next activity of an
ongoing instance using Bayesian Neural Networks (BNNs) with the online resource
allocation that is extended from the minimum cost and maximum flow algorithm.
To validate the proposed method, we performed experiments using an artificial
event log and a real-life event log from a global financial organization.
| [
{
"version": "v1",
"created": "Fri, 11 Oct 2019 12:35:12 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Sep 2021 00:01:10 GMT"
}
] | 1,632,355,200,000 | [
[
"Park",
"Gyunam",
""
],
[
"Song",
"Minseok",
""
]
] |
1910.06636 | Guillaume Escamocher | Guillaume Escamocher, Barry O'Sullivan | Solving Logic Grid Puzzles with an Algorithm that Imitates Human
Behavior | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present in this paper our solver for logic grid puzzles. The approach used
by our algorithm mimics the way a human would try to solve the same problem.
Every progress made during the solving process is accompanied by a detailed
explanation of our program's reasoning. Since this reasoning is based on the
same heuristics that a human would employ, the user can easily follow the given
explanation.
| [
{
"version": "v1",
"created": "Tue, 15 Oct 2019 10:18:07 GMT"
}
] | 1,571,184,000,000 | [
[
"Escamocher",
"Guillaume",
""
],
[
"O'Sullivan",
"Barry",
""
]
] |
1910.06718 | Rina Panigrahy | Rina Panigrahy | How does the Mind store Information? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How we store information in our mind has been a major intriguing open
question. We approach this question not from a physiological standpoint as to
how information is physically stored in the brain, but from a conceptual and
algorithm standpoint as to the right data structures to be used to organize and
index information. Here we propose a memory architecture directly based on the
recursive sketching ideas from the paper "Recursive Sketches for Modular Deep
Networks", ICML 2019 (arXiv:1905.12730), to store information in memory as
concise sketches. We also give a high level, informal exposition of the
recursive sketching idea from the paper that makes use of subspace embeddings
to capture deep network computations into a concise sketch. These sketches form
an implicit knowledge graph that can be used to find related information via
sketches from the past while processing an event.
| [
{
"version": "v1",
"created": "Thu, 3 Oct 2019 04:07:16 GMT"
}
] | 1,571,184,000,000 | [
[
"Panigrahy",
"Rina",
""
]
] |
1910.06902 | Kumar Sankar Ray | Sandip Paul, Kumar Sankar Ray, Diganta Saha | A Unified Framework for Nonmonotonic Reasoning with Vagueness and
Uncertainty | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An interval-valued fuzzy answer set programming paradigm is proposed for
nonmonotonic reasoning with vague and uncertain information. The set of
sub-intervals of $[0,1]$ is considered as truth-space. The intervals are
ordered using preorder-based truth and knowledge ordering. The preorder based
ordering is an enhanced version of bilattice-based ordering. The system can
represent and reason with prioritized rules, rules with exceptions. An
iterative method for answer set computation is proposed. The sufficient
conditions for termination of iterations are identified for a class of logic
programs using the notion of difference equations.
| [
{
"version": "v1",
"created": "Tue, 1 Oct 2019 10:18:40 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Jan 2020 08:58:49 GMT"
},
{
"version": "v3",
"created": "Fri, 3 Jan 2020 08:04:05 GMT"
},
{
"version": "v4",
"created": "Wed, 5 Aug 2020 12:11:43 GMT"
}
] | 1,596,672,000,000 | [
[
"Paul",
"Sandip",
""
],
[
"Ray",
"Kumar Sankar",
""
],
[
"Saha",
"Diganta",
""
]
] |
1910.07004 | Tomer Libal | Tomer Libal and Alexander Steen | The NAI Suite -- Drafting and Reasoning over Legal Texts | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A prototype for automated reasoning over legal texts, called NAI, is
presented. As an input, NAI accepts formalized logical representations of such
legal texts that can be created and curated using an integrated annotation
interface. The prototype supports automated reasoning over the given text
representation and multiple quality assurance procedures. The pragmatics of the
NAI suite as well its feasibility in practical applications is studied on a
fragment of the Smoking Prohibition (Children in Motor Vehicles) (Scotland) Act
2016 of the Scottish Parliament.
| [
{
"version": "v1",
"created": "Tue, 15 Oct 2019 18:57:11 GMT"
}
] | 1,571,270,400,000 | [
[
"Libal",
"Tomer",
""
],
[
"Steen",
"Alexander",
""
]
] |
1910.08137 | Christian Muise | Christian Muise, Tathagata Chakraborti, Shubham Agarwal, Ondrej
Bajgar, Arunima Chaudhary, Luis A. Lastras-Montano, Josef Ondrej, Miroslav
Vodolan, Charlie Wiecha | Planning for Goal-Oriented Dialogue Systems | 42 pages, 17 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating complex multi-turn goal-oriented dialogue agents is a difficult
problem that has seen a considerable focus from many leaders in the tech
industry, including IBM, Google, Amazon, and Microsoft. This is in large part
due to the rapidly growing market demand for dialogue agents capable of
goal-oriented behaviour. Due to the business process nature of these
conversations, end-to-end machine learning systems are generally not a viable
option, as the generated dialogue agents must be deployable and verifiable on
behalf of the businesses authoring them.
In this work, we propose a paradigm shift in the creation of goal-oriented
complex dialogue systems that dramatically eliminates the need for a designer
to manually specify a dialogue tree, which nearly all current systems have to
resort to when the interaction pattern falls outside standard patterns such as
slot filling. We propose a declarative representation of the dialogue agent to
be processed by state-of-the-art planning technology. Our proposed approach
covers all aspects of the process; from model solicitation to the execution of
the generated plans/dialogue agents. Along the way, we introduce novel planning
encodings for declarative dialogue synthesis, a variety of interfaces for
working with the specification as a dialogue architect, and a robust executor
for generalized contingent plans. We have created prototype implementations of
all components, and in this paper, we further demonstrate the resulting system
empirically.
| [
{
"version": "v1",
"created": "Thu, 17 Oct 2019 20:00:10 GMT"
}
] | 1,571,616,000,000 | [
[
"Muise",
"Christian",
""
],
[
"Chakraborti",
"Tathagata",
""
],
[
"Agarwal",
"Shubham",
""
],
[
"Bajgar",
"Ondrej",
""
],
[
"Chaudhary",
"Arunima",
""
],
[
"Lastras-Montano",
"Luis A.",
""
],
[
"Ondrej",
"Josef",
""
],
[
"Vodolan",
"Miroslav",
""
],
[
"Wiecha",
"Charlie",
""
]
] |
1910.08243 | Lee Martie | Lee Martie, Mohammad Arif Ul Alam, Gaoyuan Zhang, Ryan R. Anderson | Reflecting After Learning for Understanding | Presented at the Advances in Cognitive Systems conference
(http://www.cogsys.org/conference/2019) and to be published in the Advances
in Cognitive Systems journal (http://www.cogsys.org/journal) | Advances in Cognitive Systems 8 (2019) 53-71 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today, image classification is a common way for systems to process visual
content. Although neural network approaches to classification have seen great
progress in reducing error rates, it is not clear what this means for a
cognitive system that needs to make sense of the multiple and competing
predictions from its own classifiers. As a step to address this, we present a
novel framework that uses meta-reasoning and meta-operations to unify
predictions into abstractions, properties, or relationships. Using the
framework on images from ImageNet, we demonstrate systems that unify 41% to 46%
of predictions in general and unify 67% to 75% of predictions when the systems
can explain their conceptual differences. We also demonstrate a system in "the
wild" by feeding live video images through it and show it unifying 51% of
predictions in general and 69% of predictions when their differences can be
explained conceptually by the system. In a survey given to 24 participants, we
found that 87% of the unified predictions describe their corresponding images.
| [
{
"version": "v1",
"created": "Fri, 18 Oct 2019 03:37:30 GMT"
}
] | 1,581,379,200,000 | [
[
"Martie",
"Lee",
""
],
[
"Alam",
"Mohammad Arif Ul",
""
],
[
"Zhang",
"Gaoyuan",
""
],
[
"Anderson",
"Ryan R.",
""
]
] |
1910.08677 | Atiye Alaeddini | Atiye Alaeddini and Daniel Klein | Optimal Immunization Policy Using Dynamic Programming | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decisions in public health are almost always made in the context of
uncertainty. Policy makers are responsible for making important decisions,
faced with the daunting task of choosing from amongst many possible options.
This task is called planning under uncertainty, and is particularly acute when
addressing complex systems, such as issues of global health and development.
Uncertainty leads to cautious or incorrect decisions that cost time, money, and
human life. It is with this understanding that we pursue greater clarity on,
and methods to address optimal policy making in health. Decision making under
uncertainty is a challenging task, and all too often this uncertainty is
averaged away to simplify results for policy makers. Our goal in this work is
to implement dynamic programming which provides basis for compiling planning
results into reactive strategies. We present here a description of an AI-based
method and illustrate how this method can improve our ability to find an
optimal vaccination strategy. We model the problem as a partially observable
Markov decision process, POMDP and show how a re-active policy can be computed
using dynamic programming. In this paper, we developed a framework for optimal
health policy design in an uncertain dynamic setting. We apply a stochastic
dynamic programming approach to identify the optimal time to change the health
intervention policy and the value of decision relevant information for
improving the impact of the policy.
| [
{
"version": "v1",
"created": "Sat, 19 Oct 2019 01:52:52 GMT"
},
{
"version": "v2",
"created": "Mon, 18 May 2020 16:09:58 GMT"
}
] | 1,589,846,400,000 | [
[
"Alaeddini",
"Atiye",
""
],
[
"Klein",
"Daniel",
""
]
] |
1910.09311 | Giuseppe Giacopelli | Giuseppe Giacopelli | Studying Topology of Time Lines Graph leads to an alternative approach
to the Newcomb's Paradox | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Newcomb's paradox is one of the most known paradox in Game Theory about
the Oracles. We will define the graph associated to the time lines of the Game.
After this Studying its topology and using only the Expected Utility Principle
we will formulate a solution of the paradox able to explain all the classical
cases.
| [
{
"version": "v1",
"created": "Tue, 15 Oct 2019 18:03:11 GMT"
}
] | 1,571,702,400,000 | [
[
"Giacopelli",
"Giuseppe",
""
]
] |
1910.09437 | Tahar M Kechadi | M-Tahar Kechadi, Kok Seng Low, G.Goncalves | Recurrent neural network approach for cyclic job shop scheduling problem | Journal of Manufacturing Systems, Volume 32, Issue 4, October 2013,
Pages 689-699 | null | 10.1016/j.jmsy.2013.02.001 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While cyclic scheduling is involved in numerous real-world applications,
solving the derived problem is still of exponential complexity. This paper
focuses specifically on modelling the manufacturing application as a cyclic job
shop problem and we have developed an efficient neural network approach to
minimise the cycle time of a schedule. Our approach introduces an interesting
model for a manufacturing production, and it is also very efficient, adaptive
and flexible enough to work with other techniques. Experimental results
validated the approach and confirmed our hypotheses about the system model and
the efficiency of neural networks for such a class of problems.
| [
{
"version": "v1",
"created": "Mon, 21 Oct 2019 15:13:48 GMT"
}
] | 1,571,702,400,000 | [
[
"Kechadi",
"M-Tahar",
""
],
[
"Low",
"Kok Seng",
""
],
[
"Goncalves",
"G.",
""
]
] |
1910.09755 | Yash Pote | Yash Pote, Saurabh Joshi and Kuldeep S. Meel | Phase Transition Behavior of Cardinality and XOR Constraints | null | https://doi.org/10.24963/ijcai.2019/162 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The runtime performance of modern SAT solvers is deeply connected to the
phase transition behavior of CNF formulas. While CNF solving has witnessed
significant runtime improvement over the past two decades, the same does not
hold for several other classes such as the conjunction of cardinality and XOR
constraints, denoted as CARD-XOR formulas. The problem of determining the
satisfiability of CARD-XOR formulas is a fundamental problem with a wide
variety of applications ranging from discrete integration in the field of
artificial intelligence to maximum likelihood decoding in coding theory. The
runtime behavior of random CARD-XOR formulas is unexplored in prior work. In
this paper, we present the first rigorous empirical study to characterize the
runtime behavior of 1-CARD-XOR formulas. We show empirical evidence of a
surprising phase-transition that follows a non-linear tradeoff between CARD and
XOR constraints.
| [
{
"version": "v1",
"created": "Tue, 22 Oct 2019 03:53:00 GMT"
}
] | 1,571,788,800,000 | [
[
"Pote",
"Yash",
""
],
[
"Joshi",
"Saurabh",
""
],
[
"Meel",
"Kuldeep S.",
""
]
] |
1910.09986 | Haodi Zhang | Haodi Zhang, Zihang Gao, Yi Zhou, Hao Zhang, Kaishun Wu, Fangzhen Lin | Faster and Safer Training by Embedding High-Level Knowledge into Deep
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning has been successfully used in many dynamic
decision making domains, especially those with very large state spaces.
However, it is also well-known that deep reinforcement learning can be very
slow and resource intensive. The resulting system is often brittle and
difficult to explain. In this paper, we attempt to address some of these
problems by proposing a framework of Rule-interposing Learning (RIL) that
embeds high level rules into the deep reinforcement learning. With some good
rules, this framework not only can accelerate the learning process, but also
keep it away from catastrophic explorations, thus making the system relatively
stable even during the very early stage of training. Moreover, given the rules
are high level and easy to interpret, they can be easily maintained, updated
and shared with other similar tasks.
| [
{
"version": "v1",
"created": "Tue, 22 Oct 2019 13:56:47 GMT"
}
] | 1,571,788,800,000 | [
[
"Zhang",
"Haodi",
""
],
[
"Gao",
"Zihang",
""
],
[
"Zhou",
"Yi",
""
],
[
"Zhang",
"Hao",
""
],
[
"Wu",
"Kaishun",
""
],
[
"Lin",
"Fangzhen",
""
]
] |
1910.10393 | Shilpesh Garg | Shilpesh Garg | RTOP: A Conceptual and Computational Framework for General Intelligence | 17 pages, added architecture and flow diagram | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A novel general intelligence model is proposed with three types of learning.
A unified sequence of the foreground percept trace and the command trace
translates into direct and time-hop observation paths to form the basis of Raw
learning. Raw learning includes the formation of image-image associations,
which lead to the perception of temporal and spatial relationships among
objects and object parts; and the formation of image-audio associations, which
serve as the building blocks of language. Offline identification of similar
segments in the observation paths and their subsequent reduction into a common
segment through merging of memory nodes leads to Generalized learning.
Generalization includes the formation of interpolated sensory nodes for robust
and generic matching, the formation of sensory properties nodes for specific
matching and superimposition, and the formation of group nodes for simpler
logic pathways. Online superimposition of memory nodes across multiple
predictions, primarily the superimposition of images on the internal projection
canvas, gives rise to Innovative learning and thought. The learning of actions
happens the same way as raw learning while the action determination happens
through the utility model built into the raw learnings, the utility function
being the pleasure and pain of the physical senses.
| [
{
"version": "v1",
"created": "Wed, 23 Oct 2019 07:40:19 GMT"
},
{
"version": "v2",
"created": "Sat, 11 Jan 2020 09:56:58 GMT"
}
] | 1,578,960,000,000 | [
[
"Garg",
"Shilpesh",
""
]
] |
1910.10547 | Tahar M Kechadi | Nhien-An Le-Khac, Lamine M. Aouad, M-Tahar Kechadi | Knowledge Map: Toward a New Approach Supporting the Knowledge Management
in Distributed Data Mining | Third International Conference on Autonomic and Autonomous Systems
(ICAS'07) | null | 10.1109/CONIELECOMP.2007.80 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distributed data mining (DDM) deals with the problem of finding patterns or
models, called knowledge, in an environment with distributed data and
computations. Today, a massive amounts of data which are often geographically
distributed and owned by different organisation are being mined. As
consequence, a large mount of knowledge are being produced. This causes
problems of not only knowledge management but also visualization in data
mining. Besides, the main aim of DDM is to exploit fully the benefit of
distributed data analysis while minimising the communication. Existing DDM
techniques perform partial analysis of local data at individual sites and then
generate a global model by aggregating these local results. These two steps are
not independent since naive approaches to local analysis may produce an
incorrect and ambiguous global data model. The integrating and cooperating of
these two steps need an effective knowledge management, concretely an efficient
map of knowledge in order to take the advantage of mined knowledge to guide
mining the data. In this paper, we present "knowledge map", a representation of
knowledge about mined knowledge. This new approach aims to manage efficiently
mined knowledge in large scale distributed platform such as Grid. This
knowledge map is used to facilitate not only the visualization, evaluation of
mining results but also the coordinating of local mining process and existing
knowledge to increase the accuracy of final model.
| [
{
"version": "v1",
"created": "Wed, 23 Oct 2019 13:14:18 GMT"
}
] | 1,571,875,200,000 | [
[
"Le-Khac",
"Nhien-An",
""
],
[
"Aouad",
"Lamine M.",
""
],
[
"Kechadi",
"M-Tahar",
""
]
] |
1910.12283 | Chen Joya | Xianfeng Liang, Likang Wu, Joya Chen, Yang Liu, Runlong Yu, Min Hou,
Han Wu, Yuyang Ye, Qi Liu, Enhong Chen | Long-term Joint Scheduling for Urban Traffic | KDD Cup 2019 Special PaddlePaddle Award | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, the traffic congestion in modern cities has become a growing worry
for the residents. As presented in Baidu traffic report, the commuting stress
index has reached surprising 1.973 in Beijing during rush hours, which results
in longer trip time and increased vehicular queueing. Previous works have
demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing
systems and optimized bus transportation, the traffic efficiency could be
significantly improved with little resource consumption. However, there are
still two disadvantages that restrict their performance: (1) they only consider
single scheduling in a short time, but ignoring the layout after first
reposition, and (2) they only focus on the single transport. However, the
multi-modal characteristics of urban public transportation are largely
under-exploited. In this paper, we propose an efficient and economical
multi-modal traffic scheduling scheme named JLRLS based on spatio -temporal
prediction, which adopts reinforcement learning to obtain optimal long-term and
joint schedule. In JLRLS, we combines multiple transportation to conduct
scheduling by their own characteristics, which potentially helps the system to
reach the optimal performance. Our implementation of an example by PaddlePaddle
is available at https://github.com/bigdata-ustc/Long-term-Joint-Scheduling,
with an explaining video at https://youtu.be/t5M2wVPhTyk.
| [
{
"version": "v1",
"created": "Sun, 27 Oct 2019 15:16:59 GMT"
}
] | 1,572,307,200,000 | [
[
"Liang",
"Xianfeng",
""
],
[
"Wu",
"Likang",
""
],
[
"Chen",
"Joya",
""
],
[
"Liu",
"Yang",
""
],
[
"Yu",
"Runlong",
""
],
[
"Hou",
"Min",
""
],
[
"Wu",
"Han",
""
],
[
"Ye",
"Yuyang",
""
],
[
"Liu",
"Qi",
""
],
[
"Chen",
"Enhong",
""
]
] |
1910.13012 | Nick Petosa | Nick Petosa and Tucker Balch | Multiplayer AlphaZero | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The AlphaZero algorithm has achieved superhuman performance in two-player,
deterministic, zero-sum games where perfect information of the game state is
available. This success has been demonstrated in Chess, Shogi, and Go where
learning occurs solely through self-play. Many real-world applications (e.g.,
equity trading) require the consideration of a multiplayer environment. In this
work, we suggest novel modifications of the AlphaZero algorithm to support
multiplayer environments, and evaluate the approach in two simple 3-player
games. Our experiments show that multiplayer AlphaZero learns successfully and
consistently outperforms a competing approach: Monte Carlo tree search. These
results suggest that our modified AlphaZero can learn effective strategies in
multiplayer game scenarios. Our work supports the use of AlphaZero in
multiplayer games and suggests future research for more complex environments.
| [
{
"version": "v1",
"created": "Tue, 29 Oct 2019 00:06:01 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Nov 2019 03:02:04 GMT"
},
{
"version": "v3",
"created": "Mon, 9 Dec 2019 06:20:54 GMT"
}
] | 1,575,936,000,000 | [
[
"Petosa",
"Nick",
""
],
[
"Balch",
"Tucker",
""
]
] |
1910.13513 | Minh Ho\`ang H\`a | Minh Ho\`ang H\`a, Tat Dat Nguyen, Thinh Nguyen Duy, Hoang Giang Pham,
Thuy Do, Louis-Martin Rousseau | A new constraint programming model and a linear programming-based
adaptive large neighborhood search for the vehicle routing problem with
synchronization constraints | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a vehicle routing problem which seeks to minimize cost subject to
time window and synchronization constraints. In this problem, the fleet of
vehicles is categorized into regular and special vehicles. Some customers
require both vehicles' services, whose starting service times at the customer
are synchronized. Despite its important real-world application, this problem
has rarely been studied in the literature. To solve the problem, we propose a
Constraint Programming (CP) model and an Adaptive Large Neighborhood Search
(ALNS) in which the design of insertion operators is based on solving linear
programming (LP) models to check the insertion feasibility. A number of
acceleration techniques is also proposed to significantly reduce the
computational time. The computational experiments show that our new CP model
finds better solutions than an existing CP-based ANLS, when used on small
instances with 25 customers and with a much shorter running time. Our LP-based
ALNS dominates the cp-ALNS, in terms of solution quality, when it provides
solutions with better objective values, on average, for all instance classes.
This demonstrates the advantage of using linear programming instead of
constraint programming when dealing with a variant of vehicle routing problems
with relatively tight constraints, which is often considered to be more
favorable for CP-based methods.
| [
{
"version": "v1",
"created": "Fri, 18 Oct 2019 03:55:05 GMT"
}
] | 1,572,480,000,000 | [
[
"Hà",
"Minh Hoàng",
""
],
[
"Nguyen",
"Tat Dat",
""
],
[
"Duy",
"Thinh Nguyen",
""
],
[
"Pham",
"Hoang Giang",
""
],
[
"Do",
"Thuy",
""
],
[
"Rousseau",
"Louis-Martin",
""
]
] |
1910.13701 | Aakash Maroti | Aakash Maroti | RBED: Reward Based Epsilon Decay | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | $\varepsilon$-greedy is a policy used to balance exploration and exploitation
in many reinforcement learning setting. In cases where the agent uses some
on-policy algorithm to learn optimal behaviour, it makes sense for the agent to
explore more initially and eventually exploit more as it approaches the target
behaviour. This shift from heavy exploration to heavy exploitation can be
represented as decay in the $\varepsilon$ value, where $\varepsilon$ depicts
the how much an agent is allowed to explore. This paper proposes a new approach
to this $\varepsilon$ decay where the decay is based on feedback from the
environment. This paper also compares and contrasts one such approach based on
rewards and compares it against standard exponential decay. The new approach,
in the environments tested, produces more consistent results that on average
perform better.
| [
{
"version": "v1",
"created": "Wed, 30 Oct 2019 07:28:33 GMT"
}
] | 1,572,480,000,000 | [
[
"Maroti",
"Aakash",
""
]
] |
1910.14217 | EPTCS | Shakil M. Khan (Ryerson University), Mikhail Soutchanski (Ryerson
University) | Towards A Logical Account of Epistemic Causality | In Proceedings CREST 2019, arXiv:1910.13641 | EPTCS 308, 2019, pp. 1-16 | 10.4204/EPTCS.308.1 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reasoning about observed effects and their causes is important in multi-agent
contexts. While there has been much work on causality from an objective
standpoint, causality from the point of view of some particular agent has
received much less attention. In this paper, we address this issue by
incorporating an epistemic dimension to an existing formal model of causality.
We define what it means for an agent to know the causes of an effect. Then
using a counterexample, we prove that epistemic causality is a different notion
from its objective counterpart.
| [
{
"version": "v1",
"created": "Thu, 31 Oct 2019 02:29:44 GMT"
}
] | 1,572,566,400,000 | [
[
"Khan",
"Shakil M.",
"",
"Ryerson University"
],
[
"Soutchanski",
"Mikhail",
"",
"Ryerson\n University"
]
] |
1911.00384 | Tuan Dam | Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen | Generalized Mean Estimation in Monte-Carlo Tree Search | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider Monte-Carlo Tree Search (MCTS) applied to Markov Decision
Processes (MDPs) and Partially Observable MDPs (POMDPs), and the well-known
Upper Confidence bound for Trees (UCT) algorithm. In UCT, a tree with nodes
(states) and edges (actions) is incrementally built by the expansion of nodes,
and the values of nodes are updated through a backup strategy based on the
average value of child nodes. However, it has been shown that with enough
samples the maximum operator yields more accurate node value estimates than
averaging. Instead of settling for one of these value estimates, we go a step
further proposing a novel backup strategy which uses the power mean operator,
which computes a value between the average and maximum value. We call our new
approach Power-UCT, and argue how the use of the power mean operator helps to
speed up the learning in MCTS. We theoretically analyze our method providing
guarantees of convergence to the optimum. Finally, we empirically demonstrate
the effectiveness of our method in well-known MDP and POMDP benchmarks, showing
significant improvement in performance and convergence speed w.r.t. state of
the art algorithms.
| [
{
"version": "v1",
"created": "Fri, 1 Nov 2019 14:02:36 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Jul 2020 14:40:08 GMT"
}
] | 1,594,684,800,000 | [
[
"Dam",
"Tuan",
""
],
[
"Klink",
"Pascal",
""
],
[
"D'Eramo",
"Carlo",
""
],
[
"Peters",
"Jan",
""
],
[
"Pajarinen",
"Joni",
""
]
] |
1911.00449 | Xixi Li | Yun Bai, Suling Jia, Xixi Li | Research and application of time series algorithms in centralized
purchasing data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Based on the online transaction data of COSCO group's centralized procurement
platform, this paper studies the clustering method of time series type data.
The different methods of similarity calculation, different clustering methods
with different K values are analysed, and the best clustering method suitable
for centralized purchasing data is determined. The company list under the
corresponding cluster is obtained. The time series motif discovery algorithm is
used to model the centroid of each cluster. Through ARIMA method, we also made
12 periods of prediction for the centroid of each category. This paper
constructs a matrix of "Customer Lifecycle Theory - Five Elements of Marketing
", and puts forward corresponding marketing suggestions for customers at
different life cycle stages.
| [
{
"version": "v1",
"created": "Fri, 1 Nov 2019 16:31:24 GMT"
}
] | 1,572,825,600,000 | [
[
"Bai",
"Yun",
""
],
[
"Jia",
"Suling",
""
],
[
"Li",
"Xixi",
""
]
] |
1911.01156 | Alun Preece | Frank Stein, Alun Preece | AAAI FSS-19: Artificial Intelligence in Government and Public Sector
Proceedings | Post-symposium proceedings including 18 papers | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Arlington, Virginia, USA, November 7-8, 2019
| [
{
"version": "v1",
"created": "Mon, 4 Nov 2019 12:26:51 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Nov 2019 08:07:11 GMT"
}
] | 1,575,244,800,000 | [
[
"Stein",
"Frank",
""
],
[
"Preece",
"Alun",
""
]
] |
1911.01157 | Luis Gal\'arraga | Luis Gal\'arraga and Julien Delaunay and Jean-Louis Dessalles | REMI: Mining Intuitive Referring Expressions on Knowledge Bases | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A referring expression (RE) is a description that identifies a set of
instances unambiguously. Mining REs from data finds applications in natural
language generation, algorithmic journalism, and data maintenance. Since there
may exist multiple REs for a given set of entities, it is common to focus on
the most intuitive ones, i.e., the most concise and informative. In this paper
we present REMI, a system that can mine intuitive REs on large RDF knowledge
bases. Our experimental evaluation shows that REMI finds REs deemed intuitive
by users. Moreover we show that REMI is several orders of magnitude faster than
an approach based on inductive logic programming.
| [
{
"version": "v1",
"created": "Mon, 4 Nov 2019 12:30:33 GMT"
}
] | 1,572,912,000,000 | [
[
"Galárraga",
"Luis",
""
],
[
"Delaunay",
"Julien",
""
],
[
"Dessalles",
"Jean-Louis",
""
]
] |
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