id
stringlengths 9
10
| submitter
stringlengths 5
47
⌀ | authors
stringlengths 5
1.72k
| title
stringlengths 11
234
| comments
stringlengths 1
491
⌀ | journal-ref
stringlengths 4
396
⌀ | doi
stringlengths 13
97
⌀ | report-no
stringlengths 4
138
⌀ | categories
stringclasses 1
value | license
stringclasses 9
values | abstract
stringlengths 29
3.66k
| versions
listlengths 1
21
| update_date
int64 1,180B
1,718B
| authors_parsed
sequencelengths 1
98
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1806.07709 | Anthony Young | Anthony Peter Young | Notes on Abstract Argumentation Theory | 100 pages, 39 figures, 6 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This note reviews Section 2 of Dung's seminal 1995 paper on abstract
argumentation theory. In particular, we clarify and make explicit all of the
proofs mentioned therein, and provide more examples to illustrate the
definitions, with the aim to help readers approaching abstract argumentation
theory for the first time. However, we provide minimal commentary and will
refer the reader to Dung's paper for the intuitions behind various concepts.
The appropriate mathematical prerequisites are provided in the appendices.
| [
{
"version": "v1",
"created": "Mon, 18 Jun 2018 22:30:19 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Jan 2019 22:24:00 GMT"
},
{
"version": "v3",
"created": "Sat, 22 Feb 2020 21:19:49 GMT"
},
{
"version": "v4",
"created": "Tue, 5 Apr 2022 20:59:45 GMT"
}
] | 1,649,289,600,000 | [
[
"Young",
"Anthony Peter",
""
]
] |
1806.07717 | Joerg Puehrer | Gerhard Brewka, J\"org P\"uhrer, Hannes Strass, Johannes P. Wallner,
Stefan Woltran | Weighted Abstract Dialectical Frameworks: Extended and Revised Report | This is an extended and corrected version of the paper Weighted
Abstract Dialectical Frameworks published in the Proceedings of the 32nd AAAI
Conference on Artificial Intelligence (AAAI 2018) | null | null | DBAI-TR-2018-110 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Abstract Dialectical Frameworks (ADFs) generalize Dung's argumentation
frameworks allowing various relationships among arguments to be expressed in a
systematic way. We further generalize ADFs so as to accommodate arbitrary
acceptance degrees for the arguments. This makes ADFs applicable in domains
where both the initial status of arguments and their relationship are only
insufficiently specified by Boolean functions. We define all standard ADF
semantics for the weighted case, including grounded, preferred and stable
semantics. We illustrate our approach using acceptance degrees from the unit
interval and show how other valuation structures can be integrated. In each
case it is sufficient to specify how the generalized acceptance conditions are
represented by formulas, and to specify the information ordering underlying the
characteristic ADF operator. We also present complexity results for problems
related to weighted ADFs.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2018 13:26:03 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Sep 2018 08:01:55 GMT"
}
] | 1,536,537,600,000 | [
[
"Brewka",
"Gerhard",
""
],
[
"Pührer",
"Jörg",
""
],
[
"Strass",
"Hannes",
""
],
[
"Wallner",
"Johannes P.",
""
],
[
"Woltran",
"Stefan",
""
]
] |
1806.08055 | Prashan Mathugama Babun Appuhamilage | Prashan Madumal, Tim Miller, Frank Vetere, Liz Sonenberg | Towards a Grounded Dialog Model for Explainable Artificial Intelligence | 15 pages, First international workshop on socio-cognitive systems at
Federated AI Meeting (FAIM) 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To generate trust with their users, Explainable Artificial Intelligence (XAI)
systems need to include an explanation model that can communicate the internal
decisions, behaviours and actions to the interacting humans. Successful
explanation involves both cognitive and social processes. In this paper we
focus on the challenge of meaningful interaction between an explainer and an
explainee and investigate the structural aspects of an explanation in order to
propose a human explanation dialog model. We follow a bottom-up approach to
derive the model by analysing transcripts of 398 different explanation dialog
types. We use grounded theory to code and identify key components of which an
explanation dialog consists. We carry out further analysis to identify the
relationships between components and sequences and cycles that occur in a
dialog. We present a generalized state model obtained by the analysis and
compare it with an existing conceptual dialog model of explanation.
| [
{
"version": "v1",
"created": "Thu, 21 Jun 2018 03:22:54 GMT"
}
] | 1,529,625,600,000 | [
[
"Madumal",
"Prashan",
""
],
[
"Miller",
"Tim",
""
],
[
"Vetere",
"Frank",
""
],
[
"Sonenberg",
"Liz",
""
]
] |
1806.08247 | Eric Verbeek | H.M.W. Verbeek and R. Medeiros de Carvalho | Log Skeletons: A Classification Approach to Process Discovery | 16 pages with 9 figures, followed by an appendix of 14 pages with 17
figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | To test the effectiveness of process discovery algorithms, a Process
Discovery Contest (PDC) has been set up. This PDC uses a classification
approach to measure this effectiveness: The better the discovered model can
classify whether or not a new trace conforms to the event log, the better the
discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art
fully-automated discovery algorithms score poorly on this classification. Even
the best of these algorithms, the Inductive Miner, scored only 147 correct
classified traces out of 200 traces on the PDC of 2017. This paper introduces
the rule-based log skeleton model, which is closely related to the Declare
constraint model, together with a way to classify traces using this model. This
classification using log skeletons is shown to score better on the PDC of 2017
than state-of-the-art discovery algorithms: 194 out of 200. As a result, one
can argue that the fully-automated algorithm to construct (or: discover) a log
skeleton from an event log outperforms existing state-of-the-art
fully-automated discovery algorithms.
| [
{
"version": "v1",
"created": "Thu, 21 Jun 2018 13:51:56 GMT"
}
] | 1,529,625,600,000 | [
[
"Verbeek",
"H. M. W.",
""
],
[
"de Carvalho",
"R. Medeiros",
""
]
] |
1806.08544 | Simon Lucas | Simon M. Lucas | Game AI Research with Fast Planet Wars Variants | To appear in Proceedings of IEEE Conference on Computational and
Games, 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a new implementation of Planet Wars, designed from the
outset for Game AI research. The skill-depth of the game makes it a challenge
for game-playing agents, and the speed of more than 1 million game ticks per
second enables rapid experimentation and prototyping. The parameterised nature
of the game together with an interchangeable actuator model make it well suited
to automated game tuning. The game is designed to be fun to play for humans,
and is directly playable by General Video Game AI agents.
| [
{
"version": "v1",
"created": "Fri, 22 Jun 2018 08:18:53 GMT"
}
] | 1,529,884,800,000 | [
[
"Lucas",
"Simon M.",
""
]
] |
1806.08554 | Bei Chen | Yihong Chen, Bei Chen, Xuguang Duan, Jian-Guang Lou, Yue Wang, Wenwu
Zhu, Yong Cao | Learning-to-Ask: Knowledge Acquisition via 20 Questions | Accepted by KDD 2018 | null | 10.1145/3219819.3220047 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Almost all the knowledge empowered applications rely upon accurate knowledge,
which has to be either collected manually with high cost, or extracted
automatically with unignorable errors. In this paper, we study 20 Questions, an
online interactive game where each question-response pair corresponds to a fact
of the target entity, to acquire highly accurate knowledge effectively with
nearly zero labor cost. Knowledge acquisition via 20 Questions predominantly
presents two challenges to the intelligent agent playing games with human
players. The first one is to seek enough information and identify the target
entity with as few questions as possible, while the second one is to leverage
the remaining questioning opportunities to acquire valuable knowledge
effectively, both of which count on good questioning strategies. To address
these challenges, we propose the Learning-to-Ask (LA) framework, within which
the agent learns smart questioning strategies for information seeking and
knowledge acquisition by means of deep reinforcement learning and generalized
matrix factorization respectively. In addition, a Bayesian approach to
represent knowledge is adopted to ensure robustness to noisy user responses.
Simulating experiments on real data show that LA is able to equip the agent
with effective questioning strategies, which result in high winning rates and
rapid knowledge acquisition. Moreover, the questioning strategies for
information seeking and knowledge acquisition boost the performance of each
other, allowing the agent to start with a relatively small knowledge set and
quickly improve its knowledge base in the absence of constant human
supervision.
| [
{
"version": "v1",
"created": "Fri, 22 Jun 2018 08:48:49 GMT"
}
] | 1,529,884,800,000 | [
[
"Chen",
"Yihong",
""
],
[
"Chen",
"Bei",
""
],
[
"Duan",
"Xuguang",
""
],
[
"Lou",
"Jian-Guang",
""
],
[
"Wang",
"Yue",
""
],
[
"Zhu",
"Wenwu",
""
],
[
"Cao",
"Yong",
""
]
] |
1806.08874 | Eray Ozkural | Eray \"Ozkural | The Foundations of Deep Learning with a Path Towards General
Intelligence | Submitted to AGI 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Like any field of empirical science, AI may be approached axiomatically. We
formulate requirements for a general-purpose, human-level AI system in terms of
postulates. We review the methodology of deep learning, examining the explicit
and tacit assumptions in deep learning research. Deep Learning methodology
seeks to overcome limitations in traditional machine learning research as it
combines facets of model richness, generality, and practical applicability. The
methodology so far has produced outstanding results due to a productive synergy
of function approximation, under plausible assumptions of irreducibility and
the efficiency of back-propagation family of algorithms. We examine these
winning traits of deep learning, and also observe the various known failure
modes of deep learning. We conclude by giving recommendations on how to extend
deep learning methodology to cover the postulates of general-purpose AI
including modularity, and cognitive architecture. We also relate deep learning
to advances in theoretical neuroscience research.
| [
{
"version": "v1",
"created": "Fri, 22 Jun 2018 22:52:12 GMT"
}
] | 1,529,971,200,000 | [
[
"Özkural",
"Eray",
""
]
] |
1806.08908 | Eray Ozkural | Eray \"Ozkural | Zeta Distribution and Transfer Learning Problem | Submitted to AGI 2018, pre-print | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore the relations between the zeta distribution and algorithmic
information theory via a new model of the transfer learning problem. The
program distribution is approximated by a zeta distribution with parameter near
$1$. We model the training sequence as a stochastic process. We analyze the
upper temporal bound for learning a training sequence and its entropy rates,
assuming an oracle for the transfer learning problem. We argue from empirical
evidence that power-law models are suitable for natural processes. Four
sequence models are proposed. Random typing model is like no-free lunch where
transfer learning does not work. Zeta process independently samples programs
from the zeta distribution. A model of common sub-programs inspired by genetics
uses a database of sub-programs. An evolutionary zeta process samples mutations
from Zeta distribution. The analysis of stochastic processes inspired by
evolution suggest that AI may be feasible in nature, countering no-free lunch
sort of arguments.
| [
{
"version": "v1",
"created": "Sat, 23 Jun 2018 04:47:37 GMT"
}
] | 1,529,971,200,000 | [
[
"Özkural",
"Eray",
""
]
] |
1806.08925 | Janardan Misra | Janardan Misra | An Inductive Formalization of Self Reproduction in Dynamical Hierarchies | Preprint of the paper appearing in proceedings of the 10th
International Conference on the Simulation and Synthesis of Living Systems
(ALife X), pp. 553-558, MIT Press, 2006 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Formalizing self reproduction in dynamical hierarchies is one of the
important problems in Artificial Life (AL) studies. We study, in this paper, an
inductively defined algebraic framework for self reproduction on macroscopic
organizational levels under dynamical system setting for simulated AL models
and explore some existential results. Starting with defining self reproduction
for atomic entities we define self reproduction with possible mutations on
higher organizational levels in terms of hierarchical sets and the
corresponding inductively defined `meta' - reactions. We introduce constraints
to distinguish a collection of entities from genuine cases of emergent
organizational structures.
| [
{
"version": "v1",
"created": "Sat, 23 Jun 2018 07:47:57 GMT"
}
] | 1,529,971,200,000 | [
[
"Misra",
"Janardan",
""
]
] |
1806.09328 | Conrad Sanderson | Majid Namazi, Conrad Sanderson, M.A. Hakim Newton, M.M.A. Polash,
Abdul Sattar | Diversified Late Acceptance Search | null | Lecture Notes in Computer Science, Vol. 11320, pp. 299-311, 2018 | 10.1007/978-3-030-03991-2_29 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome
the main downside of traditional Hill Climbing (HC) search, which is often
quickly trapped in a local optimum due to strictly accepting only non-worsening
moves within each iteration. In contrast, LAHC also accepts worsening moves, by
keeping a circular array of fitness values of previously visited solutions and
comparing the fitness values of candidate solutions against the least recent
element in the array. While this straightforward strategy has proven effective,
there are nevertheless situations where LAHC can unfortunately behave in a
similar manner to HC. For example, when a new local optimum is found, often the
same fitness value is stored many times in the array. To address this
shortcoming, we propose new acceptance and replacement strategies to take into
account worsening, improving, and sideways movement scenarios with the aim to
improve the diversity of values in the array. Compared to LAHC, the proposed
Diversified Late Acceptance Search approach is shown to lead to better quality
solutions that are obtained with a lower number of iterations on benchmark
Travelling Salesman Problems and Quadratic Assignment Problems.
| [
{
"version": "v1",
"created": "Mon, 25 Jun 2018 08:47:08 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Sep 2018 05:04:55 GMT"
},
{
"version": "v3",
"created": "Mon, 10 Dec 2018 04:17:27 GMT"
}
] | 1,544,486,400,000 | [
[
"Namazi",
"Majid",
""
],
[
"Sanderson",
"Conrad",
""
],
[
"Newton",
"M. A. Hakim",
""
],
[
"Polash",
"M. M. A.",
""
],
[
"Sattar",
"Abdul",
""
]
] |
1806.09455 | Hector Geffner | Tomas Geffner and Hector Geffner | Compact Policies for Fully-Observable Non-Deterministic Planning as SAT | null | Proc. ICAPS 2018 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fully observable non-deterministic (FOND) planning is becoming increasingly
important as an approach for computing proper policies in probabilistic
planning, extended temporal plans in LTL planning, and general plans in
generalized planning. In this work, we introduce a SAT encoding for FOND
planning that is compact and can produce compact strong cyclic policies. Simple
variations of the encodings are also introduced for strong planning and for
what we call, dual FOND planning, where some non-deterministic actions are
assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial).
The resulting FOND planners are compared empirically with existing planners
over existing and new benchmarks. The notion of "probabilistic interesting
problems" is also revisited to yield a more comprehensive picture of the
strengths and limitations of current FOND planners and the proposed SAT
approach.
| [
{
"version": "v1",
"created": "Mon, 25 Jun 2018 13:51:04 GMT"
}
] | 1,529,971,200,000 | [
[
"Geffner",
"Tomas",
""
],
[
"Geffner",
"Hector",
""
]
] |
1806.09487 | Pavel Surynek | Pavel Surynek | Finding Optimal Solutions to Token Swapping by Conflict-based Search and
Reduction to SAT | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study practical approaches to solving the token swapping (TSWAP) problem
optimally in this short paper. In TSWAP, we are given an undirected graph with
colored vertices. A colored token is placed in each vertex. A pair of tokens
can be swapped between adjacent vertices. The goal is to perform a sequence of
swaps so that token and vertex colors agree across the graph. The minimum
number of swaps is required in the optimization variant of the problem. We
observed similarities between the TSWAP problem and multi-agent path finding
(MAPF) where instead of tokens we have multiple agents that need to be moved
from their current vertices to given unique target vertices. The difference
between both problems consists in local conditions that state transitions
(swaps/moves) must satisfy. We developed two algorithms for solving TSWAP
optimally by adapting two different approaches to MAPF - CBS and MDD- SAT. This
constitutes the first attempt to design optimal solving algorithms for TSWAP.
Experimental evaluation on various types of graphs shows that the reduction to
SAT scales better than CBS in optimal TSWAP solving.
| [
{
"version": "v1",
"created": "Mon, 25 Jun 2018 14:28:09 GMT"
}
] | 1,529,971,200,000 | [
[
"Surynek",
"Pavel",
""
]
] |
1806.09506 | Rita Gitik | Rita Gitik | Optimal Seeding and Self-Reproduction from a Mathematical Point of View | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | P. Kabamba developed generation theory as a tool for studying
self-reproducing systems. We provide an alternative definition of a generation
system and give a complete solution to the problem of finding optimal seeds for
a finite self-replicating system. We also exhibit examples illustrating a
connection between self-replication and fixed-point theory.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2018 15:39:21 GMT"
},
{
"version": "v2",
"created": "Sun, 11 Dec 2022 20:37:45 GMT"
}
] | 1,670,889,600,000 | [
[
"Gitik",
"Rita",
""
]
] |
1806.09612 | Arindam Chaudhuri AC | Arindam Chaudhuri | Predictive Maintenance for Industrial IoT of Vehicle Fleets using
Hierarchical Modified Fuzzy Support Vector Machine | Research work done at Samsung R & D Institute Delhi India | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Connected vehicle fleets are deployed worldwide in several industrial IoT
scenarios. With the gradual increase of machines being controlled and managed
through networked smart devices, the predictive maintenance potential grows
rapidly. Predictive maintenance has the potential of optimizing uptime as well
as performance such that time and labor associated with inspections and
preventive maintenance are reduced. In order to understand the trends of
vehicle faults with respect to important vehicle attributes viz mileage, age,
vehicle type etc this problem is addressed through hierarchical modified fuzzy
support vector machine (HMFSVM). The proposed method is compared with other
commonly used approaches like logistic regression, random forests and support
vector machines. This helps better implementation of telematics data to ensure
preventative management as part of the desired solution. The superiority of the
proposed method is highlighted through several experimental results.
| [
{
"version": "v1",
"created": "Sun, 24 Jun 2018 18:09:52 GMT"
}
] | 1,530,057,600,000 | [
[
"Chaudhuri",
"Arindam",
""
]
] |
1806.09771 | Zhengxing Chen | Zhengxing Chen, Chris Amato, Truong-Huy Nguyen, Seth Cooper, Yizhou
Sun, Magy Seif El-Nasr | Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games | CIG 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deck building is a crucial component in playing Collectible Card Games
(CCGs). The goal of deck building is to choose a fixed-sized subset of cards
from a large card pool, so that they work well together in-game against
specific opponents. Existing methods either lack flexibility to adapt to
different opponents or require large computational resources, still making them
unsuitable for any real-time or large-scale application. We propose a new deck
recommendation system, named Q-DeckRec, which learns a deck search policy
during a training phase and uses it to solve deck building problem instances.
Our experimental results demonstrate Q-DeckRec requires less computational
resources to build winning-effective decks after a training phase compared to
several baseline methods.
| [
{
"version": "v1",
"created": "Tue, 26 Jun 2018 02:55:16 GMT"
}
] | 1,530,057,600,000 | [
[
"Chen",
"Zhengxing",
""
],
[
"Amato",
"Chris",
""
],
[
"Nguyen",
"Truong-Huy",
""
],
[
"Cooper",
"Seth",
""
],
[
"Sun",
"Yizhou",
""
],
[
"El-Nasr",
"Magy Seif",
""
]
] |
1806.09785 | Richard Diehl Martinez | Rooz Mahdavian, Richard Diehl Martinez | Theory of Machine Networks: A Case Study | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a simplification of the Theory-of-Mind Network architecture, which
focuses on modeling complex, deterministic machines as a proxy for modeling
nondeterministic, conscious entities. We then validate this architecture in the
context of understanding engines, which, we argue, meet the required internal
and external complexity to yield meaningful abstractions.
| [
{
"version": "v1",
"created": "Tue, 26 Jun 2018 04:13:25 GMT"
}
] | 1,530,057,600,000 | [
[
"Mahdavian",
"Rooz",
""
],
[
"Martinez",
"Richard Diehl",
""
]
] |
1806.09954 | Arthur Bit-Monnot | Arthur Bit-Monnot | A Constraint-based Encoding for Domain-Independent Temporal Planning | null | null | 10.1007/978-3-319-98334-9_3 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a general constraint-based encoding for domain-independent task
planning. Task planning is characterized by causal relationships expressed as
conditions and effects of optional actions. Possible actions are typically
represented by templates, where each template can be instantiated into a number
of primitive actions. While most previous work for domain-independent task
planning has focused on primitive actions in a state-oriented view, our
encoding uses a fully lifted representation at the level of action templates.
It follows a time-oriented view in the spirit of previous work in
constraint-based scheduling. As a result, the proposed encoding is simple and
compact as it grows with the number of actions in a solution plan rather than
the number of possible primitive actions. When solved with an SMT solver, we
show that the proposed encoding is slightly more efficient than
state-of-the-art methods on temporally constrained planning benchmarks while
clearly outperforming other fully constraint-based approaches.
| [
{
"version": "v1",
"created": "Tue, 26 Jun 2018 12:58:59 GMT"
}
] | 1,603,756,800,000 | [
[
"Bit-Monnot",
"Arthur",
""
]
] |
1806.10322 | Nicolas Berberich | Nicolas Berberich, Klaus Diepold | The Virtuous Machine - Old Ethics for New Technology? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern AI and robotic systems are characterized by a high and ever-increasing
level of autonomy. At the same time, their applications in fields such as
autonomous driving, service robotics and digital personal assistants move
closer to humans. From the combination of both developments emerges the field
of AI ethics which recognizes that the actions of autonomous machines entail
moral dimensions and tries to answer the question of how we can build moral
machines. In this paper we argue for taking inspiration from Aristotelian
virtue ethics by showing that it forms a suitable combination with modern AI
due to its focus on learning from experience. We furthermore propose that
imitation learning from moral exemplars, a central concept in virtue ethics,
can solve the value alignment problem. Finally, we show that an intelligent
system endowed with the virtues of temperance and friendship to humans would
not pose a control problem as it would not have the desire for limitless
self-improvement.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2018 07:40:24 GMT"
}
] | 1,530,144,000,000 | [
[
"Berberich",
"Nicolas",
""
],
[
"Diepold",
"Klaus",
""
]
] |
1806.10449 | Marco Valtorta | Mohammad Ali Javidian and Marco Valtorta | A Proof of the Front-Door Adjustment Formula | Six figures and an ancillary document consisting of 53 slides | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide a proof of the the Front-Door adjustment formula using the
do-calculus.
| [
{
"version": "v1",
"created": "Mon, 25 Jun 2018 21:01:03 GMT"
}
] | 1,530,144,000,000 | [
[
"Javidian",
"Mohammad Ali",
""
],
[
"Valtorta",
"Marco",
""
]
] |
1806.10561 | Liangda Fang | Liangda Fang and Kewen Wang and Zhe Wang and Ximing Wen | Knowledge Compilation in Multi-Agent Epistemic Logics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epistemic logics are a primary formalism for multi-agent systems but major
reasoning tasks in such epistemic logics are intractable, which impedes
applications of multi-agent epistemic logics in automatic planning. Knowledge
compilation provides a promising way of resolving the intractability by
identifying expressive fragments of epistemic logics that are tractable for
important reasoning tasks such as satisfiability and forgetting. The property
of logical separability allows to decompose a formula into some of its
subformulas and thus modular algorithms for various reasoning tasks can be
developed. In this paper, by employing logical separability, we propose an
approach to knowledge compilation for the logic Kn by defining a normal form
SDNF. Among several novel results, we show that every epistemic formula can be
equivalently compiled into a formula in SDNF, major reasoning tasks in SDNF are
tractable, and formulas in SDNF enjoy the logical separability. Our results
shed some lights on modular approaches to knowledge compilation. Furthermore,
we apply our results in the multi-agent epistemic planning. Finally, we extend
the above result to the logic K45n that is Kn extended by introspection axioms
4 and 5.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2018 16:33:43 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Jun 2018 07:10:40 GMT"
}
] | 1,530,230,400,000 | [
[
"Fang",
"Liangda",
""
],
[
"Wang",
"Kewen",
""
],
[
"Wang",
"Zhe",
""
],
[
"Wen",
"Ximing",
""
]
] |
1806.10755 | Peter Sutor Jr. | Peter Sutor Jr., Douglas Summers-Stay, and Yiannis Aloimonos | A Computational Theory for Life-Long Learning of Semantics | Submitted and accepted to AGI 2018. Length 10 pages with 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic vectors are learned from data to express semantic relationships
between elements of information, for the purpose of solving and informing
downstream tasks. Other models exist that learn to map and classify supervised
data. However, the two worlds of learning rarely interact to inform one another
dynamically, whether across types of data or levels of semantics, in order to
form a unified model. We explore the research problem of learning these vectors
and propose a framework for learning the semantics of knowledge incrementally
and online, across multiple mediums of data, via binary vectors. We discuss the
aspects of this framework to spur future research on this approach and problem.
| [
{
"version": "v1",
"created": "Thu, 28 Jun 2018 03:34:54 GMT"
},
{
"version": "v2",
"created": "Sun, 22 Jul 2018 22:50:34 GMT"
}
] | 1,532,390,400,000 | [
[
"Sutor",
"Peter",
"Jr."
],
[
"Summers-Stay",
"Douglas",
""
],
[
"Aloimonos",
"Yiannis",
""
]
] |
1806.11103 | Marco Valtorta | Mohammad Ali Javidian and Marco Valtorta | Comment on: Decomposition of structural learning about directed acyclic
graphs [1] | 5 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an alternative proof concerning necessary and sufficient
conditions to split the problem of searching for d-separators and building the
skeleton of a DAG into small problems for every node of a separation tree T.
The proof is simpler than the original [1]. The same proof structure has been
used in [2] for learning the structure of multivariate regression chain graphs
(MVR CGs).
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2018 19:37:33 GMT"
}
] | 1,530,489,600,000 | [
[
"Javidian",
"Mohammad Ali",
""
],
[
"Valtorta",
"Marco",
""
]
] |
1806.11298 | Biqing Fang | Xiao Huang, Biqing Fang, Hai Wan, Yongmei Liu | A General Multi-agent Epistemic Planner Based on Higher-order Belief
Change | One of the authors think it's not appropriate to show this work there
days. Then we discussed, we want submit a new work and this one together
later | IJCAI. (2017) 1093-1101 | 10.24963/ijcai.2017/152 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, multi-agent epistemic planning has received attention from
both dynamic logic and planning communities. Existing implementations of
multi-agent epistemic planning are based on compilation into classical planning
and suffer from various limitations, such as generating only linear plans,
restriction to public actions, and incapability to handle disjunctive beliefs.
In this paper, we propose a general representation language for multi-agent
epistemic planning where the initial KB and the goal, the preconditions and
effects of actions can be arbitrary multi-agent epistemic formulas, and the
solution is an action tree branching on sensing results. To support efficient
reasoning in the multi-agent KD45 logic, we make use of a normal form called
alternating cover disjunctive formulas (ACDFs). We propose basic revision and
update algorithms for ACDFs. We also handle static propositional common
knowledge, which we call constraints. Based on our reasoning, revision and
update algorithms, adapting the PrAO algorithm for contingent planning from the
literature, we implemented a multi-agent epistemic planner called MEPK. Our
experimental results show the viability of our approach.
| [
{
"version": "v1",
"created": "Fri, 29 Jun 2018 08:26:55 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Aug 2018 14:02:49 GMT"
}
] | 1,534,291,200,000 | [
[
"Huang",
"Xiao",
""
],
[
"Fang",
"Biqing",
""
],
[
"Wan",
"Hai",
""
],
[
"Liu",
"Yongmei",
""
]
] |
1806.11304 | Biqing Fang | Liangda Fang, Hai Wan, Xianqiao Liu, Biqing Fang, Zhaorong Lai | Dependence in Propositional Logic: Formula-Formula Dependence and
Formula Forgetting -- Application to Belief Update and Conservative Extension | We find a mistake in this version and we need a period of time to fix
it | AAAI. (2018) 1835-1844 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dependence is an important concept for many tasks in artificial intelligence.
A task can be executed more efficiently by discarding something independent
from the task. In this paper, we propose two novel notions of dependence in
propositional logic: formula-formula dependence and formula forgetting. The
first is a relation between formulas capturing whether a formula depends on
another one, while the second is an operation that returns the strongest
consequence independent of a formula. We also apply these two notions in two
well-known issues: belief update and conservative extension. Firstly, we define
a new update operator based on formula-formula dependence. Furthermore, we
reduce conservative extension to formula forgetting.
| [
{
"version": "v1",
"created": "Fri, 29 Jun 2018 08:35:27 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jun 2019 08:20:58 GMT"
}
] | 1,560,384,000,000 | [
[
"Fang",
"Liangda",
""
],
[
"Wan",
"Hai",
""
],
[
"Liu",
"Xianqiao",
""
],
[
"Fang",
"Biqing",
""
],
[
"Lai",
"Zhaorong",
""
]
] |
1806.11338 | Aswani Kumar Cherukuri Dr | Ishwarya M S, Aswani Kumar Cherukuri | Quantum aspects of high dimensional formal representation of conceptual
spaces | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human cognition is a complex process facilitated by the intricate
architecture of human brain. However, human cognition is often reduced to
quantum theory based events in principle because of their correlative
conjectures for the purpose of analysis for reciprocal understanding. In this
paper, we begin our analysis of human cognition via formal methods and proceed
towards quantum theories. Human cognition often violate classic probabilities
on which formal representation of conceptual spaces are built. Further,
geometric representation of conceptual spaces proposed by Gardenfors discusses
the underlying content but lacks a systematic approach (Gardenfors, 2000; Kitto
et. al, 2012). However, the aforementioned views are not contradictory but
different perspective with a gap towards sufficient understanding of human
cognitive process. A comprehensive and systematic approach to model a
relatively complex scenario can be addressed by vector space approach of
conceptual spaces as discussed in literature. In this research, we have
proposed an approach that uses both formal representation and Gardenfors
geometric approach. The proposed model of high dimensional formal
representation of conceptual space is mathematically analysed and inferred to
exhibit quantum aspects. Also, the proposed model achieves cognition, in
particular, consciousness. We have demonstrated this process of achieving
consciousness with a constructive learning scenario. We have also proposed an
algorithm for conceptual scaling of a real world scenario under different
quality dimensions to obtain a conceptual scale.
| [
{
"version": "v1",
"created": "Fri, 29 Jun 2018 10:35:18 GMT"
}
] | 1,530,489,600,000 | [
[
"S",
"Ishwarya M",
""
],
[
"Cherukuri",
"Aswani Kumar",
""
]
] |
1806.11401 | Amit Mishra | Amit Kumar Mishra | WEBCA: Weakly-Electric-Fish Bioinspired Cognitive Architecture | To be published in Annual Bio-Inspired Cognitive Architecture (BICA)
Conference 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuroethology has been an active field of study for more than a century now.
Out of some of the most interesting species that has been studied so far,
weakly electric fish is a fascinating one. It performs communication,
echo-location and inter-species detection efficiently with an interesting
configuration of sensors, neu-rons and a simple brain. In this paper we propose
a cognitive architecture inspired by the way these fishes handle and process
information. We believe that it is eas-ier to understand and mimic the neural
architectures of a simpler species than that of human. Hence, the proposed
architecture is expected to both help research in cognitive robotics and also
help understand more complicated brains like that of human beings.
| [
{
"version": "v1",
"created": "Fri, 29 Jun 2018 13:22:37 GMT"
}
] | 1,530,489,600,000 | [
[
"Mishra",
"Amit Kumar",
""
]
] |
1807.00049 | Atishay Jain | Anand Venkatesan, Atishay Jain, Rakesh Grewal | AI in Game Playing: Sokoban Solver | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence is becoming instrumental in a variety of
applications. Games serve as a good breeding ground for trying and testing
these algorithms in a sandbox with simpler constraints in comparison to real
life. In this project, we aim to develop an AI agent that can solve the
classical Japanese game of Sokoban using various algorithms and heuristics and
compare their performances through standard metrics.
| [
{
"version": "v1",
"created": "Fri, 29 Jun 2018 19:49:09 GMT"
}
] | 1,530,576,000,000 | [
[
"Venkatesan",
"Anand",
""
],
[
"Jain",
"Atishay",
""
],
[
"Grewal",
"Rakesh",
""
]
] |
1807.00196 | Pedro Alejandro Ortega | Pedro A. Ortega, Shane Legg | Modeling Friends and Foes | 13 pages, 9 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How can one detect friendly and adversarial behavior from raw data? Detecting
whether an environment is a friend, a foe, or anything in between, remains a
poorly understood yet desirable ability for safe and robust agents. This paper
proposes a definition of these environmental "attitudes" based on an
characterization of the environment's ability to react to the agent's private
strategy. We define an objective function for a one-shot game that allows
deriving the environment's probability distribution under friendly and
adversarial assumptions alongside the agent's optimal strategy. Furthermore, we
present an algorithm to compute these equilibrium strategies, and show
experimentally that both friendly and adversarial environments possess
non-trivial optimal strategies.
| [
{
"version": "v1",
"created": "Sat, 30 Jun 2018 16:07:43 GMT"
}
] | 1,530,576,000,000 | [
[
"Ortega",
"Pedro A.",
""
],
[
"Legg",
"Shane",
""
]
] |
1807.00401 | Kalyan Veeramachaneni | James Max Kanter, Benjamin Schreck, Kalyan Veeramachaneni | Machine learning 2.0 : Engineering Data Driven AI Products | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ML 2.0: In this paper, we propose a paradigm shift from the current practice
of creating machine learning models - which requires months-long discovery,
exploration and "feasibility report" generation, followed by re-engineering for
deployment - in favor of a rapid, 8-week process of development, understanding,
validation and deployment that can executed by developers or subject matter
experts (non-ML experts) using reusable APIs. This accomplishes what we call a
"minimum viable data-driven model," delivering a ready-to-use machine learning
model for problems that haven't been solved before using machine learning. We
provide provisions for the refinement and adaptation of the "model," with
strict enforcement and adherence to both the scaffolding/abstractions and the
process. We imagine that this will bring forth the second phase in machine
learning, in which discovery is subsumed by more targeted goals of delivery and
impact.
| [
{
"version": "v1",
"created": "Sun, 1 Jul 2018 21:50:58 GMT"
}
] | 1,530,576,000,000 | [
[
"Kanter",
"James Max",
""
],
[
"Schreck",
"Benjamin",
""
],
[
"Veeramachaneni",
"Kalyan",
""
]
] |
1807.00564 | Manfred Jaeger | Manfred Jaeger and Oliver Schulte | Inference, Learning, and Population Size: Projectivity for SRL Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A subtle difference between propositional and relational data is that in many
relational models, marginal probabilities depend on the population or domain
size. This paper connects the dependence on population size to the classic
notion of projectivity from statistical theory: Projectivity implies that
relational predictions are robust with respect to changes in domain size. We
discuss projectivity for a number of common SRL systems, and identify syntactic
fragments that are guaranteed to yield projective models. The syntactic
conditions are restrictive, which suggests that projectivity is difficult to
achieve in SRL, and care must be taken when working with different domain
sizes.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 09:40:00 GMT"
}
] | 1,530,576,000,000 | [
[
"Jaeger",
"Manfred",
""
],
[
"Schulte",
"Oliver",
""
]
] |
1807.00589 | Vishal Sharma | Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate and
Parag Singla | Lifted Marginal MAP Inference | Accepted in UAI-18. Corrected some typos | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lifted inference reduces the complexity of inference in relational
probabilistic models by identifying groups of constants (or atoms) which behave
symmetric to each other. A number of techniques have been proposed in the
literature for lifting marginal as well MAP inference. We present the first
application of lifting rules for marginal-MAP (MMAP), an important inference
problem in models having latent (random) variables. Our main contribution is
two fold: (1) we define a new equivalence class of (logical) variables, called
Single Occurrence for MAX (SOM), and show that solution lies at extreme with
respect to the SOM variables, i.e., predicate groundings differing only in the
instantiation of the SOM variables take the same truth value (2) we define a
sub-class {\em SOM-R} (SOM Reduce) and exploit properties of extreme
assignments to show that MMAP inference can be performed by reducing the domain
of SOM-R variables to a single constant.We refer to our lifting technique as
the {\em SOM-R} rule for lifted MMAP. Combined with existing rules such as
decomposer and binomial, this results in a powerful framework for lifted MMAP.
Experiments on three benchmark domains show significant gains in both time and
memory compared to ground inference as well as lifted approaches not using
SOM-R.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 10:45:21 GMT"
},
{
"version": "v2",
"created": "Sun, 8 Jul 2018 12:59:57 GMT"
}
] | 1,531,180,800,000 | [
[
"Sharma",
"Vishal",
""
],
[
"Sheikh",
"Noman Ahmed",
""
],
[
"Mittal",
"Happy",
""
],
[
"Gogate",
"Vibhav",
""
],
[
"Singla",
"Parag",
""
]
] |
1807.00643 | Ankit Anand | Gagan Madan, Ankit Anand, Mausam and Parag Singla | Block-Value Symmetries in Probabilistic Graphical Models | 11 pages, 3 figures, Accepted in UAI 2018 and StaR AI 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One popular way for lifted inference in probabilistic graphical models is to
first merge symmetric states into a single cluster (orbit) and then use these
for downstream inference, via variations of orbital MCMC [Niepert, 2012]. These
orbits are represented compactly using permutations over variables, and
variable-value (VV) pairs, but they can miss several state symmetries in a
domain.
We define the notion of permutations over block-value (BV) pairs, where a
block is a set of variables. BV strictly generalizes VV symmetries, and can
compute many more symmetries for increasing block sizes. To operationalize use
of BV permutations in lifted inference, we describe 1) an algorithm to compute
BV permutations given a block partition of the variables, 2) BV-MCMC, an
extension of orbital MCMC that can sample from BV orbits, and 3) a heuristic to
suggest good block partitions. Our experiments show that BV-MCMC can mix much
faster compared to vanilla MCMC and orbital MCMC.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 13:03:22 GMT"
},
{
"version": "v2",
"created": "Sun, 8 Jul 2018 06:09:06 GMT"
}
] | 1,531,180,800,000 | [
[
"Madan",
"Gagan",
""
],
[
"Anand",
"Ankit",
""
],
[
"Mausam",
"",
""
],
[
"Singla",
"Parag",
""
]
] |
1807.00743 | Tanya Braun | Tanya Braun and Ralf M\"oller | Fusing First-order Knowledge Compilation and the Lifted Junction Tree
Algorithm | Accepted at the Eighth International Workshop on Statistical
Relational AI, a version is to appear in the Proceedings of the KI-18:
Advances in AI | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard approaches for inference in probabilistic formalisms with
first-order constructs include lifted variable elimination (LVE) for single
queries as well as first-order knowledge compilation (FOKC) based on weighted
model counting. To handle multiple queries efficiently, the lifted junction
tree algorithm (LJT) uses a first-order cluster representation of a model and
LVE as a subroutine in its computations. For certain inputs, the
implementations of LVE and, as a result, LJT ground parts of a model where FOKC
has a lifted run. The purpose of this paper is to prepare LJT as a backbone for
lifted inference and to use any exact inference algorithm as subroutine. Using
FOKC in LJT allows us to compute answers faster than LJT, LVE, and FOKC for
certain inputs.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 15:33:48 GMT"
}
] | 1,530,576,000,000 | [
[
"Braun",
"Tanya",
""
],
[
"Möller",
"Ralf",
""
]
] |
1807.00744 | Marcel Gehrke | Marcel Gehrke, Tanya Braun, and Ralf M\"oller | Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree
Algorithm | Accepted at the Eighth International Workshop on Statistical
Relational AI | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The lifted dynamic junction tree algorithm (LDJT) efficiently answers
filtering and prediction queries for probabilistic relational temporal models
by building and then reusing a first-order cluster representation of a
knowledge base for multiple queries and time steps. Unfortunately, a non-ideal
elimination order can lead to groundings even though a lifted run is possible
for a model. We extend LDJT (i) to identify unnecessary groundings while
proceeding in time and (ii) to prevent groundings by delaying eliminations
through changes in a temporal first-order cluster representation. The extended
version of LDJT answers multiple temporal queries orders of magnitude faster
than the original version.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 15:33:49 GMT"
}
] | 1,530,576,000,000 | [
[
"Gehrke",
"Marcel",
""
],
[
"Braun",
"Tanya",
""
],
[
"Möller",
"Ralf",
""
]
] |
1807.00886 | Sai Vikneshwar Mani Jayaraman | Aarthy Shivram Arun, Sai Vikneshwar Mani Jayaraman, Christopher R\'e
and Atri Rudra | Hypertree Decompositions Revisited for PGMs | Accepted for StarAI Proceedings. Camera Ready Version of
arXiv:1804.01640 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We revisit the classical problem of exact inference on probabilistic
graphical models (PGMs). Our algorithm is based on recent \emph{worst-case
optimal database join} algorithms, which can be asymptotically faster than
traditional data processing methods. We present the first empirical evaluation
of these algorithms via JoinInfer -- a new exact inference engine. We
empirically explore the properties of the data for which our engine can be
expected to outperform traditional inference engines, refining current
theoretical notions. Further, JoinInfer outperforms existing state-of-the-art
inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by
up to a factor of 630x. Finally, we propose a promising data-driven heuristic
that extends JoinInfer to automatically tailor its parameters and/or switch to
the traditional inference algorithms.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 21:04:06 GMT"
}
] | 1,530,662,400,000 | [
[
"Arun",
"Aarthy Shivram",
""
],
[
"Jayaraman",
"Sai Vikneshwar Mani",
""
],
[
"Ré",
"Christopher",
""
],
[
"Rudra",
"Atri",
""
]
] |
1807.00900 | Eneldo Loza Menc\'ia | Patryk Hopner, Eneldo Loza Menc\'ia | Analysis and Optimization of Deep Counterfactual Value Networks | Long version of publication appearing at KI 2018: The 41st German
Conference on Artificial Intelligence
(http://dx.doi.org/10.1007/978-3-030-00111-7_26). Corrected typo in title | null | 10.1007/978-3-030-00111-7_26 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently a strong poker-playing algorithm called DeepStack was published,
which is able to find an approximate Nash equilibrium during gameplay by using
heuristic values of future states predicted by deep neural networks. This paper
analyzes new ways of encoding the inputs and outputs of DeepStack's deep
counterfactual value networks based on traditional abstraction techniques, as
well as an unabstracted encoding, which was able to increase the network's
accuracy.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 21:36:23 GMT"
},
{
"version": "v2",
"created": "Fri, 12 Oct 2018 16:48:29 GMT"
}
] | 1,539,561,600,000 | [
[
"Hopner",
"Patryk",
""
],
[
"Mencía",
"Eneldo Loza",
""
]
] |
1807.01079 | Anna Latour | Anna L.D. Latour, Behrouz Babaki, Siegfried Nijssen | Stochastic Constraint Optimization using Propagation on Ordered Binary
Decision Diagrams | Eighth International Workshop on Statistical Relational AI, in
conjunction with the 2018 International Joint Conference on Artificial
Intelligence (IJCAI 2018) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A number of problems in relational Artificial Intelligence can be viewed as
Stochastic Constraint Optimization Problems (SCOPs). These are constraint
optimization problems that involve objectives or constraints with a stochastic
component. Building on the recently proposed language SC-ProbLog for modeling
SCOPs, we propose a new method for solving these problems. Earlier methods used
Probabilistic Logic Programming (PLP) techniques to create Ordered Binary
Decision Diagrams (OBDDs), which were decomposed into smaller constraints in
order to exploit existing constraint programming (CP) solvers. We argue that
this approach has as drawback that a decomposed representation of an OBDD does
not guarantee domain consistency during search, and hence limits the efficiency
of the solver. For the specific case of monotonic distributions, we suggest an
alternative method for using CP in SCOP, based on the development of a new
propagator; we show that this propagator is linear in the size of the OBDD, and
has the potential to be more efficient than the decomposition method, as it
maintains domain consistency.
| [
{
"version": "v1",
"created": "Tue, 3 Jul 2018 10:58:38 GMT"
}
] | 1,530,662,400,000 | [
[
"Latour",
"Anna L. D.",
""
],
[
"Babaki",
"Behrouz",
""
],
[
"Nijssen",
"Siegfried",
""
]
] |
1807.01081 | Sergio Hernandez | Sergio Hernandez Cerezo, Guillem Duran Ballester, Spiros Baxevanakis | Solving Atari Games Using Fractals And Entropy | 7 pages, 1 figure, submitted to NIPS-2018 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we introduce a novel MCTS based approach that is derived from
the laws of the thermodynamics. The algorithm coined Fractal Monte Carlo (FMC),
allows us to create an agent that takes intelligent actions in both continuous
and discrete environments while providing control over every aspect of the
agent behavior. Results show that FMC is several orders of magnitude more
efficient than similar techniques, such as MCTS, in the Atari games tested.
| [
{
"version": "v1",
"created": "Tue, 3 Jul 2018 10:59:26 GMT"
}
] | 1,530,662,400,000 | [
[
"Cerezo",
"Sergio Hernandez",
""
],
[
"Ballester",
"Guillem Duran",
""
],
[
"Baxevanakis",
"Spiros",
""
]
] |
1807.01268 | Mauricio Gonzalez-Soto | M. Gonzalez-Soto, L.E. Sucar, H.J. Escalante | Playing against Nature: causal discovery for decision making under
uncertainty | Accepted as poster presentation at the CausalML Workshop at ICML 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider decision problems under uncertainty where the options available
to a decision maker and the resulting outcome are related through a causal
mechanism which is unknown to the decision maker. We ask how a decision maker
can learn about this causal mechanism through sequential decision making as
well as using current causal knowledge inside each round in order to make
better choices had she not considered causal knowledge and propose a decision
making procedure in which an agent holds \textit{beliefs} about her environment
which are used to make a choice and are updated using the observed outcome. As
proof of concept, we present an implementation of this causal decision making
model and apply it in a simple scenario. We show that the model achieves a
performance similar to the classic Q-learning while it also acquires a causal
model of the environment.
| [
{
"version": "v1",
"created": "Tue, 3 Jul 2018 16:36:03 GMT"
}
] | 1,530,662,400,000 | [
[
"Gonzalez-Soto",
"M.",
""
],
[
"Sucar",
"L. E.",
""
],
[
"Escalante",
"H. J.",
""
]
] |
1807.01425 | Artem Molchanov | Artem Molchanov, Karol Hausman, Stan Birchfield, Gaurav Sukhatme | Region Growing Curriculum Generation for Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning a policy capable of moving an agent between any two states in the
environment is important for many robotics problems involving navigation and
manipulation. Due to the sparsity of rewards in such tasks, applying
reinforcement learning in these scenarios can be challenging. Common approaches
for tackling this problem include reward engineering with auxiliary rewards,
requiring domain-specific knowledge or changing the objective.
In this work, we introduce a method based on region-growing that allows
learning in an environment with any pair of initial and goal states. Our
algorithm first learns how to move between nearby states and then increases the
difficulty of the start-goal transitions as the agent's performance improves.
This approach creates an efficient curriculum for learning the objective
behavior of reaching any goal from any initial state. In addition, we describe
a method to adaptively adjust expansion of the growing region that allows
automatic adjustment of the key exploration hyperparameter to environments with
different requirements. We evaluate our approach on a set of simulated
navigation and manipulation tasks, where we demonstrate that our algorithm can
efficiently learn a policy in the presence of sparse rewards.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2018 01:49:29 GMT"
}
] | 1,530,748,800,000 | [
[
"Molchanov",
"Artem",
""
],
[
"Hausman",
"Karol",
""
],
[
"Birchfield",
"Stan",
""
],
[
"Sukhatme",
"Gaurav",
""
]
] |
1807.01586 | Marcel Gehrke | Marcel Gehrke, Tanya Braun, and Ralf M\"oller | Answering Hindsight Queries with Lifted Dynamic Junction Trees | Accepted at the Eighth International Workshop on Statistical
Relational AI. arXiv admin note: substantial text overlap with
arXiv:1807.00744 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The lifted dynamic junction tree algorithm (LDJT) efficiently answers
filtering and prediction queries for probabilistic relational temporal models
by building and then reusing a first-order cluster representation of a
knowledge base for multiple queries and time steps. We extend LDJT to (i) solve
the smoothing inference problem to answer hindsight queries by introducing an
efficient backward pass and (ii) discuss different options to instantiate a
first-order cluster representation during a backward pass. Further, our
relational forward backward algorithm makes hindsight queries to the very
beginning feasible. LDJT answers multiple temporal queries faster than the
static lifted junction tree algorithm on an unrolled model, which performs
smoothing during message passing.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2018 15:38:58 GMT"
}
] | 1,530,748,800,000 | [
[
"Gehrke",
"Marcel",
""
],
[
"Braun",
"Tanya",
""
],
[
"Möller",
"Ralf",
""
]
] |
1807.01801 | Amar Viswanathan Kannan | Amar Viswanathan, Geeth de Mel, James A.Hendler | Feature-based reformulation of entities in triple pattern queries | ESWC 2018 submission | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs encode uniquely identifiable entities to other entities or
literal values by means of relationships, thus enabling semantically rich
querying over the stored data. Typically, the semantics of such queries are
often crisp thereby resulting in crisp answers. Query log statistics show that
a majority of the queries issued to knowledge graphs are often entity centric
queries. When a user needs additional answers the state-of-the-art in assisting
users is to rewrite the original query resulting in a set of approximations.
Several strategies have been proposed in past to address this. They typically
move up the taxonomy to relax a specific element to a more generic element.
Entities don't have a taxonomy and they end up being generalized. To address
this issue, in this paper, we propose an entity centric reformulation strategy
that utilizes schema information and entity features present in the graph to
suggest rewrites. Once the features are identified, the entity in concern is
reformulated as a set of features. Since entities can have a large number of
features, we introduce strategies that select the top-k most relevant and
{informative ranked features and augment them to the original query to create a
valid reformulation. We then evaluate our approach by showing that our
reformulation strategy produces results that are more informative when compared
with state-of-the-art
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2018 22:24:50 GMT"
}
] | 1,530,835,200,000 | [
[
"Viswanathan",
"Amar",
""
],
[
"de Mel",
"Geeth",
""
],
[
"Hendler",
"James A.",
""
]
] |
1807.01953 | Aswani Kumar Cherukuri Dr | M S Ishwarya, Aswani Kumar Cherukuri | Lattice based Conceptual Spaces to Explore Cognitive Functionalities for
Prosthetic Arm | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Upper limb Prosthetic can be viewed as an independent cognitive system in
order to develop a conceptual space. In this paper, we provide a detailed
analogical reasoning of prosthetic arm to build the conceptual spaces with the
help of the theory called geometric framework of conceptual spaces proposed by
Gardenfors. Terminologies of conceptual spaces such as concepts, similarities,
properties, quality dimensions and prototype are applied for a specific
prosthetic system and conceptual space is built for prosthetic arm. Concept
lattice traversals are used on the lattice represented conceptual spaces.
Cognitive functionalities such as generalization (Similarities) and
specialization (Differences) are achieved in the lattice represented conceptual
space. This might well prove to design intelligent prosthetics to assist
challenged humans. Geometric framework of conceptual spaces holds similar
concepts closer in geometric structures in a way similar to concept lattices.
Hence, we also propose to use concept lattice to represent concepts of
geometric framework of conceptual spaces. Also, we extend our discussion with
our insights on conceptual spaces of bidirectional hand prosthetics.
| [
{
"version": "v1",
"created": "Thu, 5 Jul 2018 11:58:16 GMT"
}
] | 1,530,835,200,000 | [
[
"Ishwarya",
"M S",
""
],
[
"Cherukuri",
"Aswani Kumar",
""
]
] |
1807.02072 | Anton Kolonin Dr. | Anton Kolonin | Representing scenarios for process evolution management | 12 pages, 3 figures, 1 table | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the following writing we discuss a conceptual framework for representing
events and scenarios from the perspective of a novel form of causal analysis.
This causal analysis is applied to the events and scenarios so as to determine
measures that could be used to manage the development of the processes that
they are a part of in real time. An overall terminological framework and
entity-relationship model are suggested along with a specification of the
functional sets involved in both reasoning and analytics. The model is
considered to be a specific case of the generic problem of finding sequential
series in disparate data. The specific inference and reasoning processes are
identified for future implementation.
| [
{
"version": "v1",
"created": "Thu, 5 Jul 2018 16:10:23 GMT"
}
] | 1,530,835,200,000 | [
[
"Kolonin",
"Anton",
""
]
] |
1807.02406 | Ramesh Ramasamy Pandi | Song Guang Ho, Ramesh Ramasamy Pandi, Sarat Chandra Nagavarapu and
Justin Dauwels | Multi-atomic Annealing Heuristic for Static Dial-a-ride Problem | To be presented at the IEEE International Conference on Service
Operations and Logistics, and Informatics (SOLI), Singapore, 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dial-a-ride problem (DARP) deals with the transportation of users between
pickup and drop-off locations associated with specified time windows. This
paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve
static dial-a-ride problem. Two new local search operators (burn and reform), a
new construction heuristic and two request sequencing mechanisms (Sorted List
and Random List) are developed. Computational experiments conducted on various
standard DARP test instances prove that MATA is an expeditious meta-heuristic
in contrast to other existing methods. In all experiments, MATA demonstrates
the capability to obtain high quality solutions, faster convergence, and
quicker attainment of a first feasible solution. It is observed that MATA
attains a first feasible solution 29.8 to 65.1% faster, and obtains a final
solution that is 3.9 to 5.2% better, when compared to other algorithms within
60 sec.
| [
{
"version": "v1",
"created": "Fri, 29 Jun 2018 12:09:57 GMT"
}
] | 1,531,094,400,000 | [
[
"Ho",
"Song Guang",
""
],
[
"Pandi",
"Ramesh Ramasamy",
""
],
[
"Nagavarapu",
"Sarat Chandra",
""
],
[
"Dauwels",
"Justin",
""
]
] |
1807.02637 | Dejan Lavbi\v{c} | Dejan Lavbi\v{c}, Tadej Matek and Alja\v{z} Zrnec | Recommender system for learning SQL using hints | 18 pages, 8 figures, 2 tables | Interactive learning environments 25 (2017) 1048 - 1064 | 10.1080/10494820.2016.1244084 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today's software industry requires individuals who are proficient in as many
programming languages as possible. Structured query language (SQL), as an
adopted standard, is no exception, as it is the most widely used query language
to retrieve and manipulate data. However, the process of learning SQL turns out
to be challenging. The need for a computer-aided solution to help users learn
SQL and improve their proficiency is vital. In this study, we present a new
approach to help users conceptualize basic building blocks of the language
faster and more efficiently. The adaptive design of the proposed approach aids
users in learning SQL by supporting their own path to the solution and
employing successful previous attempts, while not enforcing the ideal solution
provided by the instructor. Furthermore, we perform an empirical evaluation
with 93 participants and demonstrate that the employment of hints is
successful, being especially beneficial for users with lower prior knowledge.
| [
{
"version": "v1",
"created": "Sat, 7 Jul 2018 09:13:34 GMT"
}
] | 1,531,180,800,000 | [
[
"Lavbič",
"Dejan",
""
],
[
"Matek",
"Tadej",
""
],
[
"Zrnec",
"Aljaž",
""
]
] |
1807.02879 | Laura Giordano | Laura Giordano, Valentina Gliozzi | Reasoning about exceptions in ontologies: from the lexicographic closure
to the skeptical closure | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reasoning about exceptions in ontologies is nowadays one of the challenges
the description logics community is facing. The paper describes a preferential
approach for dealing with exceptions in Description Logics, based on the
rational closure. The rational closure has the merit of providing a simple and
efficient approach for reasoning with exceptions, but it does not allow
independent handling of the inheritance of different defeasible properties of
concepts. In this work we outline a possible solution to this problem by
introducing a variant of the lexicographical closure, that we call skeptical
closure, which requires to construct a single base. We develop a bi-preference
semantics semantics for defining a characterization of the skeptical closure.
| [
{
"version": "v1",
"created": "Sun, 8 Jul 2018 20:28:54 GMT"
}
] | 1,531,180,800,000 | [
[
"Giordano",
"Laura",
""
],
[
"Gliozzi",
"Valentina",
""
]
] |
1807.03083 | Patrick Rodler | Patrick Rodler and Wolfgang Schmid | Evaluating Active Learning Heuristics for Sequential Diagnosis | This work was presented at the International Workshop on Principles
of Diagnosis 2018 (DX-2018) and a version of this work was formally published
as "Patrick Rodler, Wolfgang Schmid. On the impact and proper use of
heuristics in test-driven ontology debugging. RuleML+RR, 2018." | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Given a malfunctioning system, sequential diagnosis aims at identifying the
root cause of the failure in terms of abnormally behaving system components. As
initial system observations usually do not suffice to deterministically pin
down just one explanation of the system's misbehavior, additional system
measurements can help to differentiate between possible explanations. The goal
is to restrict the space of explanations until there is only one (highly
probable) explanation left. To achieve this with a minimal-cost set of
measurements, various (active learning) heuristics for selecting the best next
measurement have been proposed.
We report preliminary results of extensive ongoing experiments with a set of
selection heuristics on real-world diagnosis cases. In particular, we try to
answer questions such as "Is some heuristic always superior to all others?",
"On which factors does the (relative) performance of the particular heuristics
depend?" or "Under which circumstances should I use which heuristic?"
| [
{
"version": "v1",
"created": "Mon, 9 Jul 2018 12:56:03 GMT"
},
{
"version": "v2",
"created": "Fri, 5 Aug 2022 11:50:28 GMT"
}
] | 1,659,916,800,000 | [
[
"Rodler",
"Patrick",
""
],
[
"Schmid",
"Wolfgang",
""
]
] |
1807.03633 | Tong Wang | Tong Wang, Veerajalandhar Allareddy, Sankeerth Rampa and
Veerasathpurush Allareddy | Interpretable Patient Mortality Prediction with Multi-value Rule Sets | arXiv admin note: text overlap with arXiv:1710.05257 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting
patient mortality. Compared to rule sets built from single-valued rules, MRS
adopts a more generalized form of association rules that allows multiple values
in a condition. Rules of this form are more concise than classical
single-valued rules in capturing and describing patterns in data. Our
formulation also pursues a higher efficiency of feature utilization, which
reduces possible cost in data collection and storage. We propose a Bayesian
framework for formulating a MRS model and propose an efficient inference method
for learning a maximum \emph{a posteriori}, incorporating theoretically
grounded bounds to iteratively reduce the search space and improve the search
efficiency. Experiments show that our model was able to achieve better
performance than baseline method including the current system used by the
hospital.
| [
{
"version": "v1",
"created": "Fri, 6 Jul 2018 22:47:19 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Jul 2018 14:57:51 GMT"
}
] | 1,532,390,400,000 | [
[
"Wang",
"Tong",
""
],
[
"Allareddy",
"Veerajalandhar",
""
],
[
"Rampa",
"Sankeerth",
""
],
[
"Allareddy",
"Veerasathpurush",
""
]
] |
1807.03760 | Yen-Chia Hsu | Yen-Chia Hsu | SimArch: A Multi-agent System For Human Path Simulation In Architecture
Design | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human moving path is an important feature in architecture design. By studying
the path, architects know where to arrange the basic elements (e.g. structures,
glasses, furniture, etc.) in the space. This paper presents SimArch, a
multi-agent system for human moving path simulation. It involves a behavior
model built by using a Markov Decision Process. The model simulates human
mental states, target range detection, and collision prediction when agents are
on the floor, in a particular small gallery, looking at an exhibit, or leaving
the floor. It also models different kinds of human characteristics by assigning
different transition probabilities. A modified weighted A* search algorithm
quickly plans the sub-optimal path of the agents. In an experiment, SimArch
takes a series of preprocessed floorplans as inputs, simulates the moving path,
and outputs a density map for evaluation. The density map provides the
prediction that how likely a person will occur in a location. A following
discussion illustrates how architects can use the density map to improve their
floorplan design.
| [
{
"version": "v1",
"created": "Tue, 10 Jul 2018 17:04:49 GMT"
}
] | 1,531,267,200,000 | [
[
"Hsu",
"Yen-Chia",
""
]
] |
1807.04375 | Michael Green | Michael Cerny Green, Ahmed Khalifa, Gabriella A.B. Barros, Tiago
Machado, Andy Nealen and Julian Togelius | AtDelfi: Automatically Designing Legible, Full Instructions For Games | 10 pages, 11 figures, published at Foundations of Digital Games
Conference 2018 | Foundations of Digital Games (FDG) 2018 | 10.1145/3235765.3235790 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a fully automatic method for generating video game
tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full
Instructions for games) was created to investigate procedural generation of
instructions that teach players how to play video games. We present a
representation of game rules and mechanics using a graph system as well as a
tutorial generation method that uses said graph representation. We demonstrate
the concept by testing it on games within the General Video Game Artificial
Intelligence (GVG-AI) framework; the paper discusses tutorials generated for
eight different games. Our findings suggest that a graph representation scheme
works well for simple arcade style games such as Space Invaders and Pacman, but
it appears that tutorials for more complex games might require higher-level
understanding of the game than just single mechanics.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2018 23:02:43 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Sep 2018 21:54:25 GMT"
}
] | 1,537,315,200,000 | [
[
"Green",
"Michael Cerny",
""
],
[
"Khalifa",
"Ahmed",
""
],
[
"Barros",
"Gabriella A. B.",
""
],
[
"Machado",
"Tiago",
""
],
[
"Nealen",
"Andy",
""
],
[
"Togelius",
"Julian",
""
]
] |
1807.04458 | Mikael Zayenz Lagerkvist | Magnus Gedda, Mikael Z. Lagerkvist, Martin Butler | Monte Carlo Methods for the Game Kingdomino | To be published in IEEE Conference on Computational Intelligence and
Games 2018 (IEEE CIG 2018) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Kingdomino is introduced as an interesting game for studying game playing:
the game is multiplayer (4 independent players per game); it has a limited game
depth (13 moves per player); and it has limited but not insignificant
interaction among players.
Several strategies based on locally greedy players, Monte Carlo Evaluation
(MCE), and Monte Carlo Tree Search (MCTS) are presented with variants. We
examine a variation of UCT called progressive win bias and a playout policy
(Player-greedy) focused on selecting good moves for the player. A thorough
evaluation is done showing how the strategies perform and how to choose
parameters given specific time constraints. The evaluation shows that
surprisingly MCE is stronger than MCTS for a game like Kingdomino.
All experiments use a cloud-native design, with a game server in a Docker
container, and agents communicating using a REST-style JSON protocol. This
enables a multi-language approach to separating the game state, the strategy
implementations, and the coordination layer.
| [
{
"version": "v1",
"created": "Thu, 12 Jul 2018 08:07:21 GMT"
},
{
"version": "v2",
"created": "Sun, 15 Jul 2018 05:23:13 GMT"
}
] | 1,531,785,600,000 | [
[
"Gedda",
"Magnus",
""
],
[
"Lagerkvist",
"Mikael Z.",
""
],
[
"Butler",
"Martin",
""
]
] |
1807.04561 | Fabio Patrizi | Giuseppe De Giacomo, Brian Logan, Paolo Felli, Fabio Patrizi,
Sebastian Sardina | Situation Calculus for Synthesis of Manufacturing Controllers | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Manufacturing is transitioning from a mass production model to a
manufacturing as a service model in which manufacturing facilities 'bid' to
produce products. To decide whether to bid for a complex, previously unseen
product, a manufacturing facility must be able to synthesize, 'on the fly', a
process plan controller that delegates abstract manufacturing tasks in the
supplied process recipe to the appropriate manufacturing resources, e.g., CNC
machines, robots etc. Previous work in applying AI behaviour composition to
synthesize process plan controllers has considered only finite state ad-hoc
representations. Here, we study the problem in the relational setting of the
Situation Calculus. By taking advantage of recent work on abstraction in the
Situation Calculus, process recipes and available resources are represented by
ConGolog programs over, respectively, an abstract and a concrete action theory.
This allows us to capture the problem in a formal, general framework, and show
decidability for the case of bounded action theories. We also provide
techniques for actually synthesizing the controller.
| [
{
"version": "v1",
"created": "Thu, 12 Jul 2018 12:05:41 GMT"
}
] | 1,531,440,000,000 | [
[
"De Giacomo",
"Giuseppe",
""
],
[
"Logan",
"Brian",
""
],
[
"Felli",
"Paolo",
""
],
[
"Patrizi",
"Fabio",
""
],
[
"Sardina",
"Sebastian",
""
]
] |
1807.04861 | Vitaliy Batusov | Vitaliy Batusov, Giuseppe De Giacomo, Mikhail Soutchanski | Hybrid Temporal Situation Calculus | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to model continuous change in Reiter's temporal situation
calculus action theories has attracted a lot of interest. In this paper, we
propose a new development of his approach, which is directly inspired by hybrid
systems in control theory. Specifically, while keeping the foundations of
Reiter's axiomatization, we propose an elegant extension of his approach by
adding a time argument to all fluents that represent continuous change.
Thereby, we insure that change can happen not only because of actions, but also
due to the passage of time. We present a systematic methodology to derive, from
simple premises, a new group of axioms which specify how continuous fluents
change over time within a situation. We study regression for our new temporal
basic action theories and demonstrate what reasoning problems can be solved.
Finally, we formally show that our temporal basic action theories indeed
capture hybrid automata.
| [
{
"version": "v1",
"created": "Thu, 12 Jul 2018 23:20:11 GMT"
}
] | 1,531,699,200,000 | [
[
"Batusov",
"Vitaliy",
""
],
[
"De Giacomo",
"Giuseppe",
""
],
[
"Soutchanski",
"Mikhail",
""
]
] |
1807.05517 | Michele Lombardi | Michele Lombardi and Michela Milano | Boosting Combinatorial Problem Modeling with Machine Learning | Originally submitted to IJCAI2018 | null | 10.24963/ijcai.2018/177 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the past few years, the area of Machine Learning (ML) has witnessed
tremendous advancements, becoming a pervasive technology in a wide range of
applications. One area that can significantly benefit from the use of ML is
Combinatorial Optimization. The three pillars of constraint satisfaction and
optimization problem solving, i.e., modeling, search, and optimization, can
exploit ML techniques to boost their accuracy, efficiency and effectiveness. In
this survey we focus on the modeling component, whose effectiveness is crucial
for solving the problem. The modeling activity has been traditionally shaped by
optimization and domain experts, interacting to provide realistic results.
Machine Learning techniques can tremendously ease the process, and exploit the
available data to either create models or refine expert-designed ones. In this
survey we cover approaches that have been recently proposed to enhance the
modeling process by learning either single constraints, objective functions, or
the whole model. We highlight common themes to multiple approaches and draw
connections with related fields of research.
| [
{
"version": "v1",
"created": "Sun, 15 Jul 2018 09:12:08 GMT"
}
] | 1,531,785,600,000 | [
[
"Lombardi",
"Michele",
""
],
[
"Milano",
"Michela",
""
]
] |
1807.05609 | Bart Jacobs | Bart Jacobs | The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's
update rule | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evidence in probabilistic reasoning may be 'hard' or 'soft', that is, it may
be of yes/no form, or it may involve a strength of belief, in the unit interval
[0, 1]. Reasoning with soft, [0, 1]-valued evidence is important in many
situations but may lead to different, confusing interpretations. This paper
intends to bring more mathematical and conceptual clarity to the field by
shifting the existing focus from specification of soft evidence to accomodation
of soft evidence. There are two main approaches, known as Jeffrey's rule and
Pearl's method; they give different outcomes on soft evidence. This paper
argues that they can be understood as correction and as improvement. It
describes these two approaches as different ways of updating with soft
evidence, highlighting their differences, similarities and applications. This
account is based on a novel channel-based approach to Bayesian probability.
Proper understanding of these two update mechanisms is highly relevant for
inference, decision tools and probabilistic programming languages.
| [
{
"version": "v1",
"created": "Sun, 15 Jul 2018 20:29:15 GMT"
},
{
"version": "v2",
"created": "Sun, 3 Mar 2019 19:26:13 GMT"
},
{
"version": "v3",
"created": "Sat, 29 Jun 2019 09:28:18 GMT"
}
] | 1,562,025,600,000 | [
[
"Jacobs",
"Bart",
""
]
] |
1807.06096 | Nils Jansen | Nils Jansen, Bettina K\"onighofer, Sebastian Junges, Alexandru C.
Serban, Roderick Bloem | Safe Reinforcement Learning via Probabilistic Shields | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper targets the efficient construction of a safety shield for decision
making in scenarios that incorporate uncertainty. Markov decision processes
(MDPs) are prominent models to capture such planning problems. Reinforcement
learning (RL) is a machine learning technique to determine near-optimal
policies in MDPs that may be unknown prior to exploring the model. However,
during exploration, RL is prone to induce behavior that is undesirable or not
allowed in safety- or mission-critical contexts. We introduce the concept of a
probabilistic shield that enables decision-making to adhere to safety
constraints with high probability. In a separation of concerns, we employ
formal verification to efficiently compute the probabilities of critical
decisions within a safety-relevant fragment of the MDP. We use these results to
realize a shield that is applied to an RL algorithm which then optimizes the
actual performance objective. We discuss tradeoffs between sufficient progress
in exploration of the environment and ensuring safety. In our experiments, we
demonstrate on the arcade game PAC-MAN and on a case study involving service
robots that the learning efficiency increases as the learning needs orders of
magnitude fewer episodes.
| [
{
"version": "v1",
"created": "Mon, 16 Jul 2018 20:29:04 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Nov 2019 16:12:41 GMT"
}
] | 1,574,726,400,000 | [
[
"Jansen",
"Nils",
""
],
[
"Könighofer",
"Bettina",
""
],
[
"Junges",
"Sebastian",
""
],
[
"Serban",
"Alexandru C.",
""
],
[
"Bloem",
"Roderick",
""
]
] |
1807.06142 | Catarina Moreira | Catarina Moreira and Andreas Wichert | Introducing Quantum-Like Influence Diagrams for Violations of the Sure
Thing Principle | null | Quantum Interactions, 2018 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is the focus of this work to extend and study the previously proposed
quantum-like Bayesian networks to deal with decision-making scenarios by
incorporating the notion of maximum expected utility in influence diagrams. The
general idea is to take advantage of the quantum interference terms produced in
the quantum-like Bayesian Network to influence the probabilities used to
compute the expected utility of some action. This way, we are not proposing a
new type of expected utility hypothesis. On the contrary, we are keeping it
under its classical definition. We are only incorporating it as an extension of
a probabilistic graphical model in a compact graphical representation called an
influence diagram in which the utility function depends on the probabilistic
influences of the quantum-like Bayesian network.
Our findings suggest that the proposed quantum-like influence digram can
indeed take advantage of the quantum interference effects of quantum-like
Bayesian Networks to maximise the utility of a cooperative behaviour in
detriment of a fully rational defect behaviour under the prisoner's dilemma
game.
| [
{
"version": "v1",
"created": "Mon, 16 Jul 2018 22:39:16 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Dec 2020 17:39:56 GMT"
}
] | 1,609,459,200,000 | [
[
"Moreira",
"Catarina",
""
],
[
"Wichert",
"Andreas",
""
]
] |
1807.06286 | Tobias Joppen | Tobias Joppen, Christian Wirth, and Johannes F\"urnkranz | Preference-Based Monte Carlo Tree Search | To be published | Proceedings of the 41st German Conference on Artificial
Intelligence (KI-18), 2018 | 10.1007/978-3-030-00111-7_28 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monte Carlo tree search (MCTS) is a popular choice for solving sequential
anytime problems. However, it depends on a numeric feedback signal, which can
be difficult to define. Real-time MCTS is a variant which may only rarely
encounter states with an explicit, extrinsic reward. To deal with such cases,
the experimenter has to supply an additional numeric feedback signal in the
form of a heuristic, which intrinsically guides the agent. Recent work has
shown evidence that in different areas the underlying structure is ordinal and
not numerical. Hence erroneous and biased heuristics are inevitable, especially
in such domains. In this paper, we propose a MCTS variant which only depends on
qualitative feedback, and therefore opens up new applications for MCTS. We also
find indications that translating absolute into ordinal feedback may be
beneficial. Using a puzzle domain, we show that our preference-based MCTS
variant, wich only receives qualitative feedback, is able to reach a
performance level comparable to a regular MCTS baseline, which obtains
quantitative feedback.
| [
{
"version": "v1",
"created": "Tue, 17 Jul 2018 09:04:35 GMT"
}
] | 1,537,401,600,000 | [
[
"Joppen",
"Tobias",
""
],
[
"Wirth",
"Christian",
""
],
[
"Fürnkranz",
"Johannes",
""
]
] |
1807.06419 | Subhash Kak | Subhash Kak | On Ternary Coding and Three-Valued Logic | 12 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematically, ternary coding is more efficient than binary coding. It is
little used in computation because technology for binary processing is already
established and the implementation of ternary coding is more complicated, but
remains relevant in algorithms that use decision trees and in communications.
In this paper we present a new comparison of binary and ternary coding and
their relative efficiencies are computed both for number representation and
decision trees. The implications of our inability to use optimal representation
through mathematics or logic are examined. Apart from considerations of
representation efficiency, ternary coding appears preferable to binary coding
in classification of many real-world problems of artificial intelligence (AI)
and medicine. We examine the problem of identifying appropriate three classes
for domain-specific applications.
| [
{
"version": "v1",
"created": "Fri, 13 Jul 2018 17:23:54 GMT"
}
] | 1,531,872,000,000 | [
[
"Kak",
"Subhash",
""
]
] |
1807.06734 | Michael Green | Michael Cerny Green, Ahmed Khalifa, Gabriella A.B. Barros, Andy
Nealen, Julian Togelius | Generating Levels That Teach Mechanics | 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International
Workshop on Procedural Content Generation (PCG2018) | null | 10.1145/3235765.3235820 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The automatic generation of game tutorials is a challenging AI problem. While
it is possible to generate annotations and instructions that explain to the
player how the game is played, this paper focuses on generating a gameplay
experience that introduces the player to a game mechanic. It evolves small
levels for the Mario AI Framework that can only be beaten by an agent that
knows how to perform specific actions in the game. It uses variations of a
perfect A* agent that are limited in various ways, such as not being able to
jump high or see enemies, to test how failing to do certain actions can stop
the player from beating the level.
| [
{
"version": "v1",
"created": "Wed, 18 Jul 2018 01:47:47 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Sep 2018 21:52:23 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Sep 2018 15:30:52 GMT"
},
{
"version": "v4",
"created": "Mon, 1 Oct 2018 16:15:18 GMT"
}
] | 1,538,438,400,000 | [
[
"Green",
"Michael Cerny",
""
],
[
"Khalifa",
"Ahmed",
""
],
[
"Barros",
"Gabriella A. B.",
""
],
[
"Nealen",
"Andy",
""
],
[
"Togelius",
"Julian",
""
]
] |
1807.06813 | Pier Luca Lanzi | Stefano Di Palma and Pier Luca Lanzi | Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card
Game Scopone | Preprint. Accepted for publication in the IEEE Transaction on Games | IEEE Transactions on Games 2018 | 10.1109/TG.2018.2834618 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.
| [
{
"version": "v1",
"created": "Wed, 18 Jul 2018 08:18:22 GMT"
}
] | 1,532,908,800,000 | [
[
"Di Palma",
"Stefano",
""
],
[
"Lanzi",
"Pier Luca",
""
]
] |
1807.07134 | Sophia Sanborn | Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths | Representational efficiency outweighs action efficiency in human program
induction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The importance of hierarchically structured representations for tractable
planning has long been acknowledged. However, the questions of how people
discover such abstractions and how to define a set of optimal abstractions
remain open. This problem has been explored in cognitive science in the problem
solving literature and in computer science in hierarchical reinforcement
learning. Here, we emphasize an algorithmic perspective on learning
hierarchical representations in which the objective is to efficiently encode
the structure of the problem, or, equivalently, to learn an algorithm with
minimal length. We introduce a novel problem-solving paradigm that links
problem solving and program induction under the Markov Decision Process (MDP)
framework. Using this task, we target the question of whether humans discover
hierarchical solutions by maximizing efficiency in number of actions they
generate or by minimizing the complexity of the resulting representation and
find evidence for the primacy of representational efficiency.
| [
{
"version": "v1",
"created": "Wed, 18 Jul 2018 20:20:40 GMT"
}
] | 1,532,044,800,000 | [
[
"Sanborn",
"Sophia",
""
],
[
"Bourgin",
"David D.",
""
],
[
"Chang",
"Michael",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] |
1807.07389 | Felix Diaz Hermida | F. D\'iaz-Hermida, Juan. C. Vidal | Fuzzy quantification for linguistic data analysis and data mining | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fuzzy quantification is a subtopic of fuzzy logic which deals with the
modelling of the quantified expressions we can find in natural language. Fuzzy
quantifiers have been successfully applied in several fields like fuzzy,
control, fuzzy databases, information retrieval, natural language generation,
etc. Their ability to model and evaluate linguistic expressions in a
mathematical way, makes fuzzy quantifiers very powerful for data analytics and
data mining applications. In this paper we will give a general overview of the
main applications of fuzzy quantifiers in this field as well as some ideas to
use them in new application contexts.
| [
{
"version": "v1",
"created": "Thu, 19 Jul 2018 13:22:01 GMT"
}
] | 1,532,044,800,000 | [
[
"Díaz-Hermida",
"F.",
""
],
[
"Vidal",
"Juan. C.",
""
]
] |
1807.07991 | Oshani Seneviratne | Oshani Seneviratne, Sabbir M. Rashid, Shruthi Chari, James P.
McCusker, Kristin P. Bennett, James A. Hendler, and Deborah L. McGuinness | Knowledge Integration for Disease Characterization: A Breast Cancer
Example | International Semantic Web Conference (Resource Track) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid advancements in cancer research, the information that is
useful for characterizing disease, staging tumors, and creating treatment and
survivorship plans has been changing at a pace that creates challenges when
physicians try to remain current. One example involves increasing usage of
biomarkers when characterizing the pathologic prognostic stage of a breast
tumor. We present our semantic technology approach to support cancer
characterization and demonstrate it in our end-to-end prototype system that
collects the newest breast cancer staging criteria from authoritative oncology
manuals to construct an ontology for breast cancer. Using a tool we developed
that utilizes this ontology, physician-facing applications can be used to
quickly stage a new patient to support identifying risks, treatment options,
and monitoring plans based on authoritative and best practice guidelines.
Physicians can also re-stage existing patients or patient populations, allowing
them to find patients whose stage has changed in a given patient cohort. As new
guidelines emerge, using our proposed mechanism, which is grounded by semantic
technologies for ingesting new data from staging manuals, we have created an
enriched cancer staging ontology that integrates relevant data from several
sources with very little human intervention.
| [
{
"version": "v1",
"created": "Fri, 20 Jul 2018 18:26:29 GMT"
}
] | 1,532,390,400,000 | [
[
"Seneviratne",
"Oshani",
""
],
[
"Rashid",
"Sabbir M.",
""
],
[
"Chari",
"Shruthi",
""
],
[
"McCusker",
"James P.",
""
],
[
"Bennett",
"Kristin P.",
""
],
[
"Hendler",
"James A.",
""
],
[
"McGuinness",
"Deborah L.",
""
]
] |
1807.08060 | Arushi Jain | Arushi Jain, Khimya Khetarpal, Doina Precup | Safe Option-Critic: Learning Safety in the Option-Critic Architecture | To appear at The Knowledge Engineering Review (KER), 2021. Previous
draft appeared in Adaptive Learning Agents (ALA) 2018 workshop held at ICML,
AAMAS in Stockholm. Corrected typos, added references and added extra figures | The Knowledge Engineering Review 36 (2021) e4 | 10.1017/S0269888921000035 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Designing hierarchical reinforcement learning algorithms that exhibit safe
behaviour is not only vital for practical applications but also, facilitates a
better understanding of an agent's decisions. We tackle this problem in the
options framework, a particular way to specify temporally abstract actions
which allow an agent to use sub-policies with start and end conditions. We
consider a behaviour as safe that avoids regions of state-space with high
uncertainty in the outcomes of actions. We propose an optimization objective
that learns safe options by encouraging the agent to visit states with higher
behavioural consistency. The proposed objective results in a trade-off between
maximizing the standard expected return and minimizing the effect of model
uncertainty in the return. We propose a policy gradient algorithm to optimize
the constrained objective function. We examine the quantitative and qualitative
behaviour of the proposed approach in a tabular grid-world, continuous-state
puddle-world, and three games from the Arcade Learning Environment: Ms.Pacman,
Amidar, and Q*Bert. Our approach achieves a reduction in the variance of
return, boosts performance in environments with intrinsic variability in the
reward structure, and compares favorably both with primitive actions as well as
with risk-neutral options.
| [
{
"version": "v1",
"created": "Sat, 21 Jul 2018 00:39:23 GMT"
},
{
"version": "v2",
"created": "Tue, 2 Mar 2021 11:07:34 GMT"
}
] | 1,625,097,600,000 | [
[
"Jain",
"Arushi",
""
],
[
"Khetarpal",
"Khimya",
""
],
[
"Precup",
"Doina",
""
]
] |
1807.08173 | Alberto Rossi | Alberto Rossi, Gianni Barlacchi, Monica Bianchini, Bruno Lepri | Modeling Taxi Drivers' Behaviour for the Next Destination Prediction | preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.
| [
{
"version": "v1",
"created": "Sat, 21 Jul 2018 16:31:03 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Jan 2019 10:34:48 GMT"
}
] | 1,546,992,000,000 | [
[
"Rossi",
"Alberto",
""
],
[
"Barlacchi",
"Gianni",
""
],
[
"Bianchini",
"Monica",
""
],
[
"Lepri",
"Bruno",
""
]
] |
1807.08217 | Keerthana P G | Basel Alghanem, Keerthana P G | Asynchronous Advantage Actor-Critic Agent for Starcraft II | arXiv admin note: text overlap with arXiv:1708.04782 by other authors | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Deep reinforcement learning, and especially the Asynchronous Advantage
Actor-Critic algorithm, has been successfully used to achieve super-human
performance in a variety of video games. Starcraft II is a new challenge for
the reinforcement learning community with the release of pysc2 learning
environment proposed by Google Deepmind and Blizzard Entertainment. Despite
being a target for several AI developers, few have achieved human level
performance. In this project we explain the complexities of this environment
and discuss the results from our experiments on the environment. We have
compared various architectures and have proved that transfer learning can be an
effective paradigm in reinforcement learning research for complex scenarios
requiring skill transfer.
| [
{
"version": "v1",
"created": "Sun, 22 Jul 2018 01:07:43 GMT"
}
] | 1,532,476,800,000 | [
[
"Alghanem",
"Basel",
""
],
[
"G",
"Keerthana P",
""
]
] |
1807.08595 | Zhaohong Deng | Te Zhang, Zhaohong Deng, Dongrui Wu, and Shitong Wang | Multi-View Fuzzy Logic System with the Cooperation between Visible and
Hidden Views | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-view datasets are frequently encountered in learning tasks, such as web
data mining and multimedia information analysis. Given a multi-view dataset,
traditional learning algorithms usually decompose it into several single-view
datasets, from each of which a single-view model is learned. In contrast, a
multi-view learning algorithm can achieve better performance by cooperative
learning on the multi-view data. However, existing multi-view approaches mainly
focus on the views that are visible and ignore the hidden information behind
the visible views, which usually contains some intrinsic information of the
multi-view data, or vice versa. To address this problem, this paper proposes a
multi-view fuzzy logic system, which utilizes both the hidden information
shared by the multiple visible views and the information of each visible view.
Extensive experiments were conducted to validate its effectiveness.
| [
{
"version": "v1",
"created": "Mon, 23 Jul 2018 13:25:37 GMT"
}
] | 1,532,390,400,000 | [
[
"Zhang",
"Te",
""
],
[
"Deng",
"Zhaohong",
""
],
[
"Wu",
"Dongrui",
""
],
[
"Wang",
"Shitong",
""
]
] |
1807.09530 | Karl Kurzer | Karl Kurzer, Chenyang Zhou, J. Marius Z\"ollner | Decentralized Cooperative Planning for Automated Vehicles with
Hierarchical Monte Carlo Tree Search | null | null | 10.1109/IVS.2018.8500712 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments.
| [
{
"version": "v1",
"created": "Wed, 25 Jul 2018 11:20:47 GMT"
}
] | 1,580,774,400,000 | [
[
"Kurzer",
"Karl",
""
],
[
"Zhou",
"Chenyang",
""
],
[
"Zöllner",
"J. Marius",
""
]
] |
1807.09836 | G Gordon Worley IIi | G Gordon Worley III | Robustness to fundamental uncertainty in AGI alignment | null | Journal of Consciousness Studies, Volume 27, Numbers 1-2, 2020,
pp. 225-241(17) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The AGI alignment problem has a bimodal distribution of outcomes with most
outcomes clustering around the poles of total success and existential,
catastrophic failure. Consequently, attempts to solve AGI alignment should, all
else equal, prefer false negatives (ignoring research programs that would have
been successful) to false positives (pursuing research programs that will
unexpectedly fail). Thus, we propose adopting a policy of responding to points
of philosophical and practical uncertainty associated with the alignment
problem by limiting and choosing necessary assumptions to reduce the risk of
false positives. Herein we explore in detail two relevant points of uncertainty
that AGI alignment research hinges on---meta-ethical uncertainty and
uncertainty about mental phenomena---and show how to reduce false positives in
response to them.
| [
{
"version": "v1",
"created": "Wed, 25 Jul 2018 20:11:47 GMT"
},
{
"version": "v2",
"created": "Sat, 24 Aug 2019 10:03:09 GMT"
}
] | 1,581,984,000,000 | [
[
"Worley",
"G Gordon",
"III"
]
] |
1807.09942 | Jake Chandler | Richard Booth, Jake Chandler | On Strengthening the Logic of Iterated Belief Revision: Proper Ordinal
Interval Operators | Extended version of a paper accepted to KR 2018. 40 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Darwiche and Pearl's seminal 1997 article outlined a number of baseline
principles for a logic of iterated belief revision. These principles, the DP
postulates, have been supplemented in a number of alternative ways. Most of the
suggestions made have resulted in a form of `reductionism' that identifies
belief states with orderings of worlds. However, this position has recently
been criticised as being unacceptably strong. Other proposals, such as the
popular principle (P), aka `Independence', characteristic of `admissible'
revision operators, remain commendably more modest. In this paper, we
supplement both the DP postulates and (P) with a number of novel conditions.
While the DP postulates constrain the relation between a prior and a posterior
conditional belief set, our new principles notably govern the relation between
two posterior conditional belief sets obtained from a common prior by different
revisions. We show that operators from the resulting family, which subsumes
both lexicographic and restrained revision, can be represented as relating
belief states that are associated with a `proper ordinal interval' (POI)
assignment, a structure more fine-grained than a simple ordering of worlds. We
close the paper by noting that these operators satisfy iterated versions of a
large number of AGM era postulates, including Superexpansion, that are not
sound for admissible operators in general.
| [
{
"version": "v1",
"created": "Thu, 26 Jul 2018 03:38:43 GMT"
}
] | 1,532,649,600,000 | [
[
"Booth",
"Richard",
""
],
[
"Chandler",
"Jake",
""
]
] |
1807.10299 | Joshua Achiam | Joshua Achiam, Harrison Edwards, Dario Amodei, Pieter Abbeel | Variational Option Discovery Algorithms | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore methods for option discovery based on variational inference and
make two algorithmic contributions. First: we highlight a tight connection
between variational option discovery methods and variational autoencoders, and
introduce Variational Autoencoding Learning of Options by Reinforcement
(VALOR), a new method derived from the connection. In VALOR, the policy encodes
contexts from a noise distribution into trajectories, and the decoder recovers
the contexts from the complete trajectories. Second: we propose a curriculum
learning approach where the number of contexts seen by the agent increases
whenever the agent's performance is strong enough (as measured by the decoder)
on the current set of contexts. We show that this simple trick stabilizes
training for VALOR and prior variational option discovery methods, allowing a
single agent to learn many more modes of behavior than it could with a fixed
context distribution. Finally, we investigate other topics related to
variational option discovery, including fundamental limitations of the general
approach and the applicability of learned options to downstream tasks.
| [
{
"version": "v1",
"created": "Thu, 26 Jul 2018 18:05:45 GMT"
}
] | 1,532,908,800,000 | [
[
"Achiam",
"Joshua",
""
],
[
"Edwards",
"Harrison",
""
],
[
"Amodei",
"Dario",
""
],
[
"Abbeel",
"Pieter",
""
]
] |
1807.10847 | Bryan Head | Bryan Head and Uri Wilensky | Agent cognition through micro-simulations: Adaptive and tunable
intelligence with NetLogo LevelSpace | Model source code available here:
https://github.com/qiemem/Wolf-Sheep-Predation-Micro-Sims, In: Unifying
Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity.
Springer, Cham | null | 10.1007/978-3-319-96661-8_7 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present a method of endowing agents in an agent-based model (ABM) with
sophisticated cognitive capabilities and a naturally tunable level of
intelligence. Often, ABMs use random behavior or greedy algorithms for
maximizing objectives (such as a predator always chasing after the closest
prey). However, random behavior is too simplistic in many circumstances and
greedy algorithms, as well as classic AI planning techniques, can be brittle in
the context of the unpredictable and emergent situations in which agents may
find themselves. Our method, called agent-centric Monte Carlo cognition
(ACMCC), centers around using a separate agent-based model to represent the
agents' cognition. This model is then used by the agents in the primary model
to predict the outcomes of their actions, and thus guide their behavior. To
that end, we have implemented our method in the NetLogo agent-based modeling
platform, using the recently released LevelSpace extension, which we developed
to allow NetLogo models to interact with other NetLogo models. As an
illustrative example, we extend the Wolf Sheep Predation model (included with
NetLogo) by using ACMCC to guide animal behavior, and analyze the impact on
agent performance and model dynamics. We find that ACMCC provides a reliable
and understandable method of controlling agent intelligence, and has a large
impact on agent performance and model dynamics even at low settings.
| [
{
"version": "v1",
"created": "Fri, 27 Jul 2018 22:33:40 GMT"
}
] | 1,532,995,200,000 | [
[
"Head",
"Bryan",
""
],
[
"Wilensky",
"Uri",
""
]
] |
1807.10935 | Xiaoyu Ge | Xiaoyu Ge and Jochen Renz and Hua Hua | Towards Explainable Inference about Object Motion using Qualitative
Reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The capability of making explainable inferences regarding physical processes
has long been desired. One fundamental physical process is object motion.
Inferring what causes the motion of a group of objects can even be a
challenging task for experts, e.g., in forensics science. Most of the work in
the literature relies on physics simulation to draw such infer- ences. The
simulation requires a precise model of the under- lying domain to work well and
is essentially a black-box from which one can hardly obtain any useful
explanation. By contrast, qualitative reasoning methods have the advan- tage in
making transparent inferences with ambiguous infor- mation, which makes it
suitable for this task. However, there has been no suitable qualitative theory
proposed for object motion in three-dimensional space. In this paper, we take
this challenge and develop a qualitative theory for the motion of rigid
objects. Based on this theory, we develop a reasoning method to solve a very
interesting problem: Assuming there are several objects that were initially at
rest and now have started to move. We want to infer what action causes the
movement of these objects.
| [
{
"version": "v1",
"created": "Sat, 28 Jul 2018 13:35:39 GMT"
}
] | 1,532,995,200,000 | [
[
"Ge",
"Xiaoyu",
""
],
[
"Renz",
"Jochen",
""
],
[
"Hua",
"Hua",
""
]
] |
1807.11615 | Diego Calvanese | Diego Calvanese, Marlon Dumas, Fabrizio Maria Maggi, Marco Montali | Semantic DMN: Formalizing and Reasoning About Decisions in the Presence
of Background Knowledge | Under consideration for publication in Theory and Practice of Logic
Programming (TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Decision Model and Notation (DMN) is a recent OMG standard for the
elicitation and representation of decision models, and for managing their
interconnection with business processes. DMN builds on the notion of decision
tables, and their combination into more complex decision requirements graphs
(DRGs), which bridge between business process models and decision logic models.
DRGs may rely on additional, external business knowledge models, whose
functioning is not part of the standard. In this work, we consider one of the
most important types of business knowledge, namely background knowledge that
conceptually accounts for the structural aspects of the domain of interest, and
propose decision knowledge bases (DKBs), which semantically combine DRGs
modeled in DMN, and domain knowledge captured by means of first-order logic
with datatypes. We provide a logic-based semantics for such an integration, and
formalize different DMN reasoning tasks for DKBs. We then consider background
knowledge formulated as a description logic ontology with datatypes, and show
how the main verification tasks for DMN in this enriched setting can be
formalized as standard DL reasoning services, and actually carried out in
ExpTime. We discuss the effectiveness of our framework on a case study in
maritime security.
| [
{
"version": "v1",
"created": "Tue, 31 Jul 2018 00:27:08 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Aug 2018 18:39:08 GMT"
},
{
"version": "v3",
"created": "Fri, 14 Sep 2018 22:34:40 GMT"
}
] | 1,537,228,800,000 | [
[
"Calvanese",
"Diego",
""
],
[
"Dumas",
"Marlon",
""
],
[
"Maggi",
"Fabrizio Maria",
""
],
[
"Montali",
"Marco",
""
]
] |
1808.00089 | Biplav Srivastava | Biplav Srivastava and Francesca Rossi | Towards Composable Bias Rating of AI Services | 6 pages, appeared in 2018 ACM/AAAI Conference on AI Ethics and
Society (AIES 2018) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new wave of decision-support systems are being built today using AI
services that draw insights from data (like text and video) and incorporate
them in human-in-the-loop assistance. However, just as we expect humans to be
ethical, the same expectation needs to be met by automated systems that
increasingly get delegated to act on their behalf. A very important aspect of
an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias
occurs when the data distribution is not representative enough of the natural
phenomenon one wants to model and reason about. The possibly biased behavior of
a service is hard to detect and handle if the AI service is merely being used
and not developed from scratch, since the training data set is not available.
In this situation, we envisage a 3rd party rating agency that is independent of
the API producer or consumer and has its own set of biased and unbiased data,
with customizable distributions. We propose a 2-step rating approach that
generates bias ratings signifying whether the AI service is unbiased
compensating, data-sensitive biased, or biased. The approach also works on
composite services. We implement it in the context of text translation and
report interesting results.
| [
{
"version": "v1",
"created": "Tue, 31 Jul 2018 22:15:13 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Jan 2019 19:26:28 GMT"
}
] | 1,547,596,800,000 | [
[
"Srivastava",
"Biplav",
""
],
[
"Rossi",
"Francesca",
""
]
] |
1808.00222 | Amir Ramezani Dooraki Mr | Amir Ramezani Dooraki | Experience, Imitation and Reflection; Confucius' Conjecture and Machine
Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence recently had a great advancements caused by the
emergence of new processing power and machine learning methods. Having said
that, the learning capability of artificial intelligence is still at its
infancy comparing to the learning capability of human and many animals. Many of
the current artificial intelligence applications can only operate in a very
orchestrated, specific environments with an extensive training set that exactly
describes the conditions that will occur during execution time. Having that in
mind, and considering the several existing machine learning methods this
question rises that 'What are some of the best ways for a machine to learn?'
Regarding the learning methods of human, Confucius' point of view is that they
are by experience, imitation and reflection. This paper tries to explore and
discuss regarding these three ways of learning and their implementations in
machines by having a look at how they happen in minds.
| [
{
"version": "v1",
"created": "Wed, 1 Aug 2018 08:27:27 GMT"
}
] | 1,533,168,000,000 | [
[
"Dooraki",
"Amir Ramezani",
""
]
] |
1808.00417 | Carmine Dodaro | Carmine Dodaro, Philip Gasteiger, Kristian Reale, Francesco Ricca,
Konstantin Schekotihin | Debugging Non-Ground ASP Programs: Technique and Graphical Tools | 27 pages, 6 figures, Under consideration in Theory and Practice of
Logic Programming (TPLP) | Theory and Practice of Logic Programming 19 (2019) 290-316 | 10.1017/S1471068418000492 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Answer Set Programming (ASP) is one of the major declarative programming
paradigms in the area of logic programming and non-monotonic reasoning. Despite
that ASP features a simple syntax and an intuitive semantics, errors are common
during the development of ASP programs. In this paper we propose a novel
debugging approach allowing for interactive localization of bugs in non-ground
programs. The new approach points the user directly to a set of non-ground
rules involved in the bug, which might be refined (up to the point in which the
bug is easily identified) by asking the programmer a sequence of questions on
an expected answer set. The approach has been implemented on top of the ASP
solver WASP. The resulting debugger has been complemented by a user-friendly
graphical interface, and integrated in ASPIDE, a rich IDE for answer set
programs. In addition, an empirical analysis shows that the new debugger is not
affected by the grounding blowup limiting the application of previous
approaches based on meta-programming. Under consideration in Theory and
Practice of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Wed, 1 Aug 2018 16:59:01 GMT"
}
] | 1,582,070,400,000 | [
[
"Dodaro",
"Carmine",
""
],
[
"Gasteiger",
"Philip",
""
],
[
"Reale",
"Kristian",
""
],
[
"Ricca",
"Francesco",
""
],
[
"Schekotihin",
"Konstantin",
""
]
] |
1808.00434 | Michael Cochez | Martina Garofalo and Maria Angela Pellegrino and Abdulrahman Altabba
and Michael Cochez | Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use
Cases | Accepted for publication in NATO Science Series. arXiv admin note:
text overlap with arXiv:1709.07604 by other authors | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Industry is evolving towards Industry 4.0, which holds the promise of
increased flexibility in manufacturing, better quality and improved
productivity. A core actor of this growth is using sensors, which must capture
data that can used in unforeseen ways to achieve a performance not achievable
without them. However, the complexity of this improved setting is much greater
than what is currently used in practice. Hence, it is imperative that the
management cannot only be performed by human labor force, but part of that will
be done by automated algorithms instead. A natural way to represent the data
generated by this large amount of sensors, which are not acting measuring
independent variables, and the interaction of the different devices is by using
a graph data model. Then, machine learning could be used to aid the Industry
4.0 system to, for example, perform predictive maintenance. However, machine
learning directly on graphs, needs feature engineering and has scalability
issues. In this paper we discuss methods to convert (embed) the graph in a
vector space, such that it becomes feasible to use traditional machine learning
methods for Industry 4.0 settings.
| [
{
"version": "v1",
"created": "Tue, 31 Jul 2018 11:26:46 GMT"
}
] | 1,533,168,000,000 | [
[
"Garofalo",
"Martina",
""
],
[
"Pellegrino",
"Maria Angela",
""
],
[
"Altabba",
"Abdulrahman",
""
],
[
"Cochez",
"Michael",
""
]
] |
1808.01262 | Hendrik Baier | Timothy Atkinson, Hendrik Baier, Tara Copplestone, Sam Devlin, Jerry
Swan | The Text-Based Adventure AI Competition | updated to journal version | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence
in Games, the authors of this paper ran a competition for agents that can play
classic text-based adventure games. This competition fills a gap in existing
game AI competitions that have typically focussed on traditional card/board
games or modern video games with graphical interfaces. By providing a platform
for evaluating agents in text-based adventures, the competition provides a
novel benchmark for game AI with unique challenges for natural language
understanding and generation. This paper summarises the three competitions ran
in 2016, 2017, and 2018 (including details of open source implementations of
both the competition framework and our competitors) and presents the results of
an improved evaluation of these competitors across 20 games.
| [
{
"version": "v1",
"created": "Fri, 3 Aug 2018 17:19:28 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Oct 2018 09:56:02 GMT"
},
{
"version": "v3",
"created": "Fri, 19 Oct 2018 10:32:52 GMT"
},
{
"version": "v4",
"created": "Thu, 24 Jan 2019 14:40:42 GMT"
}
] | 1,548,374,400,000 | [
[
"Atkinson",
"Timothy",
""
],
[
"Baier",
"Hendrik",
""
],
[
"Copplestone",
"Tara",
""
],
[
"Devlin",
"Sam",
""
],
[
"Swan",
"Jerry",
""
]
] |
1808.01690 | Hongzhi Wang | Sifan Liu and Hongzhi Wang | Error Detection in a Large-Scale Lexical Taxonomy | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge base (KB) is an important aspect in artificial intelligence. One
significant challenge faced by KB construction is that it contains many noises,
which prevents its effective usage. Even though some KB cleansing algorithms
have been proposed, they focus on the structure of the knowledge graph and
neglect the relation between the concepts, which could be helpful to discover
wrong relations in KB. Motived by this, we measure the relation of two concepts
by the distance between their corresponding instances and detect errors within
the intersection of the conflicting concept sets. For efficient and effective
knowledge base cleansing, we first apply a distance-based Model to determine
the conflicting concept sets using two different methods. Then, we propose and
analyze several algorithms on how to detect and repairing the errors based on
our model, where we use hash method for an efficient way to calculate distance.
Experimental results demonstrate that the proposed approaches could cleanse the
knowledge bases efficiently and effectively.
| [
{
"version": "v1",
"created": "Sun, 5 Aug 2018 21:53:40 GMT"
}
] | 1,533,600,000,000 | [
[
"Liu",
"Sifan",
""
],
[
"Wang",
"Hongzhi",
""
]
] |
1808.03130 | Filip Murlak | Tomasz Gogacz, Yazmin Ib\'a\~nez-Garc\'ia, and Filip Murlak | Finite Query Answering in Expressive Description Logics with Transitive
Roles | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of finite ontology mediated query answering (FOMQA), the
variant of OMQA where the represented world is assumed to be finite, and thus
only finite models of the ontology are considered. We adopt the most typical
setting with unions of conjunctive queries and ontologies expressed in
description logics (DLs). The study of FOMQA is relevant in settings that are
not finitely controllable. This is the case not only for DLs without the finite
model property, but also for those allowing transitive role declarations. When
transitive roles are allowed, evaluating queries is challenging: FOMQA is
undecidable for SHOIF and only known to be decidable for the Horn fragment of
ALCIF. We show decidability of FOMQA for three proper fragments of SOIF: SOI,
SOF, and SIF. Our approach is to characterise models relevant for deciding
finite query entailment. Relying on a certain regularity of these models, we
develop automata-based decision procedures with optimal complexity bounds.
| [
{
"version": "v1",
"created": "Thu, 9 Aug 2018 12:54:04 GMT"
}
] | 1,533,859,200,000 | [
[
"Gogacz",
"Tomasz",
""
],
[
"Ibáñez-García",
"Yazmin",
""
],
[
"Murlak",
"Filip",
""
]
] |
1808.03454 | Moshe BenBassat Professor | Moshe BenBassat | AIQ: Measuring Intelligence of Business AI Software | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Focusing on Business AI, this article introduces the AIQ quadrant that
enables us to measure AI for business applications in a relative comparative
manner, i.e. to judge that software A has more or less intelligence than
software B. Recognizing that the goal of Business software is to maximize value
in terms of business results, the dimensions of the quadrant are the key
factors that determine the business value of AI software: Level of Output
Quality (Smartness) and Level of Automation. The use of the quadrant is
illustrated by several software solutions to support the real life business
challenge of field service scheduling. The role of machine learning and
conversational digital assistants in increasing the business value are also
discussed and illustrated with a recent integration of existing intelligent
digital assistants for factory floor decision making with the new version of
Google Glass. Such hands free AI solutions elevate the AIQ level to its
ultimate position.
| [
{
"version": "v1",
"created": "Fri, 10 Aug 2018 08:40:32 GMT"
}
] | 1,534,118,400,000 | [
[
"BenBassat",
"Moshe",
""
]
] |
1808.03519 | Andreas Niederquell | Andreas Niederquell | Self-Adaptive Systems in Organic Computing: Strategies for
Self-Improvement | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the intensified use of intelligent things, the demands on the
technological systems are increasing permanently. A possible approach to meet
the continuously changing challenges is to shift the system integration from
design to run-time by using adaptive systems. Diverse adaptivity properties,
so-called self-* properties, form the basis of these systems and one of the
properties is self-improvement. It describes the ability of a system not only
to adapt to a changing environment according to a predefined model, but also
the capability to adapt the adaptation logic of the whole system. In this
paper, a closer look is taken at the structure of self-adaptive systems.
Additionally, the systems' ability to improve themselves during run-time is
described from the perspective of Organic Computing. Furthermore, four
different strategies for self-improvement are presented, following the taxonomy
of self-adaptation suggested by Christian Krupitzer et al.
| [
{
"version": "v1",
"created": "Wed, 8 Aug 2018 13:56:29 GMT"
}
] | 1,534,118,400,000 | [
[
"Niederquell",
"Andreas",
""
]
] |
1808.03611 | Zhenxing Xu | Zhen-Xing Xu, Kun He, Chu-Min Li | An Iterative Path-Breaking Approach with Mutation and Restart Strategies
for the MAX-SAT Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although Path-Relinking is an effective local search method for many
combinatorial optimization problems, its application is not straightforward in
solving the MAX-SAT, an optimization variant of the satisfiability problem
(SAT) that has many real-world applications and has gained more and more
attention in academy and industry. Indeed, it was not used in any recent
competitive MAX-SAT algorithms in our knowledge. In this paper, we propose a
new local search algorithm called IPBMR for the MAX-SAT, that remedies the
drawbacks of the Path-Relinking method by using a careful combination of three
components: a new strategy named Path-Breaking to avoid unpromising regions of
the search space when generating trajectories between two elite solutions; a
weak and a strong mutation strategies, together with restarts, to diversify the
search; and stochastic path generating steps to avoid premature local optimum
solutions. We then present experimental results to show that IPBMR outperforms
two of the best state-of-the-art MAX-SAT solvers, and an empirical
investigation to identify and explain the effect of the three components in
IPBMR.
| [
{
"version": "v1",
"created": "Fri, 10 Aug 2018 16:33:13 GMT"
}
] | 1,534,118,400,000 | [
[
"Xu",
"Zhen-Xing",
""
],
[
"He",
"Kun",
""
],
[
"Li",
"Chu-Min",
""
]
] |
1808.03644 | Roman Yampolskiy | Micha\"el Trazzi, Roman V. Yampolskiy | Building Safer AGI by introducing Artificial Stupidity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) achieved super-human performance in a broad
variety of domains. We say that an AI is made Artificially Stupid on a task
when some limitations are deliberately introduced to match a human's ability to
do the task. An Artificial General Intelligence (AGI) can be made safer by
limiting its computing power and memory, or by introducing Artificial Stupidity
on certain tasks. We survey human intellectual limits and give recommendations
for which limits to implement in order to build a safe AGI.
| [
{
"version": "v1",
"created": "Sat, 11 Aug 2018 00:14:33 GMT"
}
] | 1,534,204,800,000 | [
[
"Trazzi",
"Michaël",
""
],
[
"Yampolskiy",
"Roman V.",
""
]
] |
1808.03736 | Renata Wong | Renata Wong | An Implementation, Empirical Evaluation and Proposed Improvement for
Bidirectional Splitting Method for Argumentation Frameworks under Stable
Semantics | 19 pages | Journal of Artificial Intelligence and Applications, Vol.9, No.4,
2018, pp. 11-29 | 10.5121/ijaia.2018.9402 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Abstract argumentation frameworks are formal systems that facilitate
obtaining conclusions from non-monotonic knowledge systems. Within such a
system, an argumentation semantics is defined as a set of arguments with some
desired qualities, for example, that the elements are not in conflict with each
other. Splitting an argumentation framework can efficiently speed up the
computation of argumentation semantics. With respect to stable semantics, two
methods have been proposed to split an argumentation framework either in a
unidirectional or bidirectional fashion. The advantage of bidirectional
splitting is that it is not structure-dependent and, unlike unidirectional
splitting, it can be used for frameworks consisting of a single strongly
connected component. Bidirectional splitting makes use of a minimum cut. In
this paper, we implement and test the performance of the bidirectional
splitting method, along with two types of graph cut algorithms. Experimental
data suggest that using a minimum cut will not improve the performance of
computing stable semantics in most cases. Hence, instead of a minimum cut, we
propose to use a balanced cut, where the framework is split into two
sub-frameworks of equal size. Experimental results conducted on bidirectional
splitting using the balanced cut show a significant improvement in the
performance of computing semantics.
| [
{
"version": "v1",
"created": "Sat, 11 Aug 2018 01:52:57 GMT"
}
] | 1,534,204,800,000 | [
[
"Wong",
"Renata",
""
]
] |
1808.03948 | Florentin Smarandache | Florentin Smarandache | Plithogeny, Plithogenic Set, Logic, Probability, and Statistics | 141 pages, Physical Plithogenic Set (approved), 71st Annual Gaseous
Electronics Conference, American Physical Society, November 2018, Portland,
Oregon, and Pons, Brussels, 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this book we introduce the plithogenic set (as generalization of crisp,
fuzzy, intuitionistic fuzzy, and neutrosophic sets), plithogenic logic (as
generalization of classical, fuzzy, intuitionistic fuzzy, and neutrosophic
logics), plithogenic probability (as generalization of classical, imprecise,
and neutrosophic probabilities), and plithogenic statistics (as generalization
of classical, and neutrosophic statistics). Plithogenic Set is a set whose
elements are characterized by one or more attributes, and each attribute may
have many values. An attribute value v has a corresponding (fuzzy,
intuitionistic fuzzy, or neutrosophic) degree of appurtenance d(x,v) of the
element x, to the set P, with respect to some given criteria. In order to
obtain a better accuracy for the plithogenic aggregation operators in the
plithogenic set, logic, probability and for a more exact inclusion (partial
order), a (fuzzy, intuitionistic fuzzy, or neutrosophic) contradiction
(dissimilarity) degree is defined between each attribute value and the dominant
(most important) attribute value. The plithogenic intersection and union are
linear combinations of the fuzzy operators tnorm and tconorm, while the
plithogenic complement, inclusion, equality are influenced by the attribute
values contradiction (dissimilarity) degrees. Formal definitions of plithogenic
set, logic, probability, statistics are presented into the book, followed by
plithogenic aggregation operators, various theorems related to them, and
afterwards examples and applications of these new concepts in our everyday
life.
| [
{
"version": "v1",
"created": "Sun, 12 Aug 2018 14:14:45 GMT"
}
] | 1,534,204,800,000 | [
[
"Smarandache",
"Florentin",
""
]
] |
1808.04043 | Shizhe Zhao | Shizhe Zhao, Daniel D. Harabor, David Taniar | Faster and More Robust Mesh-based Algorithms for Obstacle k-Nearest
Neighbour | submitted on Journal of Artificial Intelligence Research 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are interested in the problem of finding $k$ nearest neighbours in the
plane and in the presence of polygonal obstacles ($\textit{OkNN}$). Widely used
algorithms for OkNN are based on incremental visibility graphs, which means
they require costly and online visibility checking and have worst-case
quadratic running time. Recently $\mathbf{Polyanya}$, a fast point-to-point
pathfinding algorithm was proposed which avoids the disadvantages of visibility
graphs by searching over an alternative data structure known as a navigation
mesh. Previously, we adapted $\mathbf{Polyanya}$ to multi-target scenarios by
developing two specialised heuristic functions: the $\mathbf{Interval
heuristic}$ $h_v$ and the $\mathbf{Target heuristic}$ $h_t$. Though these
methods outperform visibility graph algorithms by orders of magnitude in all
our experiments they are not robust: $h_v$ expands many redundant nodes when
the set of neighbours is small while $h_t$ performs poorly when the set of
neighbours is large. In this paper, we propose new algorithms and heuristics
for OkNN which perform well regardless of neighbour density.
| [
{
"version": "v1",
"created": "Mon, 13 Aug 2018 02:05:27 GMT"
}
] | 1,534,204,800,000 | [
[
"Zhao",
"Shizhe",
""
],
[
"Harabor",
"Daniel D.",
""
],
[
"Taniar",
"David",
""
]
] |
1808.04247 | Truyen Tran | Trang Pham, Truyen Tran, Svetha Venkatesh | Relational dynamic memory networks | Previous versions published in 3rd Representation Learning for Graphs
Workshop (ReLiG 2017), ICPR'18, and NeurIPS'18 Workshop on machine learning
for molecules and materials; arXiv admin note: text overlap with
arXiv:1801.02622" | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural networks excel in detecting regular patterns but are less successful
in representing and manipulating complex data structures, possibly due to the
lack of an external memory. This has led to the recent development of a new
line of architectures known as Memory-Augmented Neural Networks (MANNs), each
of which consists of a neural network that interacts with an external memory
matrix. However, this RAM-like memory matrix is unstructured and thus does not
naturally encode structured objects. Here we design a new MANN dubbed
Relational Dynamic Memory Network (RMDN) to bridge the gap. Like existing
MANNs, RMDN has a neural controller but its memory is structured as
multi-relational graphs. RMDN uses the memory to represent and manipulate
graph-structured data in response to query; and as a neural network, RMDN is
trainable from labeled data. Thus RMDN learns to answer queries about a set of
graph-structured objects without explicit programming. We evaluate the
capability of RMDN on several important prediction problems, including software
vulnerability, molecular bioactivity and chemical-chemical interaction. Results
demonstrate the efficacy of the proposed model.
| [
{
"version": "v1",
"created": "Fri, 10 Aug 2018 00:01:34 GMT"
},
{
"version": "v2",
"created": "Tue, 30 Oct 2018 02:17:15 GMT"
},
{
"version": "v3",
"created": "Wed, 28 Nov 2018 03:19:09 GMT"
}
] | 1,543,449,600,000 | [
[
"Pham",
"Trang",
""
],
[
"Tran",
"Truyen",
""
],
[
"Venkatesh",
"Svetha",
""
]
] |
1808.04317 | Hugo Scurti | Hugo Scurti, Clark Verbrugge | Generating Paths with WFC | 7 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motion plans are often randomly generated for minor game NPCs. Repetitive or
regular movements, however, require non-trivial programming effort and/or
integration with a pathing system. We here describe an example-based approach
to path generation that requires little or no additional programming effort.
Our work modifies the Wave Function Collapse (WFC) algorithm, adapting it to
produce pathing plans similar to an input sketch. We show how simple sketch
modifications control path characteristics, and demonstrate feasibility through
a usable Unity implementation.
| [
{
"version": "v1",
"created": "Mon, 13 Aug 2018 16:21:00 GMT"
}
] | 1,534,204,800,000 | [
[
"Scurti",
"Hugo",
""
],
[
"Verbrugge",
"Clark",
""
]
] |
1808.04527 | Joohyung Lee | Joohyung Lee, Yi Wang | Weight Learning in a Probabilistic Extension of Answer Set Programs | Technical Report of the paper to appear in 16th International
Conference on Principles of Knowledge Representation and Reasoning | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | LPMLN is a probabilistic extension of answer set programs with the weight
scheme derived from that of Markov Logic. Previous work has shown how inference
in LPMLN can be achieved. In this paper, we present the concept of weight
learning in LPMLN and learning algorithms for LPMLN derived from those for
Markov Logic. We also present a prototype implementation that uses answer set
solvers for learning as well as some example domains that illustrate distinct
features of LPMLN learning. Learning in LPMLN is in accordance with the stable
model semantics, thereby it learns parameters for probabilistic extensions of
knowledge-rich domains where answer set programming has shown to be useful but
limited to the deterministic case, such as reachability analysis and reasoning
about actions in dynamic domains. We also apply the method to learn the
parameters for probabilistic abductive reasoning about actions.
| [
{
"version": "v1",
"created": "Tue, 14 Aug 2018 05:16:41 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Oct 2018 03:34:18 GMT"
}
] | 1,539,129,600,000 | [
[
"Lee",
"Joohyung",
""
],
[
"Wang",
"Yi",
""
]
] |
1808.04600 | Sagar Uprety Mr. | Sagar Uprety, Dawei Song | Reconciling Irrational Human Behavior with AI based Decision Making: A
Quantum Probabilistic Approach | Published at the Workshop on AI and Computational Psychology at
IJCAI-ECAI 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are many examples of human decision making which cannot be modeled by
classical probabilistic and logic models, on which the current AI systems are
based. Hence the need for a modeling framework which can enable intelligent
systems to detect and predict cognitive biases in human decisions to facilitate
better human-agent interaction. We give a few examples of irrational behavior
and use a generalized probabilistic model inspired by the mathematical
framework of Quantum Theory to model and explain such behavior.
| [
{
"version": "v1",
"created": "Tue, 14 Aug 2018 09:47:16 GMT"
}
] | 1,534,291,200,000 | [
[
"Uprety",
"Sagar",
""
],
[
"Song",
"Dawei",
""
]
] |
1808.04620 | Javier \'Alvez | Javier \'Alvez and Itziar Gonzalez-Dios and German Rigau | Applying the Closed World Assumption to SUMO-based FOL Ontologies for
Effective Commonsense Reasoning | 7 pages, 2 figure, 4 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most commonly, the Open World Assumption is adopted as a standard strategy
for the design, construction and use of ontologies. This strategy limits the
inferencing capabilities of any system because non-asserted statements (missing
knowledge) could be assumed to be alternatively true or false. As we will
demonstrate, this is especially the case of first-order logic (FOL) ontologies
where non-asserted statements is nowadays one of the main obstacles to its
practical application in automated commonsense reasoning tasks. In this paper,
we investigate the application of the Closed World Assumption (CWA) to enable a
better exploitation of FOL ontologies by using state-of-the-art automated
theorem provers. To that end, we explore different CWA formulations for the
structural knowledge encoded in a FOL translation of the SUMO ontology,
discovering that almost 30 % of the structural knowledge is missing. We
evaluate these formulations on a practical experimentation using a very large
commonsense benchmark obtained from WordNet through its mapping to SUMO. The
results show that the competency of the ontology improves more than 50 % when
reasoning under the CWA. Thus, applying the CWA automatically to FOL ontologies
reduces their ambiguity and more commonsense questions can be answered
| [
{
"version": "v1",
"created": "Tue, 14 Aug 2018 10:41:14 GMT"
},
{
"version": "v2",
"created": "Wed, 4 Mar 2020 09:02:27 GMT"
}
] | 1,583,366,400,000 | [
[
"Álvez",
"Javier",
""
],
[
"Gonzalez-Dios",
"Itziar",
""
],
[
"Rigau",
"German",
""
]
] |
1808.04758 | James Cussens | James Cussens | Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given (1) a set of clauses $T$ in some first-order language $\cal L$ and (2)
a cost function $c : B_{{\cal L}} \rightarrow \mathbb{R}_{+}$, mapping each
ground atom in the Herbrand base $B_{{\cal L}}$ to a non-negative real, then
the problem of finding a minimal cost Herbrand model is to either find a
Herbrand model $\cal I$ of $T$ which is guaranteed to minimise the sum of the
costs of true ground atoms, or establish that there is no Herbrand model for
$T$. A branch-cut-and-price integer programming (IP) approach to solving this
problem is presented. Since the number of ground instantiations of clauses and
the size of the Herbrand base are both infinite in general, we add the
corresponding IP constraints and IP variables `on the fly' via `cutting' and
`pricing' respectively. In the special case of a finite Herbrand base we show
that adding all IP variables and constraints from the outset can be
advantageous, showing that a challenging Markov logic network MAP problem can
be solved in this way if encoded appropriately.
| [
{
"version": "v1",
"created": "Tue, 14 Aug 2018 15:45:01 GMT"
}
] | 1,534,291,200,000 | [
[
"Cussens",
"James",
""
]
] |
1808.04946 | Minzhong Luo | MinZhong Luo, Li Liu | Automatic Derivation Of Formulas Using Reforcement Learning | conference | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | This paper presents an artificial intelligence algorithm that can be used to
derive formulas from various scientific disciplines called automatic derivation
machine. First, the formula is abstractly expressed as a multiway tree model,
and then each step of the formula derivation transformation is abstracted as a
mapping of multiway trees. Derivation steps similar can be expressed as a
reusable formula template by a multiway tree map. After that, the formula
multiway tree is eigen-encoded to feature vectors construct the feature space
of formulas, the Q-learning model using in this feature space can achieve the
derivation by making training data from derivation process. Finally, an
automatic formula derivation machine is made to choose the next derivation step
based on the current state and object. We also make an example about the
nuclear reactor physics problem to show how the automatic derivation machine
works.
| [
{
"version": "v1",
"created": "Wed, 15 Aug 2018 02:08:23 GMT"
}
] | 1,534,377,600,000 | [
[
"Luo",
"MinZhong",
""
],
[
"Liu",
"Li",
""
]
] |
1808.05249 | Ramon Fraga Pereira | Leonardo Amado, Jo\~ao Paulo Aires, Ramon Fraga Pereira, Maur\'icio C.
Magnaguagno, Roger Granada, Felipe Meneguzzi | LSTM-Based Goal Recognition in Latent Space | Added/Fixed some references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approaches to goal recognition have progressively relaxed the requirements
about the amount of domain knowledge and available observations, yielding
accurate and efficient algorithms capable of recognizing goals. However, to
recognize goals in raw data, recent approaches require either human engineered
domain knowledge, or samples of behavior that account for almost all actions
being observed to infer possible goals. This is clearly too strong a
requirement for real-world applications of goal recognition, and we develop an
approach that leverages advances in recurrent neural networks to perform goal
recognition as a classification task, using encoded plan traces for training.
We empirically evaluate our approach against the state-of-the-art in goal
recognition with image-based domains, and discuss under which conditions our
approach is superior to previous ones.
| [
{
"version": "v1",
"created": "Wed, 15 Aug 2018 18:52:19 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Aug 2018 19:50:55 GMT"
}
] | 1,534,896,000,000 | [
[
"Amado",
"Leonardo",
""
],
[
"Aires",
"João Paulo",
""
],
[
"Pereira",
"Ramon Fraga",
""
],
[
"Magnaguagno",
"Maurício C.",
""
],
[
"Granada",
"Roger",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
1808.05322 | Thierry Denoeux | Thierry Denoeux | Decision-Making with Belief Functions: a Review | null | International Journal of Approximate Reasoning, vol. 109, Pages
87-110, 2019 | 10.1016/j.ijar.2019.03.009 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approaches to decision-making under uncertainty in the belief function
framework are reviewed. Most methods are shown to blend criteria for decision
under ignorance with the maximum expected utility principle of Bayesian
decision theory. A distinction is made between methods that construct a
complete preference relation among acts, and those that allow incomparability
of some acts due to lack of information. Methods developed in the imprecise
probability framework are applicable in the Dempster-Shafer context and are
also reviewed. Shafer's constructive decision theory, which substitutes the
notion of goal for that of utility, is described and contrasted with other
approaches. The paper ends by pointing out the need to carry out deeper
investigation of fundamental issues related to decision-making with belief
functions and to assess the descriptive, normative and prescriptive values of
the different approaches.
| [
{
"version": "v1",
"created": "Thu, 16 Aug 2018 01:52:46 GMT"
},
{
"version": "v2",
"created": "Thu, 12 Dec 2019 08:02:05 GMT"
}
] | 1,576,195,200,000 | [
[
"Denoeux",
"Thierry",
""
]
] |
1808.06217 | Jim Davies | Vincent Breault, Sebastien Ouellet, Jim Davies | Let CONAN tell you a story: Procedural quest generation | ten pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work proposes an engine for the Creation Of Novel Adventure Narrative
(CONAN), which is a procedural quest generator. It uses a planning approach to
story generation. The engine is tested on its ability to create quests, which
are sets of actions that must be performed in order to achieve a certain goal,
usually for a reward. The engine takes in a world description represented as a
set of facts, including characters, locations, and items, and generates quests
according to the state of the world and the preferences of the characters. We
evaluate quests through the classification of the motivations behind the
quests, based on the sequences of actions required to complete the quests. We
also compare different world descriptions and analyze the difference in
motivations for the quests produced by the engine. Compared against human
structural quest analysis, the current engine was found to be able to replicate
the quest structures found in commercial video game quests.
| [
{
"version": "v1",
"created": "Sun, 19 Aug 2018 14:39:46 GMT"
}
] | 1,534,809,600,000 | [
[
"Breault",
"Vincent",
""
],
[
"Ouellet",
"Sebastien",
""
],
[
"Davies",
"Jim",
""
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.