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21,102 | Providing Self-Aware Systems with Reflexivity | cs.AI | We propose a new type of self-aware systems inspired by ideas from
higher-order theories of consciousness. First, we discussed the crucial
distinction between introspection and reflexion. Then, we focus on
computational reflexion as a mechanism by which a computer program can inspect
its own code at every stage of the ... | computer science |
21,103 | Learning to Teach Reinforcement Learning Agents | cs.AI | In this article we study the transfer learning model of action advice under a
budget. We focus on reinforcement learning teachers providing action advice to
heterogeneous students playing the game of Pac-Man under a limited advice
budget. First, we examine several critical factors affecting advice quality in
this setti... | computer science |
21,104 | Empirical Evaluation of Abstract Argumentation: Supporting the Need for
Bipolar and Probabilistic Approaches | cs.AI | In dialogical argumentation it is often assumed that the involved parties
always correctly identify the intended statements posited by each other,
realize all of the associated relations, conform to the three acceptability
states (accepted, rejected, undecided), adjust their views when new and correct
information comes... | computer science |
21,105 | Learning to Infer Graphics Programs from Hand-Drawn Images | cs.AI | We introduce a model that learns to convert simple hand drawings into
graphics programs written in a subset of \LaTeX. The model combines techniques
from deep learning and program synthesis. We learn a convolutional neural
network that proposes plausible drawing primitives that explain an image. These
drawing primitive... | computer science |
21,106 | A Vision For Continuous Automated Game Design | cs.AI | ANGELINA is an automated game design system which has previously been built
as a single software block which designs games from start to finish. In this
paper we outline a roadmap for the development of a new version of ANGELINA,
designed to iterate on games in different ways to produce a continuous creative
process th... | computer science |
21,107 | Cost and Actual Causation | cs.AI | I propose the purpose our concept of actual causation serves is minimizing
various cost in intervention practice. Actual causation has three features:
nonredundant sufficiency, continuity and abnormality; these features correspond
to the minimization of exploitative cost, exploratory cost and risk cost in
intervention ... | computer science |
21,108 | A Labelling Framework for Probabilistic Argumentation | cs.AI | The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to ... | computer science |
21,109 | Using Program Induction to Interpret Transition System Dynamics | cs.AI | Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which ... | computer science |
21,110 | Hierarchical Subtask Discovery With Non-Negative Matrix Factorization | cs.AI | Hierarchical reinforcement learning methods offer a powerful means of
planning flexible behavior in complicated domains. However, learning an
appropriate hierarchical decomposition of a domain into subtasks remains a
substantial challenge. We present a novel algorithm for subtask discovery,
based on the recently introd... | computer science |
21,111 | Balancing Explicability and Explanation in Human-Aware Planning | cs.AI | Human aware planning requires an agent to be aware of the intentions,
capabilities and mental model of the human in the loop during its decision
process. This can involve generating plans that are explicable to a human
observer as well as the ability to provide explanations when such plans cannot
be generated. This has... | computer science |
21,112 | Detection of Abnormal Input-Output Associations | cs.AI | We study a novel outlier detection problem that aims to identify abnormal
input-output associations in data, whose instances consist of multi-dimensional
input (context) and output (responses) pairs. We present our approach that
works by analyzing data in the conditional (input--output) relation space,
captured by a de... | computer science |
21,113 | Thompson Sampling Guided Stochastic Searching on the Line for Deceptive
Environments with Applications to Root-Finding Problems | cs.AI | The multi-armed bandit problem forms the foundation for solving a wide range
of on-line stochastic optimization problems through a simple, yet effective
mechanism. One simply casts the problem as a gambler that repeatedly pulls one
out of N slot machine arms, eliciting random rewards. Learning of reward
probabilities i... | computer science |
21,114 | STARDATA: A StarCraft AI Research Dataset | cs.AI | We release a dataset of 65646 StarCraft replays that contains 1535 million
frames and 496 million player actions. We provide full game state data along
with the original replays that can be viewed in StarCraft. The game state data
was recorded every 3 frames which ensures suitability for a wide variety of
machine learn... | computer science |
21,115 | A Characterization of Monotone Influence Measures for Data
Classification | cs.AI | In this work we focus on the following question: how important was the i-th
feature in determining the outcome for a given datapoint? We identify a family
of influence measures; functions that, given a datapoint x, assign a value
phi_i(x) to every feature i, which roughly corresponds to that i's importance
in determini... | computer science |
21,116 | Investigating Reinforcement Learning Agents for Continuous State Space
Environments | cs.AI | Given an environment with continuous state spaces and discrete actions, we
investigate using a Double Deep Q-learning Reinforcement Agent to find optimal
policies using the LunarLander-v2 OpenAI gym environment. | computer science |
21,117 | Decoupled Learning of Environment Characteristics for Safe Exploration | cs.AI | Reinforcement learning is a proven technique for an agent to learn a task.
However, when learning a task using reinforcement learning, the agent cannot
distinguish the characteristics of the environment from those of the task. This
makes it harder to transfer skills between tasks in the same environment.
Furthermore, t... | computer science |
21,118 | Measuring Inconsistency in Argument Graphs | cs.AI | There have been a number of developments in measuring inconsistency in
logic-based representations of knowledge. In contrast, the development of
inconsistency measures for computational models of argument has been limited.
To address this shortcoming, this paper provides a general framework for
measuring inconsistency ... | computer science |
21,119 | Addendum to: Summary Information for Reasoning About Hierarchical Plans | cs.AI | Hierarchically structured agent plans are important for efficient planning
and acting, and they also serve (among other things) to produce "richer"
classical plans, composed not just of a sequence of primitive actions, but also
"abstract" ones representing the supplied hierarchies. A crucial step for this
and other app... | computer science |
21,120 | Tosca: Operationalizing Commitments Over Information Protocols | cs.AI | The notion of commitment is widely studied as a high-level abstraction for
modeling multiagent interaction. An important challenge is supporting flexible
decentralized enactments of commitment specifications. In this paper, we
combine recent advances on specifying commitments and information protocols.
Specifically, we... | computer science |
21,121 | Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs | cs.AI | Knowledge graphs and vector space models are robust knowledge representation
techniques with individual strengths and weaknesses. Vector space models excel
at determining similarity between concepts, but are severely constrained when
evaluating complex dependency relations and other logic-based operations that
are a st... | computer science |
21,122 | Understanding and Visualizing the District of Columbia Capital Bikeshare
System Using Data Analysis for Balancing Purposes | cs.AI | Bike sharing systems' popularity has consistently been rising during the past
years. Managing and maintaining these emerging systems are indispensable parts
of these systems. Visualizing the current operations can assist in getting a
better grasp on the performance of the system. In this paper, a data mining
approach i... | computer science |
21,123 | Benchmark Environments for Multitask Learning in Continuous Domains | cs.AI | As demand drives systems to generalize to various domains and problems, the
study of multitask, transfer and lifelong learning has become an increasingly
important pursuit. In discrete domains, performance on the Atari game suite has
emerged as the de facto benchmark for assessing multitask learning. However, in
contin... | computer science |
21,124 | New Ideas for Brain Modelling 4 | cs.AI | This paper continues the research that considers a new cognitive model based
strongly on the human brain. In particular, it considers the neural binding
structure of an earlier paper. It also describes some new methods in the areas
of image processing and behaviour simulation. The work is all based on earlier
research ... | computer science |
21,125 | Maximum A Posteriori Inference in Sum-Product Networks | cs.AI | Sum-product networks (SPNs) are a class of probabilistic graphical models
that allow tractable marginal inference. However, the maximum a posteriori
(MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from
both theoretical and algorithmic perspectives. For the theoretical part, we
reduce general MA... | computer science |
21,126 | TheoSea: Marching Theory to Light | cs.AI | There is sufficient information in the far-field of a radiating dipole
antenna to rediscover the Maxwell Equations and the wave equations of light,
including the speed of light $c.$ TheoSea is a Julia program that does this in
about a second, and the key insight is that the compactness of theories drives
the search. Th... | computer science |
21,127 | Visualizing and Exploring Dynamic High-Dimensional Datasets with
LION-tSNE | cs.AI | T-distributed stochastic neighbor embedding (tSNE) is a popular and
prize-winning approach for dimensionality reduction and visualizing
high-dimensional data. However, tSNE is non-parametric: once visualization is
built, tSNE is not designed to incorporate additional data into existing
representation. It highly limits ... | computer science |
21,128 | The Size of a Hyperball in a Conceptual Space | cs.AI | The cognitive framework of conceptual spaces [3] provides geometric means for
representing knowledge. A conceptual space is a high-dimensional space whose
dimensions are partitioned into so-called domains. Within each domain, the
Euclidean metric is used to compute distances. Distances in the overall space
are computed... | computer science |
21,129 | A Survey of Parallel A* | cs.AI | A* is a best-first search algorithm for finding optimal-cost paths in graphs.
A* benefits significantly from parallelism because in many applications, A* is
limited by memory usage, so distributed memory implementations of A* that use
all of the aggregate memory on the cluster enable problems that can not be
solved by ... | computer science |
21,130 | General AI Challenge - Round One: Gradual Learning | cs.AI | The General AI Challenge is an initiative to encourage the wider artificial
intelligence community to focus on important problems in building intelligent
machines with more general scope than is currently possible. The challenge
comprises of multiple rounds, with the first round focusing on gradual
learning, i.e. the a... | computer science |
21,131 | On Ensuring that Intelligent Machines Are Well-Behaved | cs.AI | Machine learning algorithms are everywhere, ranging from simple data analysis
and pattern recognition tools used across the sciences to complex systems that
achieve super-human performance on various tasks. Ensuring that they are
well-behaved---that they do not, for example, cause harm to humans or act in a
racist or s... | computer science |
21,132 | Exploring Directional Path-Consistency for Solving Constraint Networks | cs.AI | Among the local consistency techniques used for solving constraint networks,
path-consistency (PC) has received a great deal of attention. However,
enforcing PC is computationally expensive and sometimes even unnecessary.
Directional path-consistency (DPC) is a weaker notion of PC that considers a
given variable orderi... | computer science |
21,133 | Applying Deep Bidirectional LSTM and Mixture Density Network for
Basketball Trajectory Prediction | cs.AI | Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory ba... | computer science |
21,134 | Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning
Method | cs.AI | In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
which a number of cuboid-shaped items must be put into a bin one by one
orthogonally. The objective is to find a way to place these items that can
minimize the surface area of the bin. This problem is based on the fact that
there is no fixed-size... | computer science |
21,135 | Analysis of the Impact of Negative Sampling on Link Prediction in
Knowledge Graphs | cs.AI | Knowledge graphs are large, useful, but incomplete knowledge repositories.
They encode knowledge through entities and relations which define each other
through the connective structure of the graph. This has inspired methods for
the joint embedding of entities and relations in continuous low-dimensional
vector spaces, ... | computer science |
21,136 | A Survey of Human Activity Recognition Using WiFi CSI | cs.AI | In this article, we present a survey of recent advances in passive human
behaviour recognition in indoor areas using the channel state information (CSI)
of commercial WiFi systems. Movement of human body causes a change in the
wireless signal reflections, which results in variations in the CSI. By
analyzing the data st... | computer science |
21,137 | Learning Generalized Reactive Policies using Deep Neural Networks | cs.AI | We consider the problem of learning for planning, where knowledge acquired
while planning is reused to plan faster in new problem instances. For robotic
tasks, among others, plan execution can be captured as a sequence of visual
images. For such domains, we propose to use deep neural networks in learning
for planning, ... | computer science |
21,138 | Subspace Approximation for Approximate Nearest Neighbor Search in NLP | cs.AI | Most natural language processing tasks can be formulated as the approximated
nearest neighbor search problem, such as word analogy, document similarity,
machine translation. Take the question-answering task as an example, given a
question as the query, the goal is to search its nearest neighbor in the
training dataset ... | computer science |
21,139 | Accelerating Dependency Graph Learning from Heterogeneous Categorical
Event Streams via Knowledge Transfer | cs.AI | Dependency graph, as a heterogeneous graph representing the intrinsic
relationships between different pairs of system entities, is essential to many
data analysis applications, such as root cause diagnosis, intrusion detection,
etc. Given a well-trained dependency graph from a source domain and an immature
dependency g... | computer science |
21,140 | Deep Learning for Video Game Playing | cs.AI | In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlig... | computer science |
21,141 | Deep Style Match for Complementary Recommendation | cs.AI | Humans develop a common sense of style compatibility between items based on
their attributes. We seek to automatically answer questions like "Does this
shirt go well with that pair of jeans?" In order to answer these kinds of
questions, we attempt to model human sense of style compatibility in this
paper. The basic ass... | computer science |
21,142 | Plausibility and probability in deductive reasoning | cs.AI | We consider the problem of rational uncertainty about unproven mathematical
statements, which G\"odel and others have remarked on. Using Bayesian-inspired
arguments we build a normative model of fair bets under deductive uncertainty
which draws from both probability and the theory of algorithms. We comment on
connectio... | computer science |
21,143 | Disintegration and Bayesian Inversion, Both Abstractly and Concretely | cs.AI | The notions of disintegration and Bayesian inversion are fundamental in
conditional probability theory. They produce channels, as conditional
probabilities, from a joint state, or from an already given channel (in
opposite direction). These notions exist in the literature, in concrete
situations, but are presented here... | computer science |
21,144 | Difficulty-level Modeling of Ontology-based Factual Questions | cs.AI | Semantics based knowledge representations such as ontologies are found to be
very useful in automatically generating meaningful factual questions.
Determining the difficulty level of these system generated questions is helpful
to effectively utilize them in various educational and professional
applications. The existin... | computer science |
21,145 | A Computer Composes A Fabled Problem: Four Knights vs. Queen | cs.AI | We explain how the prototype automatic chess problem composer, Chesthetica,
successfully composed a rare and interesting chess problem using the new
Digital Synaptic Neural Substrate (DSNS) computational creativity approach.
This problem represents a greater challenge from a creative standpoint because
the checkmate is... | computer science |
21,146 | BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement
Learning | cs.AI | We introduce a novel method to train agents of reinforcement learning (RL) by
sharing knowledge in a way similar to the concept of using a book. The recorded
information in the form of a book is the main means by which humans learn
knowledge. Nevertheless, the conventional deep RL methods have mainly focused
either on ... | computer science |
21,147 | Active Exploration for Learning Symbolic Representations | cs.AI | We introduce an online active exploration algorithm for data-efficiently
learning an abstract symbolic model of an environment. Our algorithm is divided
into two parts: the first part quickly generates an intermediate Bayesian
symbolic model from the data that the agent has collected so far, which the
agent can then us... | computer science |
21,148 | Knowledge Transfer Between Artificial Intelligence Systems | cs.AI | We consider the fundamental question: how a legacy "student" Artificial
Intelligent (AI) system could learn from a legacy "teacher" AI system or a
human expert without complete re-training and, most importantly, without
requiring significant computational resources. Here "learning" is understood as
an ability of one sy... | computer science |
21,149 | Object-Oriented Knowledge Extraction using Universal Exploiters | cs.AI | This paper contains analysis and extension of exploiters-based knowledge
extraction methods, which allow generation of new knowledge, based on the basic
ones. The main achievement of the paper is useful features of some universal
exploiters proof, which allow extending set of basic classes and set of basic
relations by... | computer science |
21,150 | Computational Machines in a Coexistence with Concrete Universals and
Data Streams | cs.AI | We discuss that how the majority of traditional modeling approaches are
following the idealism point of view in scientific modeling, which follow the
set theoretical notions of models based on abstract universals. We show that
while successful in many classical modeling domains, there are fundamental
limits to the appl... | computer science |
21,151 | Combining Strategic Learning and Tactical Search in Real-Time Strategy
Games | cs.AI | A commonly used technique for managing AI complexity in real-time strategy
(RTS) games is to use action and/or state abstractions. High-level abstractions
can often lead to good strategic decision making, but tactical decision quality
may suffer due to lost details. A competing method is to sample the search
space whic... | computer science |
21,152 | Ultimate Intelligence Part III: Measures of Intelligence, Perception and
Intelligent Agents | cs.AI | We propose that operator induction serves as an adequate model of perception.
We explain how to reduce universal agent models to operator induction. We
propose a universal measure of operator induction fitness, and show how it can
be used in a reinforcement learning model and a homeostasis (self-preserving)
agent based... | computer science |
21,153 | Specious rules: an efficient and effective unifying method for removing
misleading and uninformative patterns in association rule mining | cs.AI | We present theoretical analysis and a suite of tests and procedures for
addressing a broad class of redundant and misleading association rules we call
\emph{specious rules}. Specious dependencies, also known as \emph{spurious},
\emph{apparent}, or \emph{illusory associations}, refer to a well-known
phenomenon where mar... | computer science |
21,154 | Explore, Exploit or Listen: Combining Human Feedback and Policy Model to
Speed up Deep Reinforcement Learning in 3D Worlds | cs.AI | We describe a method to use discrete human feedback to enhance the
performance of deep learning agents in virtual three-dimensional environments
by extending deep-reinforcement learning to model the confidence and
consistency of human feedback. This enables deep reinforcement learning
algorithms to determine the most a... | computer science |
21,155 | Probability Reversal and the Disjunction Effect in Reasoning Systems | cs.AI | Data based judgments go into artificial intelligence applications but they
undergo paradoxical reversal when seemingly unnecessary additional data is
provided. Examples of this are Simpson's reversal and the disjunction effect
where the beliefs about the data change once it is presented or aggregated
differently. Somet... | computer science |
21,156 | Conflict management in information fusion with belief functions | cs.AI | In Information fusion, the conflict is an important concept. Indeed,
combining several imperfect experts or sources allows conflict. In the theory
of belief functions, this notion has been discussed a lot. The mass appearing
on the empty set during the conjunctive combination rule is generally
considered as conflict, b... | computer science |
21,157 | A Comparison of Public Causal Search Packages on Linear, Gaussian Data
with No Latent Variables | cs.AI | We compare Tetrad (Java) algorithms to the other public software packages BNT
(Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of
recovering DAG structure to the extent possible from data generated recursively
from linear, Gaussian structure equation models (SEMs) with no latent
variables, for ... | computer science |
21,158 | Generating OWA weights using truncated distributions | cs.AI | Ordered weighted averaging (OWA) operators have been widely used in decision
making these past few years. An important issue facing the OWA operators' users
is the determination of the OWA weights. This paper introduces an OWA
determination method based on truncated distributions that enables intuitive
generation of OW... | computer science |
21,159 | Workflow Complexity for Collaborative Interactions: Where are the
Metrics? -- A Challenge | cs.AI | In this paper, we introduce the problem of denoting and deriving the
complexity of workflows (plans, schedules) in collaborative, planner-assisted
settings where humans and agents are trying to jointly solve a task. The
interactions -- and hence the workflows that connect the human and the agents
-- may differ accordin... | computer science |
21,160 | When Waiting is not an Option : Learning Options with a Deliberation
Cost | cs.AI | Recent work has shown that temporally extended actions (options) can be
learned fully end-to-end as opposed to being specified in advance. While the
problem of "how" to learn options is increasingly well understood, the question
of "what" good options should be has remained elusive. We formulate our answer
to what "goo... | computer science |
21,161 | Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement
Learning and Multi-task Reinforcement Learning | cs.AI | Reinforcement learning (RL), while often powerful, can suffer from slow
learning speeds, particularly in high dimensional spaces. The autonomous
decomposition of tasks and use of hierarchical methods hold the potential to
significantly speed up learning in such domains. This paper proposes a novel
practical method that... | computer science |
21,162 | Warmstarting of Model-based Algorithm Configuration | cs.AI | The performance of many hard combinatorial problem solvers depends strongly
on their parameter settings, and since manual parameter tuning is both tedious
and suboptimal the AI community has recently developed several algorithm
configuration (AC) methods to automatically address this problem. While all
existing AC meth... | computer science |
21,163 | KBLRN : End-to-End Learning of Knowledge Base Representations with
Latent, Relational, and Numerical Features | cs.AI | We present KBLRN, a framework for end-to-end learning of knowledge base
representations from latent, relational, and numerical features. KBLRN
integrates feature types with a novel combination of neural representation
learning and probabilistic product of experts models. To the best of our
knowledge, KBLRN is the first... | computer science |
21,164 | Perspectives for Evaluating Conversational AI | cs.AI | Conversational AI systems are becoming famous in day to day lives. In this
paper, we are trying to address the following key question: To identify whether
design, as well as development efforts for search oriented conversational AI
are successful or not.It is tricky to define 'success' in the case of
conversational AI ... | computer science |
21,165 | Motif-based Rule Discovery for Predicting Real-valued Time Series | cs.AI | Time series prediction is of great significance in many applications and has
attracted extensive attention from the data mining community. Existing work
suggests that for many problems, the shape in the current time series may
correlate an upcoming shape in the same or another series. Therefore, it is a
promising strat... | computer science |
21,166 | General problem solving with category theory | cs.AI | This paper proposes a formal cognitive framework for problem solving based on
category theory. We introduce cognitive categories, which are categories with
exactly one morphism between any two objects. Objects in these categories are
interpreted as states and morphisms as transformations between states.
Moreover, cogni... | computer science |
21,167 | Deep Reinforcement Learning for Conversational AI | cs.AI | Deep reinforcement learning is revolutionizing the artificial intelligence
field. Currently, it serves as a good starting point for constructing
intelligent autonomous systems which offer a better knowledge of the visual
world. It is possible to scale deep reinforcement learning with the use of deep
learning and do ama... | computer science |
21,168 | Reinforcement Learning Based Conversational Search Assistant | cs.AI | In this work, we develop an end-to-end Reinforcement Learning based
architecture for a conversational search agent to assist users in searching on
an e-commerce marketplace for digital assets. Our approach caters to a search
task fundamentally different from the ones which have limited search modalities
where the user ... | computer science |
21,169 | Memory Augmented Control Networks | cs.AI | Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to acces... | computer science |
21,170 | Relational Marginal Problems: Theory and Estimation | cs.AI | In the propositional setting, the marginal problem is to find a
(maximum-entropy) distribution that has some given marginals. We study this
problem in a relational setting and make the following contributions. First, we
compare two different notions of relational marginals. Second, we show a
duality between the resulti... | computer science |
21,171 | Towards Cognitive-and-Immersive Systems: Experiments in a Shared (or
common) Blockworld Framework | cs.AI | As computational power has continued to increase, and sensors have become
more accurate, the corresponding advent of systems that are
cognitive-and-immersive (CAI) has come to pass. CAI systems fall squarely into
the intersection of AI with HCI/HRI: such systems interact with and assist the
human agents that enter them... | computer science |
21,172 | Incorrigibility in the CIRL Framework | cs.AI | A value learning system has incentives to follow shutdown instructions,
assuming the shutdown instruction provides information (in the technical sense)
about which actions lead to valuable outcomes. However, this assumption is not
robust to model mis-specification (e.g., in the case of programmer errors). We
demonstrat... | computer science |
21,173 | Temporal Pattern Mining from Evolving Networks | cs.AI | Recently, evolving networks are becoming a suitable form to model many
real-world complex systems, due to their peculiarities to represent the systems
and their constituting entities, the interactions between the entities and the
time-variability of their structure and properties. Designing computational
models able to... | computer science |
21,174 | EMR-based medical knowledge representation and inference via Markov
random fields and distributed representation learning | cs.AI | Objective: Electronic medical records (EMRs) contain an amount of medical
knowledge which can be used for clinical decision support (CDS). Our objective
is a general system that can extract and represent these knowledge contained in
EMRs to support three CDS tasks: test recommendation, initial diagnosis, and
treatment ... | computer science |
21,175 | On Compiling DNNFs without Determinism | cs.AI | State-of-the-art knowledge compilers generate deterministic subsets of DNNF,
which have been recently shown to be exponentially less succinct than DNNF. In
this paper, we propose a new method to compile DNNFs without enforcing
determinism necessarily. Our approach is based on compiling deterministic DNNFs
with the addi... | computer science |
21,176 | Cost Adaptation for Robust Decentralized Swarm Behaviour | cs.AI | The multi-agent swarm system is a robust paradigm which can drive efficient
completion of complex tasks even under energy limitations and time constraints.
However, coordination of a swarm from a centralized command center can be
difficult, particularly as the swarm becomes large and spans wide ranges. Here,
we leverag... | computer science |
21,177 | Assumption-Based Approaches to Reasoning with Priorities | cs.AI | This paper maps out the relation between different approaches for handling
preferences in argumentation with strict rules and defeasible assumptions by
offering translations between them. The systems we compare are: non-prioritized
defeats i.e. attacks, preference-based defeats, and preference-based defeats
extended wi... | computer science |
21,178 | Robust Optimization of Unconstrained Binary Quadratic Problems | cs.AI | In this paper we focus on the unconstrained binary quadratic optimization
model, maximize x^t Qx, x binary, and consider the problem of identifying
optimal solutions that are robust with respect to perturbations in the Q
matrix.. We are motivated to find robust, or stable, solutions because of the
uncertainty inherent ... | computer science |
21,179 | EB-GLS: An Improved Guided Local Search Based on the Big Valley
Structure | cs.AI | Local search is a basic building block in memetic algorithms. Guided Local
Search (GLS) can improve the efficiency of local search. By changing the guide
function, GLS guides a local search to escape from locally optimal solutions
and find better solutions. The key component of GLS is its penalizing mechanism
which det... | computer science |
21,180 | Inverse Reinforcement Learning with Conditional Choice Probabilities | cs.AI | We make an important connection to existing results in econometrics to
describe an alternative formulation of inverse reinforcement learning (IRL). In
particular, we describe an algorithm using Conditional Choice Probabilities
(CCP), which are maximum likelihood estimates of the policy estimated from
expert demonstrati... | computer science |
21,181 | A Comprehensive Survey of Graph Embedding: Problems, Techniques and
Applications | cs.AI | Graph is an important data representation which appears in a wide diversity
of real-world scenarios. Effective graph analytics provides users a deeper
understanding of what is behind the data, and thus can benefit a lot of useful
applications such as node classification, node recommendation, link prediction,
etc. Howev... | computer science |
21,182 | Humanoid Robots as Agents of Human Consciousness Expansion | cs.AI | The "Loving AI" project involves developing software enabling humanoid robots
to interact with people in loving and compassionate ways, and to promote
people' self-understanding and self-transcendence. Currently the project
centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying
Sophia with personal... | computer science |
21,183 | When Traffic Flow Prediction Meets Wireless Big Data Analytics | cs.AI | Traffic flow prediction is an important research issue for solving the
traffic congestion problem in an Intelligent Transportation System (ITS).
Traffic congestion is one of the most serious problems in a city, which can be
predicted in advance by analyzing traffic flow patterns. Such prediction is
possible by analyzin... | computer science |
21,184 | Object-Oriented Knowledge Representation and Data Storage Using
Inhomogeneous Classes | cs.AI | This paper contains analysis of concept of a class within different
object-oriented knowledge representation models. The main attention is paid to
structure of the class and its efficiency in the context of data storage, using
object-relational mapping. The main achievement of the paper is extension of
concept of homog... | computer science |
21,185 | Prioritized Norms in Formal Argumentation | cs.AI | To resolve conflicts among norms, various nonmonotonic formalisms can be used
to perform prioritized normative reasoning. Meanwhile, formal argumentation
provides a way to represent nonmonotonic logics. In this paper, we propose a
representation of prioritized normative reasoning by argumentation. Using
hierarchical ab... | computer science |
21,186 | Can you fool AI with adversarial examples on a visual Turing test? | cs.AI | Deep learning has achieved impressive results in many areas of Computer
Vision and Natural Language Pro- cessing. Among others, Visual Question
Answering (VQA), also referred to a visual Turing test, is considered one of
the most compelling problems, and recent deep learning models have reported
significant progress in... | computer science |
21,187 | User and Developer Interaction with Editable and Readable Ontologies | cs.AI | The process of building ontologies is a difficult task that involves
collaboration between ontology developers and domain experts and requires an
ongoing interaction between them. This collaboration is made more difficult,
because they tend to use different tool sets, which can hamper this
interaction. In this paper, w... | computer science |
21,188 | Automatic Error Analysis of Human Motor Performance for Interactive
Coaching in Virtual Reality | cs.AI | In the context of fitness coaching or for rehabilitation purposes, the motor
actions of a human participant must be observed and analyzed for errors in
order to provide effective feedback. This task is normally carried out by human
coaches, and it needs to be solved automatically in technical applications that
are to p... | computer science |
21,189 | Scene learning, recognition and similarity detection in a fuzzy ontology
via human examples | cs.AI | This paper introduces a Fuzzy Logic framework for scene learning, recognition
and similarity detection, where scenes are taught via human examples. The
framework allows a robot to: (i) deal with the intrinsic vagueness associated
with determining spatial relations among objects; (ii) infer similarities and
dissimilarit... | computer science |
21,190 | DeepTransport: Learning Spatial-Temporal Dependency for Traffic
Condition Forecasting | cs.AI | Predicting traffic conditions has been recently explored as a way to relieve
traffic congestion. Several pioneering approaches have been proposed based on
traffic observations of the target location as well as its adjacent regions,
but they obtain somewhat limited accuracy due to lack of mining road topology.
To addres... | computer science |
21,191 | A Policy Search Method For Temporal Logic Specified Reinforcement
Learning Tasks | cs.AI | Reward engineering is an important aspect of reinforcement learning. Whether
or not the user's intentions can be correctly encapsulated in the reward
function can significantly impact the learning outcome. Current methods rely on
manually crafted reward functions that often require parameter tuning to obtain
the desire... | computer science |
21,192 | Heuristic Online Goal Recognition in Continuous Domains | cs.AI | Goal recognition is the problem of inferring the goal of an agent, based on
its observed actions. An inspiring approach - plan recognition by planning
(PRP) - uses off-the-shelf planners to dynamically generate plans for given
goals, eliminating the need for the traditional plan library. However, existing
PRP formulati... | computer science |
21,193 | Deep Learning Assisted Heuristic Tree Search for the Container
Pre-marshalling Problem | cs.AI | One of the key challenges for operations researchers solving real-world
problems is designing and implementing high-quality heuristics to guide their
search procedures. In the past, machine learning techniques have failed to play
a major role in operations research approaches, especially in terms of guiding
branching a... | computer science |
21,194 | Intelligence Quotient and Intelligence Grade of Artificial Intelligence | cs.AI | Although artificial intelligence is currently one of the most interesting
areas in scientific research, the potential threats posed by emerging AI
systems remain a source of persistent controversy. To address the issue of AI
threat, this study proposes a standard intelligence model that unifies AI and
human characteris... | computer science |
21,195 | Explainable Planning | cs.AI | As AI is increasingly being adopted into application solutions, the challenge
of supporting interaction with humans is becoming more apparent. Partly this is
to support integrated working styles, in which humans and intelligent systems
cooperate in problem-solving, but also it is a necessary step in the process of
buil... | computer science |
21,196 | What Automated Planning can do for Business Process Management | cs.AI | Business Process Management (BPM) is a central element of today
organizations. Despite over the years its main focus has been the support of
processes in highly controlled domains, nowadays many domains of interest to
the BPM community are characterized by ever-changing requirements,
unpredictable environments and incr... | computer science |
21,197 | Parameter Sharing Deep Deterministic Policy Gradient for Cooperative
Multi-agent Reinforcement Learning | cs.AI | Deep reinforcement learning for multi-agent cooperation and competition has
been a hot topic recently. This paper focuses on cooperative multi-agent
problem based on actor-critic methods under local observations settings. Multi
agent deep deterministic policy gradient obtained state of art results for some
multi-agent ... | computer science |
21,198 | Sensor Synthesis for POMDPs with Reachability Objectives | cs.AI | Partially observable Markov decision processes (POMDPs) are widely used in
probabilistic planning problems in which an agent interacts with an environment
using noisy and imprecise sensors. We study a setting in which the sensors are
only partially defined and the goal is to synthesize "weakest" additional
sensors, suc... | computer science |
21,199 | What Does Explainable AI Really Mean? A New Conceptualization of
Perspectives | cs.AI | We characterize three notions of explainable AI that cut across research
fields: opaque systems that offer no insight into its algo- rithmic mechanisms;
interpretable systems where users can mathemat- ically analyze its algorithmic
mechanisms; and comprehensible systems that emit symbols enabling user-driven
explanatio... | computer science |
21,200 | Indexing the Event Calculus with Kd-trees to Monitor Diabetes | cs.AI | Personal Health Systems (PHS) are mobile solutions tailored to monitoring
patients affected by chronic non communicable diseases. A patient affected by a
chronic disease can generate large amounts of events. Type 1 Diabetic patients
generate several glucose events per day, ranging from at least 6 events per day
(under ... | computer science |
21,201 | Automatic Taxonomy Generation - A Use-Case in the Legal Domain | cs.AI | A key challenge in the legal domain is the adaptation and representation of
the legal knowledge expressed through texts, in order for legal practitioners
and researchers to access this information easier and faster to help with
compliance related issues. One way to approach this goal is in the form of a
taxonomy of leg... | computer science |
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