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21,302 | The mind as a computational system | cs.AI | The present document is an excerpt of an essay that I wrote as part of my
application material to graduate school in Computer Science (with a focus on
Artificial Intelligence), in 1986. I was not invited by any of the schools that
received it, so I became a theoretical physicist instead. The essay's full
title was "Som... | computer science |
21,303 | Recognizing Plans by Learning Embeddings from Observed Action
Distributions | cs.AI | Recent advances in visual activity recognition have raised the possibility of
applications such as automated video surveillance. Effective approaches for
such problems however require the ability to recognize the plans of the agents
from video information. Although traditional plan recognition algorithms depend
on acce... | computer science |
21,304 | A Heuristic Search Algorithm Using the Stability of Learning Algorithms
in Certain Scenarios as the Fitness Function: An Artificial General
Intelligence Engineering Approach | cs.AI | This paper presents a non-manual design engineering method based on heuristic
search algorithm to search for candidate agents in the solution space which
formed by artificial intelligence agents modeled on the base of
bionics.Compared with the artificial design method represented by meta-learning
and the bionics method... | computer science |
21,305 | S-Shaped vs. V-Shaped Transfer Functions for Antlion Optimization
Algorithm in Feature Selection Problems | cs.AI | Feature selection is an important preprocessing step for classification
problems. It deals with selecting near optimal features in the original
dataset. Feature selection is an NP-hard problem, so meta-heuristics can be
more efficient than exact methods. In this work, Ant Lion Optimizer (ALO),
which is a recent metaheu... | computer science |
21,306 | Nintendo Super Smash Bros. Melee: An "Untouchable" Agent | cs.AI | Nintendo's Super Smash Bros. Melee fighting game can be emulated on modern
hardware allowing us to inspect internal memory states, such as character
positions. We created an AI that avoids being hit by training using these
internal memory states and outputting controller button presses. After training
on a month's wort... | computer science |
21,307 | Detecting Qualia in Natural and Artificial Agents | cs.AI | The Hard Problem of consciousness has been dismissed as an illusion. By
showing that computers are capable of experiencing, we show that they are at
least rudimentarily conscious with potential to eventually reach
superconsciousness. The main contribution of the paper is a test for confirming
certain subjective experie... | computer science |
21,308 | The Eigenoption-Critic Framework | cs.AI | Eigenoptions (EOs) have been recently introduced as a promising idea for
generating a diverse set of options through the graph Laplacian, having been
shown to allow efficient exploration. Despite its initial promising results, a
couple of issues in current algorithms limit its application, namely: (1) EO
methods requir... | computer science |
21,309 | Toward `verifying' a Water Treatment System | cs.AI | Modeling and verifying real-world cyber-physical systems are challenging,
especially so for complex systems where manually modeling is infeasible. In
this work, we report our experience on combining model learning and abstraction
refinement to analyze a challenging system, i.e., a real-world Secure Water
Treatment (SWa... | computer science |
21,310 | A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning
Agents | cs.AI | This paper proposes a low-cost, easily realizable strategy to equip a
reinforcement learning (RL) agent the capability of behaving ethically. Our
model allows the designers of RL agents to solely focus on the task to achieve,
without having to worry about the implementation of multiple trivial ethical
patterns to follo... | computer science |
21,311 | Contradiction-Centricity: A Uniform Model for Formation of Swarm
Intelligence and its Simulations | cs.AI | It is a grand challenge to model the emergence of swarm intelligence and many
principles or models had been proposed. However, existing models do not catch
the nature of swarm intelligence and they are not generic enough to describe
various types of emergence phenomena. In this work, we propose a
contradiction-centric ... | computer science |
21,312 | In folly ripe. In reason rotten. Putting machine theology to rest | cs.AI | Computation has changed the world more than any previous expressions of
knowledge. In its particular algorithmic embodiment, it offers a perspective,
within which the digital computer (one of many possible) exercises a role
reminiscent of theology. Since it is closed to meaning, algorithmic digital
computation can at m... | computer science |
21,313 | Simulated Autonomous Driving on Realistic Road Networks using Deep
Reinforcement Learning | cs.AI | Using Deep Reinforcement Learning (DRL) can be a promising approach to handle
tasks in the field of (simulated) autonomous driving, whereby recent
publications only consider learning in unusual driving environments. This paper
outlines a developed software, which instead can be used for evaluating DRL
algorithms based ... | computer science |
21,314 | Consideration on Example 2 of "An Algorithm of General Fuzzy
InferenceWith The Reductive Property" | cs.AI | In this paper, we will show that (1) the results about the fuzzy reasoning
algoritm obtained in the paper "Computer Sciences Vol. 34, No.4, pp.145-148,
2007" according to the paper "IEEE Transactions On systems, Man and
cybernetics, 18, pp.1049-1056, 1988" are correct; (2) example 2 in the paper
"An Algorithm of Genera... | computer science |
21,315 | Reasoning in Systems with Elements that Randomly Switch Characteristics | cs.AI | We examine the issue of stability of probability in reasoning about complex
systems with uncertainty in structure. Normally, propositions are viewed as
probability functions on an abstract random graph where it is implicitly
assumed that the nodes of the graph have stable properties. But what if some of
the nodes chang... | computer science |
21,316 | Intrinsic Point of Interest Discovery from Trajectory Data | cs.AI | This paper presents a framework for intrinsic point of interest discovery
from trajectory databases. Intrinsic points of interest are regions of a
geospatial area innately defined by the spatial and temporal aspects of
trajectory data, and can be of varying size, shape, and resolution. Any
trajectory database exhibits ... | computer science |
21,317 | Inverse Reinforce Learning with Nonparametric Behavior Clustering | cs.AI | Inverse Reinforcement Learning (IRL) is the task of learning a single reward
function given a Markov Decision Process (MDP) without defining the reward
function, and a set of demonstrations generated by humans/experts. However, in
practice, it may be unreasonable to assume that human behaviors can be
explained by one r... | computer science |
21,318 | Impossibility of deducing preferences and rationality from human policy | cs.AI | Inverse reinforcement learning (IRL) attempts to infer human rewards or
preferences from observed behavior. Since human planning systematically
deviates from rationality, several approaches have been tried to account for
specific human shortcomings. However, there has been little analysis of the
general problem of infe... | computer science |
21,319 | A Berkeley View of Systems Challenges for AI | cs.AI | With the increasing commoditization of computer vision, speech recognition
and machine translation systems and the widespread deployment of learning-based
back-end technologies such as digital advertising and intelligent
infrastructures, AI (Artificial Intelligence) has moved from research labs to
production. These cha... | computer science |
21,320 | Towards a Deep Reinforcement Learning Approach for Tower Line Wars | cs.AI | There have been numerous breakthroughs with reinforcement learning in the
recent years, perhaps most notably on Deep Reinforcement Learning successfully
playing and winning relatively advanced computer games. There is undoubtedly an
anticipation that Deep Reinforcement Learning will play a major role when the
first AI ... | computer science |
21,321 | 'Indifference' methods for managing agent rewards | cs.AI | `Indifference' refers to a class of methods that are used to control a reward
based agent. These methods of control work even if the implications of the
agent's reward are otherwise not fully understood. Though they all come out of
similar ideas, indifference techniques can be classified as way of achieving
one or more... | computer science |
21,322 | Three IQs of AI Systems and their Testing Methods | cs.AI | The rapid development of artificial intelligence has brought the artificial
intelligence threat theory as well as the problem about how to evaluate the
intelligence level of intelligent products. Both need to find a quantitative
method to evaluate the intelligence level of intelligence systems, including
human intellig... | computer science |
21,323 | Improving Exploration in Evolution Strategies for Deep Reinforcement
Learning via a Population of Novelty-Seeking Agents | cs.AI | Evolution strategies (ES) are a family of black-box optimization algorithms
able to train deep neural networks roughly as well as Q-learning and policy
gradient methods on challenging deep reinforcement learning (RL) problems, but
are much faster (e.g. hours vs. days) because they parallelize better. However,
many RL p... | computer science |
21,324 | Learning Representations from Road Network for End-to-End Urban Growth
Simulation | cs.AI | From our experiences in the past, we have seen that the growth of cities is
very much dependent on the transportation networks. In mega cities,
transportation networks determine to a significant extent as to where the
people will move and houses will be built. Hence, transportation network data
is crucial to an urban g... | computer science |
21,325 | Mining Smart Card Data for Travelers' Mini Activities | cs.AI | In the context of public transport modeling and simulation, we address the
problem of mismatch between simulated transit trips and observed ones. We point
to the weakness of the current travel demand modeling process; the trips it
generates are over-optimistic and do not reflect the real passenger choices. We
introduce... | computer science |
21,326 | Column Generation for Interaction Coverage in Combinatorial Software
Testing | cs.AI | This paper proposes a novel column generation framework for combinatorial
software testing. In particular, it combines Mathematical Programming and
Constraint Programming in a hybrid decomposition to generate covering arrays.
The approach allows generating parameterized test cases with coverage
guarantees between param... | computer science |
21,327 | Hierarchical and Interpretable Skill Acquisition in Multi-task
Reinforcement Learning | cs.AI | Learning policies for complex tasks that require multiple different skills is
a major challenge in reinforcement learning (RL). It is also a requirement for
its deployment in real-world scenarios. This paper proposes a novel framework
for efficient multi-task reinforcement learning. Our framework trains agents to
emplo... | computer science |
21,328 | Revisiting the Master-Slave Architecture in Multi-Agent Deep
Reinforcement Learning | cs.AI | Many tasks in artificial intelligence require the collaboration of multiple
agents. We exam deep reinforcement learning for multi-agent domains. Recent
research efforts often take the form of two seemingly conflicting perspectives,
the decentralized perspective, where each agent is supposed to have its own
controller; ... | computer science |
21,329 | Pseudorehearsal in actor-critic agents with neural network function
approximation | cs.AI | Catastrophic forgetting has a significant negative impact in reinforcement
learning. The purpose of this study is to investigate how pseudorehearsal can
change performance of an actor-critic agent with neural-network function
approximation. We tested agent in a pole balancing task and compared different
pseudorehearsal... | computer science |
21,330 | A Deep Policy Inference Q-Network for Multi-Agent Systems | cs.AI | We present DPIQN, a deep policy inference Q-network that targets multi-agent
systems composed of controllable agents, collaborators, and opponents that
interact with each other. We focus on one challenging issue in such
systems---modeling agents with varying strategies---and propose to employ
"policy features" learned ... | computer science |
21,331 | Federated Control with Hierarchical Multi-Agent Deep Reinforcement
Learning | cs.AI | We present a framework combining hierarchical and multi-agent deep
reinforcement learning approaches to solve coordination problems among a
multitude of agents using a semi-decentralized model. The framework extends the
multi-agent learning setup by introducing a meta-controller that guides the
communication between ag... | computer science |
21,332 | Intelligent Device Discovery in the Internet of Things - Enabling the
Robot Society | cs.AI | The Internet of Things (IoT) is continuously growing to connect billions of
smart devices anywhere and anytime in an Internet-like structure, which enables
a variety of applications, services and interactions between human and objects.
In the future, the smart devices are supposed to be able to autonomously
discover a ... | computer science |
21,333 | Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs
and Text Jointly Embedding | cs.AI | Minimizing adverse reactions caused by drug-drug interactions has always been
a momentous research topic in clinical pharmacology. Detecting all possible
interactions through clinical studies before a drug is released to the market
is a demanding task. The power of big data is opening up new approaches to
discover vari... | computer science |
21,334 | Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger | cs.AI | Recent developments have established the vulnerability of deep Reinforcement
Learning (RL) to policy manipulation attacks via adversarial perturbations. In
this paper, we investigate the robustness and resilience of deep RL to
training-time and test-time attacks. Through experimental results, we
demonstrate that under ... | computer science |
21,335 | Reinforcement Learning with Analogical Similarity to Guide Schema
Induction and Attention | cs.AI | Research in analogical reasoning suggests that higher-order cognitive
functions such as abstract reasoning, far transfer, and creativity are founded
on recognizing structural similarities among relational systems. Here we
integrate theories of analogy with the computational framework of reinforcement
learning (RL). We ... | computer science |
21,336 | RedDwarfData: a simplified dataset of StarCraft matches | cs.AI | The game Starcraft is one of the most interesting arenas to test new machine
learning and computational intelligence techniques; however, StarCraft matches
take a long time and creating a good dataset for training can be hard. Besides,
analyzing match logs to extract the main characteristics can also be done in
many di... | computer science |
21,337 | DeepMind Control Suite | cs.AI | The DeepMind Control Suite is a set of continuous control tasks with a
standardised structure and interpretable rewards, intended to serve as
performance benchmarks for reinforcement learning agents. The tasks are written
in Python and powered by the MuJoCo physics engine, making them easy to use and
modify. We include... | computer science |
21,338 | A total uncertainty measure for D numbers based on belief intervals | cs.AI | As a generalization of Dempster-Shafer theory, the theory of D numbers is a
new theoretical framework for uncertainty reasoning. Measuring the uncertainty
of knowledge or information represented by D numbers is an unsolved issue in
that theory. In this paper, inspired by distance based uncertainty measures for
Dempster... | computer science |
21,339 | ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, &
Snapshot Ensembling | cs.AI | ViZDoom is a robust, first-person shooter reinforcement learning environment,
characterized by a significant degree of latent state information. In this
paper, double-Q learning and prioritized experience replay methods are tested
under a certain ViZDoom combat scenario using a competitive deep recurrent
Q-network (DRQ... | computer science |
21,340 | Practical Challenges in Explicit Ethical Machine Reasoning | cs.AI | We examine implemented systems for ethical machine reasoning with a view to
identifying the practical challenges (as opposed to philosophical challenges)
posed by the area. We identify a need for complex ethical machine reasoning not
only to be multi-objective, proactive, and scrutable but that it must draw on
heteroge... | computer science |
21,341 | Intelligence Graph | cs.AI | In fact, there exist three genres of intelligence architectures: logics (e.g.
\textit{Random Forest, A$^*$ Searching}), neurons (e.g. \textit{CNN, LSTM}) and
probabilities (e.g. \textit{Naive Bayes, HMM}), all of which are incompatible
to each other. However, to construct powerful intelligence systems with various
meth... | computer science |
21,342 | Gatekeeping Algorithms with Human Ethical Bias: The ethics of algorithms
in archives, libraries and society | cs.AI | In the age of algorithms, I focus on the question of how to ensure algorithms
that will take over many of our familiar archival and library tasks, will
behave according to human ethical norms that have evolved over many years. I
start by characterizing physical archives in the context of related
institutions such as li... | computer science |
21,343 | Entropy production rate as a criterion for inconsistency in decision
theory | cs.AI | Evaluating pairwise comparisons breaks down complex decision problems into
tractable ones. Pairwise comparison matrices (PCMs) are regularly used to solve
multiple-criteria decision-making (MCDM) problems using Saaty's analytic
hierarchy process (AHP) framework. There are two significant drawbacks of using
PCMs. First,... | computer science |
21,344 | A Reliability Theory of Truth | cs.AI | Our approach is basically a coherence approach, but we avoid the well-known
pitfalls of coherence theories of truth. Consistency is replaced by
reliability, which expresses support and attack, and, in principle, every
theory (or agent, message) counts. At the same time, we do not require a
priviledged access to "realit... | computer science |
21,345 | A Greedy Search Tree Heuristic for Symbolic Regression | cs.AI | Symbolic Regression tries to find a mathematical expression that describes
the relationship of a set of explanatory variables to a measured variable. The
main objective is to find a model that minimizes the error and, optionally,
that also minimizes the expression size. A smaller expression can be seen as an
interpreta... | computer science |
21,346 | Distance formulas capable of unifying Euclidian space and probability
space | cs.AI | For pattern recognition like image recognition, it has become clear that each
machine-learning dictionary data actually became data in probability space
belonging to Euclidean space. However, the distances in the Euclidean space and
the distances in the probability space are separated and ununified when machine
learnin... | computer science |
21,347 | Multi-platform Version of StarCraft: Brood War in a Docker Container:
Technical Report | cs.AI | We present a dockerized version of a real-time strategy game StarCraft: Brood
War, commonly used as a domain for AI research, with a pre-installed collection
of AI developement tools supporting all the major types of StarCraft bots. This
provides a convenient way to deploy StarCraft AIs on numerous hosts at once and
ac... | computer science |
21,348 | Winograd Schema - Knowledge Extraction Using Narrative Chains | cs.AI | The Winograd Schema Challenge (WSC) is a test of machine intelligence,
designed to be an improvement on the Turing test. A Winograd Schema consists of
a sentence and a corresponding question. To successfully answer these
questions, one requires the use of commonsense knowledge and reasoning. This
work focuses on extrac... | computer science |
21,349 | A generalized concept-cognitive learning: A machine learning viewpoint | cs.AI | Concept-cognitive learning (CCL) is a hot topic in recent years, and it has
attracted much attention from the communities of formal concept analysis,
granular computing and cognitive computing. However, the relationship among
cognitive computing (CC), concept-cognitive computing (CCC) and CCL is not
clearly described. ... | computer science |
21,350 | Distributed Deep Reinforcement Learning: Learn how to play Atari games
in 21 minutes | cs.AI | We present a study in Distributed Deep Reinforcement Learning (DDRL) focused
on scalability of a state-of-the-art Deep Reinforcement Learning algorithm
known as Batch Asynchronous Advantage ActorCritic (BA3C). We show that using
the Adam optimization algorithm with a batch size of up to 2048 is a viable
choice for carr... | computer science |
21,351 | Probabilistic Prognostic Estimates of Survival in Metastatic Cancer
Patients (PPES-Met) Utilizing Free-Text Clinical Narratives | cs.AI | We propose a deep learning model - Probabilistic Prognostic Estimates of
Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term
life expectancy (3 months) of the patients by analyzing free-text clinical
notes in the electronic medical record, while maintaining the temporal visit
sequence. In a sing... | computer science |
21,352 | Deep In-GPU Experience Replay | cs.AI | Experience replay allows a reinforcement learning agent to train on samples
from a large amount of the most recent experiences. A simple in-RAM experience
replay stores these most recent experiences in a list in RAM, and then copies
sampled batches to the GPU for training. I moved this list to the GPU, thus
creating an... | computer science |
21,353 | A Formalization of Kant's Second Formulation of the Categorical
Imperative | cs.AI | We present a formalization and computational implementation of the second
formulation of Kant's categorical imperative. This ethical principle requires
an agent to never treat someone merely as a means but always also as an end.
Here we interpret this principle in terms of how persons are causally affected
by actions. ... | computer science |
21,354 | Greenhouse: A Zero-Positive Machine Learning System for Time-Series
Anomaly Detection | cs.AI | This short paper describes our ongoing research on Greenhouse - a
zero-positive machine learning system for time-series anomaly detection. | computer science |
21,355 | Precision and Recall for Range-Based Anomaly Detection | cs.AI | Classical anomaly detection is principally concerned with point-based
anomalies, anomalies that occur at a single data point. In this paper, we
present a new mathematical model to express range-based anomalies, anomalies
that occur over a range (or period) of time. | computer science |
21,356 | Reasoning about Unforeseen Possibilities During Policy Learning | cs.AI | Methods for learning optimal policies in autonomous agents often assume that
the way the domain is conceptualised---its possible states and actions and
their causal structure---is known in advance and does not change during
learning. This is an unrealistic assumption in many scenarios, because new
evidence can reveal i... | computer science |
21,357 | Planning with Pixels in (Almost) Real Time | cs.AI | Recently, width-based planning methods have been shown to yield
state-of-the-art results in the Atari 2600 video games. For this, the states
were associated with the (RAM) memory states of the simulator. In this work, we
consider the same planning problem but using the screen instead. By using the
same visual inputs, t... | computer science |
21,358 | Axiomatizations of inconsistency indices for triads | cs.AI | Pairwise comparison matrices often exhibit inconsistency, therefore, a number
of indices has been introduced to measure their deviation from a consistent
matrix. Since inconsistency first emerges in the case of three alternatives,
several inconsistency indices are based on triads. Recently, a set of axioms
has been pro... | computer science |
21,359 | Neural Program Synthesis with Priority Queue Training | cs.AI | We consider the task of program synthesis in the presence of a reward
function over the output of programs, where the goal is to find programs with
maximal rewards. We employ an iterative optimization scheme, where we train an
RNN on a dataset of K best programs from a priority queue of the generated
programs so far. T... | computer science |
21,360 | Counterfactual equivalence for POMDPs, and underlying deterministic
environments | cs.AI | Partially Observable Markov Decision Processes (POMDPs) are rich environments
often used in machine learning. But the issue of information and causal
structures in POMDPs has been relatively little studied. This paper presents
the concepts of equivalent and counterfactually equivalent POMDPs, where agents
cannot distin... | computer science |
21,361 | Formalized Conceptual Spaces with a Geometric Representation of
Correlations | cs.AI | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
similarity space and concepts are represented by convex regions in this space.
After pointing out a problem with the convexity requirement, we propose a
formalization of con... | computer science |
21,362 | Model-Based Action Exploration | cs.AI | Deep reinforcement learning has great stride in solving challenging motion
control tasks.
Recently there has been a significant amount of work on methods to exploit
the data gathered during training, but less work is done on good methods for
generating data to learn from.
For continuous actions domains, the typical... | computer science |
21,363 | Multilayered Model of Speech | cs.AI | Human speech is the most important part of General Artificial Intelligence
and subject of much research. The hypothesis proposed in this article provides
explanation of difficulties that modern science tackles in the field of human
brain simulation. The hypothesis is based on the author's conviction that the
brain of a... | computer science |
21,364 | Engineering Cooperative Smart Things based on Embodied Cognition | cs.AI | The goal of the Internet of Things (IoT) is to transform any thing around us,
such as a trash can or a street light, into a smart thing. A smart thing has
the ability of sensing, processing, communicating and/or actuating. In order to
achieve the goal of a smart IoT application, such as minimizing waste
transportation ... | computer science |
21,365 | A Computational Model of Commonsense Moral Decision Making | cs.AI | We introduce a new computational model of moral decision making, drawing on a
recent theory of commonsense moral learning via social dynamics. Our model
describes moral dilemmas as a utility function that computes trade-offs in
values over abstract moral dimensions, which provide interpretable parameter
values when imp... | computer science |
21,366 | Top k Memory Candidates in Memory Networks for Common Sense Reasoning | cs.AI | Successful completion of reasoning task requires the agent to have relevant
prior knowledge or some given context of the world dynamics. Usually, the
information provided to the system for a reasoning task is just the query or
some supporting story, which is often not enough for common reasoning tasks.
The goal here is... | computer science |
21,367 | The Role of Conditional Independence in the Evolution of Intelligent
Systems | cs.AI | Systems are typically made from simple components regardless of their
complexity. While the function of each part is easily understood, higher order
functions are emergent properties and are notoriously difficult to explain. In
networked systems, both digital and biological, each component receives inputs,
performs a s... | computer science |
21,368 | A formal framework for deliberated judgment | cs.AI | While the philosophical literature has extensively studied how decisions
relate to arguments, reasons and justifications, decision theory almost
entirely ignores the latter notions and rather focuses on preference and
belief. In this article, we argue that decision theory can largely benefit from
explicitly taking into... | computer science |
21,369 | Innateness, AlphaZero, and Artificial Intelligence | cs.AI | The concept of innateness is rarely discussed in the context of artificial
intelligence. When it is discussed, or hinted at, it is often the context of
trying to reduce the amount of innate machinery in a given system. In this
paper, I consider as a test case a recent series of papers by Silver et al
(Silver et al., 20... | computer science |
21,370 | Learning model-based strategies in simple environments with hierarchical
q-networks | cs.AI | Recent advances in deep learning have allowed artificial agents to rival
human-level performance on a wide range of complex tasks; however, the ability
of these networks to learn generalizable strategies remains a pressing
challenge. This critical limitation is due in part to two factors: the opaque
information represe... | computer science |
21,371 | Reasoning about multiple aspects in DLs: Semantics and Closure
Construction | cs.AI | Starting from the observation that rational closure has the undesirable
property of being an "all or nothing" mechanism, we here propose a
multipreferential semantics, which enriches the preferential semantics
underlying rational closure in order to separately deal with the inheritance of
different properties in an ont... | computer science |
21,372 | CHALET: Cornell House Agent Learning Environment | cs.AI | We present CHALET, a 3D house simulator with support for navigation and
manipulation. CHALET includes 58 rooms and 10 house configuration, and allows
to easily create new house and room layouts. CHALET supports a range of common
household activities, including moving objects, toggling appliances, and
placing objects in... | computer science |
21,373 | Comparison Training for Computer Chinese Chess | cs.AI | This paper describes the application of comparison training (CT) for
automatic feature weight tuning, with the final objective of improving the
evaluation functions used in Chinese chess programs. First, we propose an
n-tuple network to extract features, since n-tuple networks require very little
expert knowledge throu... | computer science |
21,374 | Curiosity-driven reinforcement learning with homeostatic regulation | cs.AI | We propose a curiosity reward based on information theory principles and
consistent with the animal instinct to maintain certain critical parameters
within a bounded range. Our experimental validation shows the added value of
the additional homeostatic drive to enhance the overall information gain of a
reinforcement le... | computer science |
21,375 | Development and application of a machine learning supported methodology
for measurement and verification (M&V) 2.0 | cs.AI | The foundations of all methodologies for the measurement and verification
(M&V) of energy savings are based on the same five key principles: accuracy,
completeness, conservatism, consistency and transparency. The most widely
accepted methodologies tend to generalise M&V so as to ensure applicability
across the spectrum... | computer science |
21,376 | Directly Estimating the Variance of the λ-Return Using
Temporal-Difference Methods | cs.AI | This paper investigates estimating the variance of a temporal-difference
learning agent's update target. Most reinforcement learning methods use an
estimate of the value function, which captures how good it is for the agent to
be in a particular state and is mathematically expressed as the expected sum of
discounted fu... | computer science |
21,377 | Discovering Markov Blanket from Multiple interventional Datasets | cs.AI | In this paper, we study the problem of discovering the Markov blanket (MB) of
a target variable from multiple interventional datasets. Datasets attained from
interventional experiments contain richer causal information than passively
observed data (observational data) for MB discovery. However, almost all
existing MB d... | computer science |
21,378 | Probabilistic Planning by Probabilistic Programming | cs.AI | Automated planning is a major topic of research in artificial intelligence,
and enjoys a long and distinguished history. The classical paradigm assumes a
distinguished initial state, comprised of a set of facts, and is defined over a
set of actions which change that state in one way or another. Planning in many
real-wo... | computer science |
21,379 | Finding ReMO (Related Memory Object): A Simple Neural Architecture for
Text based Reasoning | cs.AI | To solve the text-based question and answering task that requires relational
reasoning, it is necessary to memorize a large amount of information and find
out the question relevant information from the memory. Most approaches were
based on external memory and four components proposed by Memory Network. The
distinctive ... | computer science |
21,380 | Knowledge Graph Embedding with Multiple Relation Projections | cs.AI | Knowledge graphs contain rich relational structures of the world, and thus
complement data-driven machine learning in heterogeneous data. One of the most
effective methods in representing knowledge graphs is to embed symbolic
relations and entities into continuous spaces, where relations are
approximately linear transl... | computer science |
21,381 | Ontology-based Fuzzy Markup Language Agent for Student and Robot
Co-Learning | cs.AI | An intelligent robot agent based on domain ontology, machine learning
mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning
is presented in this paper. The machine-human co-learning model is established
to help various students learn the mathematical concepts based on their
learning ability and ... | computer science |
21,382 | Safe Exploration in Continuous Action Spaces | cs.AI | We address the problem of deploying a reinforcement learning (RL) agent on a
physical system such as a datacenter cooling unit or robot, where critical
constraints must never be violated. We show how to exploit the typically smooth
dynamics of these systems and enable RL algorithms to never violate constraints
during l... | computer science |
21,383 | SWRL2SPIN: A tool for transforming SWRL rule bases in OWL ontologies to
object-oriented SPIN rules | cs.AI | SWRL is a semantic web rule language that combines OWL ontologies with Horn
Logic rules of the RuleML family of rule languages, extending the set of OWL
axioms to include Horn-like rules. Being supported by the Prot\'eg\'e ontology
editor as well as by popular rule engines and ontology reasoners, such as Jess,
Drools a... | computer science |
21,384 | A Cyber Science Based Ontology for Artificial General Intelligence
Containment | cs.AI | The development of artificial general intelligence is considered by many to
be inevitable. What such intelligence does after becoming aware is not so
certain. To that end, research suggests that the likelihood of artificial
general intelligence becoming hostile to humans is significant enough to
warrant inquiry into me... | computer science |
21,385 | Algorithms for the Greater Good! On Mental Modeling and Acceptable
Symbiosis in Human-AI Collaboration | cs.AI | Effective collaboration between humans and AI-based systems requires
effective modeling of the human in the loop, both in terms of the mental state
as well as the physical capabilities of the latter. However, these models can
also open up pathways for manipulating and exploiting the human in the hopes of
achieving some... | computer science |
21,386 | Features, Projections, and Representation Change for Generalized
Planning | cs.AI | Generalized planning is concerned with the characterization and computation
of plans that solve many instances at once. In the standard formulation, a
generalized plan is a mapping from feature or observation histories into
actions, assuming that the instances share a common pool of features and
actions. This assumptio... | computer science |
21,387 | An Incremental Off-policy Search in a Model-free Markov Decision Process
Using a Single Sample Path | cs.AI | In this paper, we consider a modified version of the control problem in a
model free Markov decision process (MDP) setting with large state and action
spaces. The control problem most commonly addressed in the contemporary
literature is to find an optimal policy which maximizes the value function,
i.e., the long run di... | computer science |
21,388 | Deep Learning Works in Practice. But Does it Work in Theory? | cs.AI | Deep learning relies on a very specific kind of neural networks: those
superposing several neural layers. In the last few years, deep learning
achieved major breakthroughs in many tasks such as image analysis, speech
recognition, natural language processing, and so on. Yet, there is no
theoretical explanation of this s... | computer science |
21,389 | Lifted Filtering via Exchangeable Decomposition | cs.AI | We present a model for recursive Bayesian filtering based on lifted multiset
states. Combining multisets with lifting makes it possible to simultaneously
exploit multiple strategies for reducing inference complexity when compared to
list-based grounded state representations. The core idea is to borrow the
concept of Ma... | computer science |
21,390 | A family of OWA operators based on Faulhaber's formulas | cs.AI | In this paper we develop a new family of Ordered Weighted Averaging (OWA)
operators. Weight vector is obtained from a desired orness of the operator.
Using Faulhaber's formulas we obtain direct and simple expressions for the
weight vector without any iteration loop. With the exception of one weight, the
remaining follo... | computer science |
21,391 | Deceptive Games | cs.AI | Deceptive games are games where the reward structure or other aspects of the
game are designed to lead the agent away from a globally optimal policy. While
many games are already deceptive to some extent, we designed a series of games
in the Video Game Description Language (VGDL) implementing specific types of
deceptio... | computer science |
21,392 | Recursive Feature Generation for Knowledge-based Learning | cs.AI | When humans perform inductive learning, they often enhance the process with
background knowledge. With the increasing availability of well-formed
collaborative knowledge bases, the performance of learning algorithms could be
significantly enhanced if a way were found to exploit these knowledge bases. In
this work, we p... | computer science |
21,393 | A Semantic Model for Historical Manuscripts | cs.AI | The study and publication of historical scientific manuscripts are com- plex
tasks that involve, among others, the explicit representation of the text mean-
ings and reasoning on temporal entities. In this paper we present the first
results of an interdisciplinary project dedicated to the study of Saussure's
manuscript... | computer science |
21,394 | Crowd Flow Prediction by Deep Spatio-Temporal Transfer Learning | cs.AI | Crowd flow prediction is a fundamental urban computing problem. Recently,
deep learning has been successfully applied to solve this problem, but it
relies on rich historical data. In reality, many cities may suffer from data
scarcity issue when their targeted service or infrastructure is new. To
overcome this issue, th... | computer science |
21,395 | How do Humans Understand Explanations from Machine Learning Systems? An
Evaluation of the Human-Interpretability of Explanation | cs.AI | Recent years have seen a boom in interest in machine learning systems that
can provide a human-understandable rationale for their predictions or
decisions. However, exactly what kinds of explanation are truly
human-interpretable remains poorly understood. This work advances our
understanding of what makes explanations ... | computer science |
21,396 | Modelling contextuality by probabilistic programs with hypergraph
semantics | cs.AI | Models of a phenomenon are often developed by examining it under different
experimental conditions, or measurement contexts. The resultant probabilistic
models assume that the underlying random variables, which define a measurable
set of outcomes, can be defined independent of the measurement context. The
phenomenon is... | computer science |
21,397 | Plan Explanations as Model Reconciliation -- An Empirical Study | cs.AI | Recent work in explanation generation for decision making agents has looked
at how unexplained behavior of autonomous systems can be understood in terms of
differences in the model of the system and the human's understanding of the
same, and how the explanation process as a result of this mismatch can be then
seen as a... | computer science |
21,398 | Tunneling Neural Perception and Logic Reasoning through Abductive
Learning | cs.AI | Perception and reasoning are basic human abilities that are seamlessly
connected as part of human intelligence. However, in current machine learning
systems, the perception and reasoning modules are incompatible. Tasks requiring
joint perception and reasoning ability are difficult to accomplish autonomously
and still d... | computer science |
21,399 | Coordinated Exploration in Concurrent Reinforcement Learning | cs.AI | We consider a team of reinforcement learning agents that concurrently learn
to operate in a common environment. We identify three properties - adaptivity,
commitment, and diversity - which are necessary for efficient coordinated
exploration and demonstrate that straightforward extensions to single-agent
optimistic and ... | computer science |
21,400 | The Sea Exploration Problem: Data-driven Orienteering on a Continuous
Surface | cs.AI | This paper describes a problem arising in sea exploration, where the aim is
to schedule the expedition of a ship for collecting information about the
resources on the seafloor. The aim is to collect data by probing on a set of
carefully chosen locations, so that the information available is optimally
enriched. This pro... | computer science |
21,401 | Guided Policy Exploration for Markov Decision Processes using an
Uncertainty-Based Value-of-Information Criterion | cs.AI | Reinforcement learning in environments with many action-state pairs is
challenging. At issue is the number of episodes needed to thoroughly search the
policy space. Most conventional heuristics address this search problem in a
stochastic manner. This can leave large portions of the policy space unvisited
during the ear... | computer science |
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