scholarly journals Planning Graph Heuristics for Belief Space Search

2006 ◽  
Vol 26 ◽  
pp. 35-99 ◽  
Author(s):  
D. Bryce ◽  
S. Kambhampati ◽  
D. E. Smith
Keyword(s):  
State Of The Art ◽  
Relative Effectiveness ◽  
Belief State ◽  
Distance Measures ◽  
Aggregate State ◽  
Efficient Planning ◽  
Formal Basis ◽  
Planning Graph ◽  
Belief States ◽  
Conditional Planning

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning.

Epistemic Teleology

2018 ◽  
Author(s):  
Ralph Wedgwood
Keyword(s):  
Belief State ◽  
Epistemic Consequentialism ◽  
The Difference ◽  
Belief States ◽  
Epistemic Teleology

Wedgwood focuses his discussion around two evaluative concepts: correctness and rationality. Wedgwood proposes that these two concepts are related in the following way: one belief state is more rational than another if and only if the first has less expected inaccuracy than the former. He argues, however, that this view should not be understood as a form of consequentialism since it is not the total consequences of a belief state that determine its rationality. The view is rather a version of epistemic teleology. Wedgwood deploys this view to illuminate the difference between synchronic and diachronic evaluation of belief states as well as to disarm objections that have been leveled against epistemic consequentialism.

Epistemic and Doxastic Contents

2019 ◽  
pp. 213-238
Author(s):  
Francesco Berto ◽  
Mark Jago
Keyword(s):  
Bounded Rationality ◽  
Logical Consequence ◽  
External World ◽  
Belief State ◽  
Impossible Worlds ◽  
Formal Models ◽  
Closure Principle ◽  
Primary Focus ◽  
Belief States ◽  
Trivial Consequence

The case for making belief states the primary focus of our analysis and for including impossible worlds in that analysis is outlined in this chapter. This allows the reader to deny various closure principles, although this won’t help defeat worries about external-world scepticism. The issue that concerns the authors most is the problem of bounded rationality: belief states seem to be closed under ‘easy’ trivial consequence, but not under full logical consequence, and yet the former implies the latter. The solution presented here is that some trivial closure principle must fail on a given belief state, yet it is indeterminate just where this occurs. Formal models of belief states along these lines are given and it is shown that they respect the indeterminacy-of-closure intuition. Finally, the chapter discusses how we might square this approach with the fact that some people seem to believe contradictions.

scholarly journals Evaluating semantometrics from computer science publications

2020 ◽  
Vol 125 (3) ◽  
pp. 2915-2954
Author(s):  
Christin Katharina Kreutz ◽  
Premtim Sahitaj ◽  
Ralf Schenkel
Keyword(s):  
State Of The Art ◽  
Binary Classification ◽  
Citation Network ◽  
Distance Measures ◽  
Semantic Features ◽  
Citation Networks ◽  
Current State ◽  
Publication Time ◽  
Citation Practices ◽  
Vector Representations

AbstractIdentification of important works and assessment of importance of publications in vast scientific corpora are challenging yet common tasks subjected by many research projects. While the influence of citations in finding seminal papers has been analysed thoroughly, citation-based approaches come with several problems. Their impracticality when confronted with new publications which did not yet receive any citations, area-dependent citation practices and different reasons for citing are only a few drawbacks of them. Methods relying on more than citations, for example semantic features such as words or topics contained in publications of citation networks, are regarded with less vigour while providing promising preliminary results. In this work we tackle the issue of classifying publications with their respective referenced and citing papers as either seminal, survey or uninfluential by utilising semantometrics. We use distance measures over words, semantics, topics and publication years of papers in their citation network to engineer features on which we predict the class of a publication. We present the SUSdblp dataset consisting of 1980 labelled entries to provide a means of evaluating this approach. A classification accuracy of up to .9247 was achieved when combining multiple types of features using semantometrics. This is +.1232 compared to the current state of the art (SOTA) which uses binary classification to identify papers from classes seminal and survey. The utilisation of one-vector representations for the ternary classification task resulted in an accuracy of .949 which is +.1475 compared to the binary SOTA. Classification based on information available at publication time derived with semantometrics resulted in an accuracy of .8152 while an accuracy of .9323 could be achieved when using one-vector representations.

scholarly journals Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

2019 ◽  
Author(s):  
Maria Osmala ◽  
Harri Lähdesmäki
Keyword(s):  
Human Genome ◽  
Binding Sites ◽  
State Of The Art ◽  
Regulatory Protein ◽  
Distance Measures ◽  
Supervised Machine Learning ◽  
Cell Type ◽  
Enhancer Prediction ◽  
Art Methods ◽  
Chromatin Feature

AbstractBackgroundThe binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently.ResultsIn this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods.ConclusionPREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.Availabilityhttps://github.com/MariaOsmala/[email protected]

scholarly journals Observations on Darwiche and Pearl's Approach for Iterated Belief Revision

2019 ◽  
Author(s):  
Theofanis Aravanis ◽  
Pavlos Peppas ◽  
Mary-Anne Williams
Keyword(s):  
Belief Revision ◽  
Possible Worlds ◽  
Belief State ◽  
Strong Sense ◽  
Preference Ordering ◽  
Satisfactory Solution ◽  
Entire Class ◽  
Seminal Work ◽  
Iterated Belief Revision ◽  
Belief States

Notwithstanding the extensive work on iterated belief revision, there is, still, no fully satisfactory solution within the classical AGM paradigm. The seminal work of Darwiche and Pearl (DP approach, for short) remains the most dominant, despite its well-documented shortcomings. In this article, we make further observations on the DP approach. Firstly, we prove that the DP postulates are, in a strong sense, inconsistent with Parikh's relevance-sensitive axiom (P), extending previous initial conflicts. Immediate consequences of this result are that an entire class of intuitive revision operators, which includes Dalal's operator, violates the DP postulates, as well as that the Independence postulate and Spohn's conditionalization are inconsistent with (P). Lastly, we show that the DP postulates allow for more revision polices than the ones that can be captured by identifying belief states with total preorders over possible worlds, a fact implying that a preference ordering (over possible worlds) is an insufficient representation for a belief state.

scholarly journals EPOC: Efficient Perception via Optimal Communication

2020 ◽  
Vol 34 (04) ◽  
pp. 4107-4114 ◽  
Author(s):  
Masoumeh Heidari Kapourchali ◽  
Bonny Banerjee
Keyword(s):  
Recognition Accuracy ◽  
State Of The Art ◽  
The State ◽  
Belief State ◽  
Sensor Data ◽  
Communication Policy ◽  
Agent Model ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Communication Policies

We propose an agent model capable of actively and selectively communicating with other agents to predict its environmental state efficiently. Selecting whom to communicate with is a challenge when the internal model of other agents is unobservable. Our agent learns a communication policy as a mapping from its belief state to with whom to communicate in an online and unsupervised manner, without any reinforcement. Human activity recognition from multimodal, multisource and heterogeneous sensor data is used as a testbed to evaluate the proposed model where each sensor is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art even though our model uses significantly fewer parameters and infers the state in a localized manner. The learned policy reduces number of communications. The agent is tolerant to communication failures and can recognize unreliable agents through their communication messages. To the best of our knowledge, this is the first work on learning communication policies by an agent for predicting its environmental state.

scholarly journals Distribution Bisimilarity via the Power of Convex Algebras

2021 ◽  
Vol Volume 17, Issue 3 ◽  
Author(s):  
Filippo Bonchi ◽  
Alexandra Silva ◽  
Ana Sokolova
Keyword(s):  
Probability Distributions ◽  
Belief State ◽  
Proof Technique ◽  
Extended Version ◽  
Transition Systems ◽  
Probabilistic Automata ◽  
Abstract Approach ◽  
Belief States ◽  
Combine Probability ◽  
Do So

Probabilistic automata (PA), also known as probabilistic nondeterministic labelled transition systems, combine probability and nondeterminism. They can be given different semantics, like strong bisimilarity, convex bisimilarity, or (more recently) distribution bisimilarity. The latter is based on the view of PA as transformers of probability distributions, also called belief states, and promotes distributions to first-class citizens. We give a coalgebraic account of distribution bisimilarity, and explain the genesis of the belief-state transformer from a PA. To do so, we make explicit the convex algebraic structure present in PA and identify belief-state transformers as transition systems with state space that carries a convex algebra. As a consequence of our abstract approach, we can give a sound proof technique which we call bisimulation up-to convex hull. Comment: Full (extended) version of a CONCUR 2017 paper, minor revision of the LMCS submission

scholarly journals Variational Adversarial Kernel Learned Imitation Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 6599-6606
Author(s):  
Fan Yang ◽  
Alina Vereshchaka ◽  
Yufan Zhou ◽  
Changyou Chen ◽  
Wen Dong
Keyword(s):  
Cost Function ◽  
Distance Measure ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Distance Measures ◽  
Imitation Learning ◽  
Matching Problems ◽  
Distribution Matching ◽  
Informative Signals ◽  
The Cost

Imitation learning refers to the problem where an agent learns to perform a task through observing and mimicking expert demonstrations, without knowledge of the cost function. State-of-the-art imitation learning algorithms reduce imitation learning to distribution-matching problems by minimizing some distance measures. However, the distance measure may not always provide informative signals for a policy update. To this end, we propose the variational adversarial kernel learned imitation learning (VAKLIL), which measures the distance using the maximum mean discrepancy with variational kernel learning. Our method optimizes over a large cost-function space and is sample efficient and robust to overfitting. We demonstrate the performance of our algorithm through benchmarking with four state-of-the-art imitation learning algorithms over five high-dimensional control tasks, and a complex transportation control task. Experimental results indicate that our algorithm significantly outperforms related algorithms in all scenarios.

scholarly journals On Computational Aspects of Iterated Belief Change

2020 ◽  
Author(s):  
Nicolas Schwind ◽  
Sebastien Konieczny ◽  
Jean-Marie Lagniez ◽  
Pierre Marquis
Keyword(s):  
Belief Change ◽  
Special Kind ◽  
Belief State ◽  
Point Of View ◽  
Inference Problem ◽  
Complexity Bounds ◽  
Reasonable Time ◽  
The Family ◽  
Computational Aspects ◽  
Belief States

Iterated belief change aims to determine how the belief state of a rational agent evolves given a sequence of change formulae. Several families of iterated belief change operators (revision operators, improvement operators) have been pointed out so far, and characterized from an axiomatic point of view. This paper focuses on the inference problem for iterated belief change, when belief states are represented as a special kind of stratified belief bases. The computational complexity of the inference problem is identified and shown to be identical for all revision operators satisfying Darwiche and Pearl's (R*1-R*6) postulates. In addition, some complexity bounds for the inference problem are provided for the family of soft improvement operators. We also show that a revised belief state can be computed in a reasonable time for large-sized instances using SAT-based algorithms, and we report empirical results showing the feasibility of iterated belief change for bases of significant sizes.

scholarly journals Belief States in Criminal Law

2019 ◽  
Author(s):  
James A. Macleod
Keyword(s):  
Criminal Law ◽  
Empirical Work ◽  
Belief State ◽  
Theory And Practice ◽  
Mens Rea ◽  
Systematic Account ◽  
Legal Literature ◽  
Jury Instructions ◽  
Experimental Epistemology ◽  
Belief States

Belief-state ascription—determining what someone “knew,” “believed,” was “aware of,” etc.—is central to many areas of law. In criminal law, the distinction between knowledge and recklessness, and the use of broad jury instructions concerning other belief states, presupposes a common and stable understanding of what those belief-state terms mean. But a wealth of empirical work at the intersection of philosophy and psychology—falling under the banner of “Experimental Epistemology”—reveals how laypeople’s understandings of mens rea concepts differ systematically from what scholars, courts, and perhaps legislators, have assumed.As implemented, mens rea concepts are much more context-dependent and normatively evaluative than the conventional wisdom suggests, even assuming that jurors are following jury instructions to the letter. As a result, there is less difference between knowledge and recklessness than is typically assumed; jurors consistently “over”-ascribe knowledge to criminal defendants; and concepts like “belief,” “awareness,” and “conscious disregard” mean different things in different contexts, resulting in mens rea findings systematically responsive to aspects of the case traditionally considered irrelevant to the meaning of those terms.This Article provides the first systematic account of the factors driving jurors’ ascriptions of the specific belief states criminal law invokes. After surveying mens rea jury instructions, introducing the Experimental Epistemology literature to the legal literature on mens rea, and examining the implications of that literature for criminal law, this Article considers ways to begin bridging the surprisingly large gap between mens rea theory and practice.

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