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Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories [article]

Thomas Eiter, Thomas Lukasiewicz
2012 arXiv   pre-print
Using a concept of a history and its belief state, we then show how several important problems in reasoning about actions can be concisely formulated in our formalism.  ...  We present the language m Pcal C+ for probabilistic reasoning about actions, which is a generalization of the action language cal C+ that allows to deal with probabilistic as well as nondeterministic effects  ...  combine qualitative and quantitative uncertainty in a uni­ One way of adding uncertainty to reasoning about actions form framework for reasoning about actions: Even though is based on qualitative  ... 
arXiv:1212.2461v1 fatcat:4pfac5mojrgk7globa6z7ohsli

Reasoning about actions with sensing under qualitative and probabilistic uncertainty

Luca Iocchi, Thomas Lukasiewicz, Daniele Nardi, Riccardo Rosati
2009 ACM Transactions on Computational Logic  
We focus on the aspect of sensing in reasoning about actions under qualitative and probabilistic uncertainty.  ...  We then introduce the action language E+ for reasoning about actions with sensing under qualitative and probabilistic uncertainty, which is an extension of E by actions with nondeterministic and probabilistic  ...  in our framework for reasoning about actions under qualitative and probabilistic uncertainty.  ... 
doi:10.1145/1459010.1459015 fatcat:gp6jdlod7vbpflk4kit7qrusw4

Representations of uncertainty: where art thou?

Ádám Koblinger, József Fiser, Máté Lengyel
2021 Current Opinion in Behavioral Sciences  
Next, we critically review neural and behavioral evidence about the representation of uncertainty in the brain agreeing with fully Bayesian representations.  ...  Perception is often described as probabilistic inference requiring an internal representation of uncertainty.  ...  Moreover, just as representing uncertainty about the decision variable (and other variables, as we saw above) can allow efficient information gathering in active sensing, representing uncertainty about  ... 
doi:10.1016/j.cobeha.2021.03.009 pmid:34026948 pmcid:PMC8121756 fatcat:vrxne6udvvc7ji6lsv2apz5f4a

Some varieties of qualitative probability [chapter]

Michael P. Wellman
1995 Lecture Notes in Computer Science  
I discuss some of these in further depth, identify central issues, and suggest some general comparisons.  ...  In this essay I present a general characterization of qualitative probability, including a partial taxonomy of possible approaches.  ...  Acknowledgment This work was supported in part by grant F49620-94-1-0027 from the US Air Force Office of Scientific Research.  ... 
doi:10.1007/bfb0035948 fatcat:kwmscel3mzai3cfmugki3ehthy

Qualitative Probabilistic Networks for Planning Under Uncertainty [article]

Michael P. Wellman
2013 arXiv   pre-print
A qualitative reasoner based on an algebra over these assertions can derive further conclusions about the influence of actions.  ...  Bayesian networks provide a probabilistic semantics for qualitative assertions about likelihood.  ...  Essential characteristics of the influence of actions on the world can ohen be captured with qualitative asser tions that are much easier to specify than complete probabilistic models.  ... 
arXiv:1304.3115v1 fatcat:nnyl5oqtd5ch5b3psfbiu4jdle

Knowledge Processing for Cognitive Robots

Moritz Tenorth, Dominik Jain, Michael Beetz
2010 Künstliche Intelligenz  
When applied to robot control, such methods allow to write more general and flexible control programs and enable reasoning about the robot's observations, the actions involved in a task, action parameters  ...  and the reasons why an action was performed.  ...  Acknowledgments This work is supported in part within the DFG excellence initiative research cluster Cognition for Technical Systems (CoTeSys), see also  ... 
doi:10.1007/s13218-010-0044-0 fatcat:pur4jvsmfbbt7kznsqefqdxxiq

Logic, Probability and Action: A Situation Calculus Perspective [article]

Vaishak Belle
2020 arXiv   pre-print
While this progress has been notable, a general-purpose first-order knowledge representation language to reason about probabilities and dynamics, including in continuous settings, is still to emerge.  ...  In this paper, we survey recent results pertaining to the integration of logic, probability and actions in the situation calculus, which is arguably one of the oldest and most well-known formalisms.  ...  rich structure as well as natively reason about the probabilistic uncertainty plaguing a robot; a mathematical language that can reason with all available information, some of which may be probabilistic  ... 
arXiv:2006.09868v1 fatcat:ku4o4tief5ande2c5s3xxj3rrq

Multi-disciplinary approaches to reasoning with imperfect information and knowledge - a synthesis and a roadmap of challenges (Dagstuhl Seminar 15221)

Igor Douven, Gabriele Kern-Isberner, Markus Knauff, Henri Prade, Marc Herbstritt
2016 Dagstuhl Reports  
This multi-disciplinary seminar brought together researchers from computer science, philosophy and psychology dealing with topics of rational reasoning, reasoning with imperfect information and rational  ...  This report documents the program and the outcomes of Dagstuhl Seminar 15221 "Multi-disciplinary Approaches to Reasoning with Imperfect Information and Knowledge -a Synthesis and a Roadmap of Challenges  ...  , and based on whether they deal with these challenges in a qualitative or in a numerical way.  ... 
doi:10.4230/dagrep.5.5.92 dblp:journals/dagstuhl-reports/DouvenKKP15 fatcat:dymjupi6vbhrniwyjxmd3pr4wa

Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds

Vaishak Belle
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Logical AI is concerned with formal languages to represent and reason with qualitative specifications; statistical AI is concerned with learning quantitative specifications from data.  ...  systems that are explainable and interpretable.  ...  But this conflates with usage in practice where there is uncertainty about what things are in this world.  ... 
doi:10.24963/ijcai.2017/733 dblp:conf/ijcai/Belle17 fatcat:xrnwnlyhdzbkdhc23g76ysnaky

Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics [article]

Zenon Colaco, Mohan Sridharan
2015 arXiv   pre-print
The architecture described in this paper couples the non-monotonic logical reasoning capabilities of a declarative language with probabilistic belief revision, enabling robots to represent and reason with  ...  For any given task, each action in the plan contained in the answer set is executed probabilistically.  ...  This work was supported in part by the US Office of Naval Research Science of Autonomy award N00014-13-1-0766. All opinions and conclusions described in this paper are those of the authors.  ... 
arXiv:1508.00059v1 fatcat:3de7osfs4jeitgl77vwjlzodja

Decision theory in expert systems and artificial intelligence

Eric J. Horvitz, John S. Breese, Max Henrion
1988 International Journal of Approximate Reasoning  
We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expert-system paradigm, and survey some recent research at the crossroads of AI and decision  ...  The potential contributions of decision science for tackling AI problems derive from decision science's explicit theoretical framework and practical methodologies for reasoning about decisions under uncertainty  ...  ACKNOWLEDGMENTS We thank Greg Cooper, Renwick Curry, Lyn Dupre, Michael Fehling, Kenneth Fertig, Michael Genesereth, David Heckerman, Edward Herskovits, Harold Lehmann and Peter Szolovits for critiquing  ... 
doi:10.1016/0888-613x(88)90120-x fatcat:6gzvzf6rufckfcfmc3adec7leu

Background to Qualitative Decision Theory

Jon Doyle, Richmond H. Thomason
1999 The AI Magazine  
Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches.  ...  s This article provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects.  ...  Acknowledgments The preceding presentation reflects our debts to the participants of the 1997 AAAI Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, especially to Fahiem  ... 
doi:10.1609/aimag.v20i2.1456 dblp:journals/aim/DoyleT99 fatcat:yx2azbymyjd47lpbl32lks3x3q

Probabilistic Analysis of Manipulation Tasks: A Conceptual Framework

Randy C. Brost, Alan D. Christiansen
1996 The international journal of robotics research  
The resulting data support the probabilistic backprojection model and illustrate a task in which probabilistic analysis is required.  ...  This article addresses the problem of manipulation planning in the presence of uncertainty.  ...  Goldberg, Matt Mason, Russ Taylor, and Pat Xavier for many useful discussions during the development of these ideas.  ... 
doi:10.1177/027836499601500101 fatcat:peogjhul5jdl3exaudhst7qyry

Decision Analysis and Expert Systems

Max Henrion, John S. Breese, Eric Horvitz
1991 The AI Magazine  
MYCIN and PROSPECTOR originally intended their schemes as approximations to the probabilistic ideal, which they saw as unattainable . . .  ...  reasoning in qualitative terms.  ...  For example, Wellman (1988a) defines monotonic and synergistic influences between variables in qualitative probabilistic terms and presents methods of qualitative probabilistic reasoning based on them  ... 
doi:10.1609/aimag.v12i4.919 dblp:journals/aim/HenrionBH91 fatcat:ylql6qmbbbcgzhlbpf6kesvwlm

Forthcoming Papers

2005 Artificial Intelligence  
Oglietti, Understanding planning with incomplete information and sensing H. Fargier and R. Sabbadin, Qualitative decision under uncertainty: back to expected utility Y. Pencolé and M.-O.  ...  We present here an extensive evaluation of several algorithms for ensemble construction, including new proposals and comparing them with standard methods in the literature.  ...  Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs.  ... 
doi:10.1016/j.artint.2005.01.001 fatcat:leaob2jalbddpjizbpelscvpuy
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