Applications of Action Languages in Cognitive Robotics

Erdem, Esra; Patoğlu, Volkan

doi:10.1007/978-3-642-30743-0_16

Cited by 17 publications

(17 citation statements)

References 48 publications

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“…where p(o t |E, a t , b t ) and p(o t |¬E, a t , b t ) are given by (9). Note the dependence of this calculation on the sequence of actions a 1:t and the initial VS-POMDP belief b 0 .…”

Section: ) Expectation-based Approachmentioning

confidence: 97%

“…ASP, a nonmonotonic logic programming paradigm, is well suited for representing and reasoning with commonsense knowledge [1], [8]. It has been used in cognitive robotics [9], e.g., for reasoning by simulated robot housekeepers [10] and for representing domain knowledge learned through natural language processing [11]. However, ASP does not support quantitative models of uncertainty, whereas a lot of information available to robots is represented probabilistically so as to quantitatively model the uncertainty in sensing and acting.…”

Section: Related Workmentioning

confidence: 99%

“…where p(o t |E) and p(o t |¬E), shorthand for p(o t |E, a t , b t ) and p(o t |¬E, a t , b t ), respectively, are computed using (9), and b t is the result of state estimation in the VS-POMDP assuming that the object exists. The posterior can be used for early termination of the search for the target object if the probability of nonexistence of the target in the domain, p(¬E|o 1:t ), exceeds a threshold (τ − ), just as the existence of the object in a specific room or cell can be confirmed when the mode of the belief (b t ) exceeds a threshold (τ + ).…”

Section: Reasoning About Target Existencementioning

confidence: 99%

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Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

2015

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Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowledge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step toward addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Nonmonotonic logical inference in ASP is used to generate a multinomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains.

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“…where p(o t |E, a t , b t ) and p(o t |¬E, a t , b t ) are given by (9). Note the dependence of this calculation on the sequence of actions a 1:t and the initial VS-POMDP belief b 0 .…”

Section: ) Expectation-based Approachmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Reasoning About Target Existencementioning

confidence: 99%

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Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

2015

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“…gibi sorular sorabilir. Takımlar böyle sorulara SAT çözücüsü kullanarak cevap verebilir, çünkü görevleri robotlar ile bagdaştıran bilgi [13]'de oldugu gibi eylem modellerine ve sorulara yerleştirilebilir. Koordinasyon bulmaya yönelik ASP formülasyonu, ilgili kısıtlar eklenerek biraz degiştirilebilir.…”

Section: Sonuçunclassified

Finding optimal decoupled plans for multiple teams of robots in cognitive factories

Saribatur

Schüller

Patoğlu

et al. 2013

2013 21st Signal Processing and Communications Applications Conference (SIU)

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Özetçe -Bu makalede robotlardan oluşan birden fazla takım için, bir aracı yardımı ile, eniyileştirilmiş planları bulmak amacıyla geliştirdigimiz yöntemler açıklanmaktadır. Bu problemde, her takımın kendine ait çalışma alanında tamamlaması gereken görevleri vardır ve takımlar arası robot degişimi yapılabilmektedir. Ayrıca takımlarşu kısıtları göz önünde bulundurmaktadırlar: 1) takımlar (ve aracı) birbirlerinin çalışma alanı ya da görevleri hakkında bilgi sahibi degildirler (örn. gizlilik nedeniyle); 2) her takım ya robot ödünç verebilir ya da ödünç alabilir, ama ikisini birden yapamaz (örn. robotların çalışma alanları arasında taşınmaları ve ayarlanmaları genelde zor ve masraflıdır). Bu problemi çözmek için önerdigimiz yöntem, güncel SAT çözücülerini ve çözüm kümesi programlama (ASP) çözücülerini kullanmaktır. Yaklaşımımızın uygulanabilirligi ve kullanışlılıgı bilişsel fabrikalar üzerinde çeşitli senaryolarla yaptıgımız deneylerle gösterilmiştir.Abstract-We introduce a novel method to find optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots between each other, subject to the following constraints: 1) teams (and the mediator) do not know about each other's workspace or tasks (e.g., for privacy purposes); 2) every team can lend or borrow robots, but not both (e.g., transportation/calibration of robots between/for different workspaces is usually costly). We introduce a novel method to solve this problem using state-of-the-art SAT solvers and ASP solvers. We show applicability and usefulness of our approach by experiments on various scenarios of cognitive factories. I. GİRİŞGeleneksel imalat ve montaj sistemleri, özelleştirilebilir ve çok çeşitli ürünler için sürekli artan pazar talebine, uygun maliyetli ve kısa teslimat zamanlı cevap vermede yetersiz kalmaktadır. Bu nedenle, özelleştirilebilir ürünlerin otomatik üretimine yeni yaklaşımlar oluşturmak üretim sektöründe verimliligi arttırmak, rekabeti ve ekonomik sürdürebilirligi saglamak için çok önemlidir. Bilişsel Fabrika kavramı [1] [2] [3], geleneksel üretime alternatif olup, bir yandan otomasyondaki esnekligi, uyumlulugu ve verimlilik talebini dengelerken, bir yandan yüksek düzeyde güvenilirligi gerçekleştirerek, kayda deger faydalar vadeden yeni bir paradigmadır. Bilişsel fabrikalar, üretim sistemini yüksek seviyede akıl yürütme yetisi ile bilişsel robotik yaklaşımıyla geliştirmeyi amaçlar. Dolayısıyla, bu sistemler kendi eylemlerini planlama yetisine sahiptirler. Bilgiye dayalı planlama ve karar verme algoritmalarını kullanarak, bilişsel fabrikalardaki birden fazla robot takımı, ortak kaynakları verimli olarak kullanabilirler ve özelleştirilebilir ürünlerin üretimini teslim tarihlerine yetişmeyi garantileyecekşekilde saglayabilirler. Bilişsel fabrikalardaki en önemli zorluklardan biri, birden fazla robotun takımlar halinde çalışarak, bir üretim siparişini en yakın teslim tarihine yetiştirmesidir. Özelleştirilebilir ürünler için teslim...

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“…16 From each AS ∈ AS, extract p-state transition s i , a k , s j , and set the probability of T P a k [s i , s j ]. // Construct and set probabilities of observations 17 Construct ASP program Π(D LR (T ), s i , Disj(A P 2 )). 18 Compute answer sets AS of ASP program.…”

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confidence: 99%

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

Sridharan¹,

Gelfond²,

Zhang³

et al. 2019

jair

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This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and nondeterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains. 1 We use the terms "robot" and "agent" interchangeably in this paper. 1 arXiv:1508.03891v4 [cs.RO] 21 Sep 2018 probability reason optimally (or near optimally) about the effects of numerically quantifiable uncertainty in sensing and action. There have been many attempts to combine the benefits of these two classes of systems, including work on joint (i.e., logic-based and probabilistic) representations of state and action, and algorithms for planning and decisionmaking in such formalisms. These approaches provide significant expressive power, but they also impose a significant computational burden. More efficient (and often approximate) reasoning algorithms for such unified probabilisticlogical paradigms are being developed. However, practical robot systems that combine abstract task-level planning with probabilistic reasoning, link, rather than unify, their logic-based and probabilistic representations, primarily because roboticists often need to trade expressivity or correctness guarantees for computational speed. Information close to the sensorimotor level is often represented probabilistically to quantitatively model and reason about the uncertainty in sensing and actuation, with the robot's beliefs including statements such as "the robotics book is on the shelf with probability 0.9". At the same time, logic-based systems are used to reason with (more) abstract commonsen...

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Applications of Action Languages in Cognitive Robotics

Cited by 17 publications

References 48 publications

Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Finding optimal decoupled plans for multiple teams of robots in cognitive factories

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

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