State-Based Regression with Sensing and Knowledge

Scherl, Richard B.; Tran, Cao Son; Baral, Chitta

doi:10.1007/978-3-540-89197-0_33

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Nevertheless, one criticism levelled at this line of work, and indeed at much of the work in cognitive robotics and reasoning about actions, is that the theory is far removed from the kind of probabilistic uncertainty and noise seen in typical robotic applications [56] and machine learning [46]. In fact, for many years, this criticism applied broadly to almost every knowledge representation language for reasoning about actions including, dynamic epistemic logic [58], the fluent calculus [55], among others [5,53].…”

Section: The Story Does Not Get Easier With Actionsmentioning

confidence: 99%

Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond

Belle

2023

Lecture Notes in Computer Science

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The unification of the first-order logic and probability has been seen as a long-standing concern in philosophy, AI and mathematics. In this talk, I will briefly review our recent results on revisiting that unification. Although there are plenty of approaches in communities such as statistical relational learning, automated planning, and neuro-symbolic AI that leverage and develop languages with logical and probabilistic aspects, they almost always restrict the representation as well as the semantic framework in various ways that does not fully explain how to combine first-order logic and probability theory in a general way. In many cases, this restriction is justified because it may be necessary to focus on practicality and efficiency. However, the search for a restriction-free mathematical theory remains ongoing. In this article, we discuss our recent results regarding the development of languages that support arbitrary quantification, possibly infinitely many random variables, both discrete and continuous distributions, as well as programming languages built on top of such features to include recursion and branching control.

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Section: The Story Does Not Get Easier With Actionsmentioning

confidence: 99%

Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond

Belle

2023

Lecture Notes in Computer Science

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“…Thus, our description of operators will not be in terms of their effect on the state of the external world but in terms of their effect on the fluents that characterize the robot's belief. Our work is informed by related work in partially observed or probabilistic regression (back-chaining) planning [2,5,18]. In general, it will be very difficult to characterize the exact pre-image of an operation in belief space; we will strive to provide an approximation that supports the construction of reasonable plans and rely on execution monitoring and replanning to handle errors due to approximation.…”

Section: Symbolic Representation Of Goals and Subgoalsmentioning

confidence: 99%

Pre-image Backchaining in Belief Space for Mobile Manipulation

Kaelbling

Lozano-Pérez

2016

Springer Tracts in Advanced Robotics

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“…Thus, our description of operators will not be in terms of their effect on the state of the external world but in terms of their effect on the fluents that characterize the robot's belief. Our work is informed by related work in partially observed or probabilistic regression (back-chaining) planning [20], [21], [22]. In general, it will be very difficult to characterize the exact pre-image of an operation in belief space; we will strive to provide an approximation that supports the construction of reasonable plans and relies on execution monitoring and replanning to handle errors due to approximation.…”

Section: Belief Set Estimation For Planningmentioning

confidence: 99%

Unifying perception, estimation and action for mobile manipulation via belief space planning

Kaelbling

Lozano-Pérez

2012

2012 IEEE International Conference on Robotics and Automation

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In this paper, we describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains. The strategy is based on planning in the belief space of probability distribution over states. Our planning approach is based on hierarchical symbolic regression (pre-image back-chaining). We develop a vocabulary of fluents that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators lead to task-oriented perception in support of the manipulation goals.

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State-Based Regression with Sensing and Knowledge

Cited by 3 publications

References 7 publications

Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond

Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond

Pre-image Backchaining in Belief Space for Mobile Manipulation

Unifying perception, estimation and action for mobile manipulation via belief space planning

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