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Tuesday, August 11, 2020 | History

2 edition of Knowledge, belief, and noisy sensing in the situation calculus. found in the catalog.

Knowledge, belief, and noisy sensing in the situation calculus.

Patricio D. Simari

Knowledge, belief, and noisy sensing in the situation calculus.

by Patricio D. Simari

  • 212 Want to read
  • 21 Currently reading

Published .
Written in English


About the Edition

The extension to the situation calculus presented by Bacchus et al. formalizes the concept of noisy actions and shows how an agent can update its beliefs, which are modeled probabilistically, when relying on noisy sensors and effectors. The extensions of Scherl and Levesque and Shapiro et al. also model knowledge and belief. While assuming noiseless actions and dealing with boolean beliefs, these frameworks support properties of knowledge and belief such as introspection about current and past beliefs. Here, it is shown how such properties of belief can be formalized and supported in the probabilistic Bacchus et al. extension. In addition, the concept of sensor coarseness is introduced and it is shown how it can be modeled in the Bacchus et al. framework. Finally, it is shown that the Bacchus et al. framework can function in a way which is equivalent to using conditional probability densities to combine noisy sensor readings.

The Physical Object
Pagination68 leaves.
Number of Pages68
ID Numbers
Open LibraryOL19512418M
ISBN 100612952762

Iterated Belief Change and Exogenous Actions in the Situation Calculus.  In R. López de Mántaras and L. Saitta (Eds.),  Proceedings of the 16th European Conference on Artificial Intelligence. Knowledge and belief are two concepts that can really make you get lost in deep thought if you think long over them. There are more than one aspects in which you can look at the two concepts. Philosophers have always debated about where lies the difference between knowledge and belief. Here is an account of knowledge vs. belief.

Belief revision always results in trusting new evidence, so it may admit an unreliable one and discard a more confident one. We therefore use belief change instead of belief revision to remedy this weakness. By introducing epistemic states, we take into account of the strength of evidence that influences the change of belief. In this paper, we present a set of postulates to characterize belief. A knowledge base of beliefs. 2. \it must rst be capable of being told" [McCarthy ’59] A way to put new beliefs into the knowledge base. 3. \automatically deduces for itself a su ciently wide class of immediate consequences" [McCarthy ’59] A reasoning mechanism to derive new beliefs from ones already in the knowledge base. Page

Situation Calculus (Reiter ), A-languages (Gel- • A goal situation being a set of belief states B G. • A set of actions A. • A subset of actions A(b) ⊆ A for each belief state plete knowledge and sensing (we call it KL1), that is based on the ability to model the agent’s knowledge.   Knowledge vs Belief. Knowledge and Belief are two words that are often confused when it comes to their meanings and connotations when strictly speaking, there is some difference between them. Knowledge is all about information. Knowledge is what we .


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Knowledge, belief, and noisy sensing in the situation calculus by Patricio D. Simari Download PDF EPUB FB2

Knowledge, Belief, and Noisy Sensing in the Situation Calculus by Patricio D. Simari A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Computer Science University of Toronto. The extension to the situation calculus presented by Bacchus et izes the concept of noisy actions and shows how an agent can update its beliefs, which are modeled probabilistically, when relying on noisy sensors and e ectors.

The extensions of Scherl. Knowledge, Belief, and Noisy Sensing in the Situation Calculus Patricio D. Simari Master of Science Graduate Department of Computer Science University of Toronto The extension to the situation calculus presented by Bacchus et al.

Knowledge formalizes the concept of noisy actions and shows how an agent can update its beliefs, which are modeled probabilistically, when relying on noisy sensors and. Knowledge, Belief, and Noisy Sensing in the Situation Calculus.

Knowledge, Belief, and Noisy Sensing in the Situation Calculus Patricio D. Simari Master of Science Graduate Department of Computer Science University of Toronto The extension to the situation calculus presented by Bacchus et al.

formalizes the concept of noisy actions and shows how an agent can update its beliefs, which are modeled probabilistically, when relying on noisy sensors and Author: and Patricio D. Simari and Patricio D.

Simari. noisy sensing actions can be handled in iterated belief change within the situation calculus for-malism. We extend the framework proposed in [22] with the capability of managing noisy sens-ings. We demonstrate that an agent can still de-tect the actual situation when the ratio of noisy sensing actions vs.

accurate sensing actions is limited. Belief change with noisy sensing in the situation calculus. Article This paper describes one approach for inducing degrees of belief from very rich knowledge bases, that can include.

As a consequence, noisy sensing actions in this framework will lead to an agent facing inconsistent situation and subsequently the agent cannot proceed further. In this paper, we investigate how noisy sensing actions can be handled in iterated belief change within the situation calculus formalism.

In this paper, we generalize the framework of Shapiro et al. [], where belief change due to sensing was combined with belief introspection in the situation calculus. the situation calculus. Recently, Fang and Liu extended the situation calculus to account for multi-agent knowledge and belief change.

In this paper, based on their framework, we investigate progression of both belief and knowledge in the single-agent propositional case. We first present a model-theoretic definition of progression of knowl.

result of noisy actions, as well as the changes to the beliefs of an agent after noisy sensing. In this paper, there is a natural evolution of theory and formula-tion when compared to Levesque’s account.

The original account was based on the situation calculus extended for knowledge and sensing [9] derived from classical epistemic logic [10]. To reason about belief change, the BHL model is then embedded in a rich theory of action and sensing provided by the situation calculus.

The BHL account provides axioms in the situation calculus regarding how the weight associated with a possible world changes as the result of acting and sensing. We propose the action language EPEC – Epistemic Probabilistic Event Calculus – that supports probabilistic, epistemic reasoning about narratives of action occurrences and environmentally triggered events, and in particular facilitates reasoning about future belief-conditioned actions and their consequences in domains that include both perfect and imperfect sensing actions.

inherently noisy, and in general serves only to increase the agent’s degree of confi-dence in variouspropositions. Buildingon a generallogical theoryofaction formal-ized in the situation calculus, developed by Reiter and others, we propose a simple axiomatization of the effect on an agent’s state of belief of taking a reading from a.

probabilities can be added, and present a simple formalization within the situation calculus of the degree of belief an agent has in propositions expressed as logical formulas. This allows us to formalize in more quantitative terms the changes in belief that arise when dealing with noisy sensors and effectors.

Among the approaches dealing with uncertainty, the one by Bacchus, Halpern and Levesque, which uses the situation calculus, is perhaps the most expressive.

However, there are still some open issues. For example, it remains unclear what an agent's knowledge base would actually look like. Recall that the situation calculus is a sorted logic with sorts S (situations), A (actions), F (fluents), etc.

An essential component of any situation calculus theory is a description of how the world is affected by actions. This is done by means of effect axioms, which given a situation s, specify how the world would be in a situation do(a;s). in its knowledge base. Our solution to these problems is to reconstruct the BHL way of updating probabilistic belief in a recently proposed variant of the situation calculus (Lakemeyer and Levesque ), where situation terms are banned from the language.

Instead they are used only as part of the possible-world se-mantics of the logic. This book presents a comprehensive treatment of these ideas, basing its theoretical and implementation foundations on the situation calculus, a dialect of first-order logic. Within this framework, it develops many features of dynamical systems modeling, including time, processes, concurrency, exogenous events, reactivity, sensing and knowledge.

actions and sensing results, so these must be known ahead of time. Second, it works by regression: to calculate belief in ˚after the actions and sensing, it regresses ˚to a formula ˚0 about the initial state (using the machinery of the situation calculus), and then calculates the initial belief in ˚0.

This re. The formalism also requires second-order logic to represent uncertain beliefs, yet a first-order representation clearly seems preferable.

In this talk we show how these issues can be addressed by incorporating noisy sensors and actions into an existing logic of. This raises the question as to what the specification for correctness should look like, since Levesque’s account makes the assumption that sensing is exact and actions are deterministic.

Building on a situation calculus theory for reasoning about degrees of belief and noise, we revisit the execution semantics of generalised plans.Reasoning about Noisy Sensors in the Situation Calculus, F.

Bacchus, J.Y. Halpern and H.J. Levesque, International Joint Conference on Artificial Intelligence (IJCAI), pagesDownward Refinement and the Efficiency of Hierarchical Problem Solving, F.

Bacchus and Q. Yang, Artificial Intelligence vol. 71, pages