Universal semantic communication I

Juba, Brendan; Sudan, Madhu

doi:10.1145/1374376.1374397

Cited by 46 publications

(53 citation statements)

References 10 publications

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“…The semantic communication model [11,6] was introduced to model the problem of communication in the presence of possible misunderstanding. In particular, it was intended to address settings where two computers communicate using communications protocols designed and implemented by different parties.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Communication for Simple Goals Is Equivalent to On-line Learning

Juba

Vempala

2011

Lecture Notes in Computer Science

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Abstract. Previous works [11,6] introduced a model of semantic communication between a "user" and a "server," in which the user attempts to achieve a given goal for communication. They show that whenever the user can sense progress, there exist universal user strategies that can achieve the goal whenever it is possible for any other user to reliably do so. A drawback of the actual constructions is that the users are inefficient: they enumerate protocols until they discover one that is successful, leading to the potential for exponential overhead in the length of the desired protocol. Goldreich et al. [6] conjectured that this overhead could be reduced to a polynomial dependence if we restricted our attention to classes of sufficiently simple user strategies and goals. In this work, we are able to obtain such universal strategies for some reasonably general special cases by establishing an equivalence between these special cases and the usual model of mistake-bounded on-line learning [3,15]. This equivalence also allows us to see the limits of constructing universal users based on sensing and motivates the study of sensing with richer kinds of feedback. Along the way, we also establish a new lower bound for the "beliefs model" [12], which demonstrates that constructions of efficient users in that framework rely on the existence of a common "belief" under which all of the servers in a class are designed to be efficient.

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Section: Introductionmentioning

confidence: 99%

“…Previous works [11,6] introduced a model of semantic communication between a "user" and a "server," in which the user attempts to achieve a given goal for communication. They show that whenever the user can sense progress, there exist universal user strategies that can achieve the goal whenever it is possible for any other user to reliably do so.…”

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confidence: 99%

Semantic Communication for Simple Goals Is Equivalent to On-line Learning

Juba

Vempala

2011

Lecture Notes in Computer Science

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“…Several authors have investigated other approaches of modeling semantic information, e.g., algebraic information theories [35,37], universal semantic communication [33,34] and semantic coding [61]. Some recent work has been collected in two proceedings [44,56].…”

Section: Related Workmentioning

confidence: 99%

Towards a Theory of Semantic Communication (Extended Technical Report)

Bao¹,

Basu²,

Dean³

et al. 2011

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Abstract-This paper studies methods of quantitatively measuring semantic information in communication. We review existing work on quantifying semantic information, then investigate a model-theoretical approach for semantic data compression and reliable semantic communication. We relate our approach to the statistical measurement of information by Shannon, and show that Shannon's source and channel coding theorems have semantic counterparts.

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“…Remarkably, especially in the natural setting, context can seemingly be used to compress communication, even when it is not shared perfectly. This ability to communicate despite a major source of uncertainty has led to a series of works attempting to model various forms of communication amid uncertainty, starting with Goldreich, Juba and Sudan [8,5] followed by [7,9,10,6,4]. This current work introduces a new theme to this series of works by introducing a functional notion of uncertainty and studying this model.…”

Section: Introductionmentioning

confidence: 99%

“…The first works to consider communication with uncertainty in a manner similar to this work were those of [8,5]. Their goal was to model an extreme form of uncertainty, where Alice and Bob do not have any prior (known) commonality in context and indeed both come with their own "protocol" which tells them how to communicate.…”

Section: Introductionmentioning

confidence: 99%

Communication with Contextual Uncertainty

Ghazi

Komargodski

Kothari

et al. 2015

Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms

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We introduce a simple model illustrating the role of context in communication and the challenge posed by uncertainty of knowledge of context. We consider a variant of distributional communication complexity where Alice gets some information x and Bob gets y, where (x, y) is drawn from a known distribution, and Bob wishes to compute some function g(x, y) (with high probability over (x, y)). In our variant, Alice does not know g, but only knows some function f which is an approximation of g. Thus, the function being computed forms the context for the communication, and knowing it imperfectly models (mild) uncertainty in this context.A naive solution would be for Alice and Bob to first agree on some common function h that is close to both f and g and then use a protocol for h to compute h(x, y). We show that any such agreement leads to a large overhead in communication ruling out such a universal solution.In contrast, we show that if g has a one-way communication protocol with complexity k in the standard setting, then it has a communication protocol with complexity O(k · (1 + I)) in the uncertain setting, where I denotes the mutual information between x and y. In the particular case where the input distribution is a product distribution, the protocol in the uncertain setting only incurs a constant factor blow-up in communication and error.Furthermore, we show that the dependence on the mutual information I is required. Namely, we construct a class of functions along with a non-product distribution over (x, y) for which the communication complexity is a single bit in the standard setting but at least Ω( √ n) bits in the uncertain setting.

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Universal semantic communication I

Cited by 46 publications

References 10 publications

Semantic Communication for Simple Goals Is Equivalent to On-line Learning

Semantic Communication for Simple Goals Is Equivalent to On-line Learning

Towards a Theory of Semantic Communication (Extended Technical Report)

Communication with Contextual Uncertainty

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