Stochastic switching circuits are relay circuits that consist of stochastic switches called pswitches. The study of stochastic switching circuits has widespread applications in many fields of computer science, neuroscience, and biochemistry. In this paper, we discuss several properties of stochastic switching circuits, including robustness, expressibility, and probability approximation. First, we study the robustness, namely, the effect caused by introducing an error of size Є to each pswitch in a stochastic circuit. We analyze two constructions and prove that simple series-parallel circuits are robust to small error perturbations, while general series-parallel circuits are not. Specifically, the total error introduced by perturbations of size less than Є is bounded by a constant multiple of Є in a simple series-parallel circuit, independent of the size of the circuit. Next, we study the expressibility of stochastic switching circuits: Given an integer q and a pswitch set S = 1 q,2 q,...,q-1 q , can we synthesize any rational probability with denominator q^n (for arbitrary n) with a simple series-parallel stochastic switching circuit? We generalize previous results and prove that when q is a multiple of 2 or 3, the answer is yes. We also show that when q is a prime number larger than 3, the answer is no. Probability approximation is studied for a general case of an arbitrary pswitch set S = s_1, s_2,... , s_(|S|) . In this case, we propose an algorithm based on local optimization to approximate any desired probability. The analysis reveals that the approximation error of a switching circuit decreases exponentially with an increasing circuit size.
There are a number of studies focusing on synthesizing a simple physical device to generate desired probabilities. Gill @cite_3 @cite_14 discussed the problem of generating rational probabilities using a sequential state machine. Motivated by neural computation, provided an algorithm to generate binary sequences with probability @math from a set of stochastic binary sequences with probabilities in @math @cite_8 . Their method can be implemented using the concept of linear feedback shift registers. Recently, inspired by PCMOS technology @cite_13 , considered the synthesis of decimal probabilities using combinational logic @cite_1 . They have considered three different scenarios, depending on whether the given probabilities can be duplicated, and whether there is freedom to choose the probabilities. In contact to the foregoing contributions, we consider the properties and probability synthesis of stochastic switching circuits. Our approach is orthogonal and complementary to that of Qian and Riedel, which is based on combinational logic. Generally, each switching circuit can be equivalently expressed by a combinational logic circuit. All the constructive methods of stochastic switching circuits in this paper can be directly applied to probabilistic combinational logic circuits.
@cite_14 @cite_8 @cite_1 @cite_3 @cite_13
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Schemes for probabilistic computation can exploit physical sources to generate random values in the form of bit streams. Generally, each source has a fixed bias and so provides bits with a specific probability of being one. If many different probability values are required, it can be expensive to generate all of these directly from physical sources. This paper demonstrates novel techniques for synthesizing combinational logic that transforms source probabilities into different target probabilities. We consider three scenarios in terms of whether the source probabilities are specified and whether they can be duplicated. In the case that the source probabilities are not specified and can be duplicated, we provide a specific choice, the set 0.4, 0.5 ; we show how to synthesize logic that transforms probabilities from this set into arbitrary decimal probabilities. Further, we show that for any integer n ≥ 2, there exists a single probability that can be transformed into arbitrary base-n fractional probabilities. In the case that the source probabilities are specified and cannot be duplicated, we provide two methods for synthesizing logic to transform them into target probabilities. In the case that the source probabilities are not specified, but once chosen cannot be duplicated, we provide an optimal choice. Shannon in his 1938 Masterpsilas Thesis demonstrated that any Boolean function can be realized by a switching relay circuit, leading to the development of deterministic digital logic. Here, we replace each classical switch with a probabilistic switch (pswitch). We present algorithms for synthesizing circuits closed with a desired probability, including an algorithm that generates optimal size circuits for any binary fraction. We also introduce a new duality property for series-parallel stochastic switching circuits. Finally, we construct a universal probability generator which maps deterministic inputs to arbitrary probabilistic outputs. Potential applications exist in the analysis and design of stochastic networks in biology and engineering. Controller synthesis for hybrid systems that satisfy temporal specifications expressing various system properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, many of the properties the controller has to satisfy are learned through machine learning techniques based on sensor input data. In this paper, we propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an expressive language to define the stochastic properties, and enforce probabilistic guarantees on them. We further show how to synthesize safe controllers using this logic for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by synthesizing safe controllers under the PrSTL specifications for multiple case studies including control of quadrotors and autonomous vehicles in dynamic environments. We consider the problem of 20 questions with noise for multiple players under the minimum entropy criterion in the setting of stochastic search, with application to target localization. Each player yields a noisy response to a binary query governed by a certain error probability. First, we propose a sequential policy for constructing questions that queries each player in sequence and refines the posterior of the target location. Second, we consider a joint policy that asks all players questions in parallel at each time instant and characterize the structure of the optimal policy for constructing the sequence of questions. This generalizes the single player probabilistic bisection method for stochastic search problems. Third, we prove an equivalence between the two schemes showing that, despite the fact that the sequential scheme has access to a more refined filtration, the joint scheme performs just as well on average. Fourth, we establish convergence rates of the mean-square error and derive error exponents. Finally, we obtain an extension to the case of unknown error probabilities. This framework provides a mathematical model for incorporating a human in the loop for active machine learning systems.