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Everything Wrong with Bitcoin in 2019

The Top 30 Salesforce Consulting Companies

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Oded Blumenthal, Software and Information Systems Engineering, Ben Gurion University, Israel;
(2) Guy Shani, Software and Information Systems Engineering, Ben Gurion University, Israel. Table of Links Introduction
Background
Related Work
POMCP for Stochastic Contingent Planning
Domain Independent Heuristics for POMCP
Empirical Evaluation
Conclusion and References 4 POMCP for Stochastic Contingent Planning We focus here on goal-oriented POMDP domains specified as stochastic contingent planning problems. We now define this concept formally. This definition does not support noisy observations, however, this is not truly a limitation. One can compile a noisy observation into a deterministic observation over an artificial fact whose value changes stochastically. Consider, for example, a sensor that nosily detects whether there is a wall in front of a robot. Instead of noisily observing whether there is a wall, we can deterministically detect a green light that is lit when the sensor (stochastically) detects a wall. That is, we can observe the green light without noise, but the green light is only noisily correlated with the existence of a wall. Algorithm 1 describes the POMCP implementation for stochastic contingent planning problems. When the agent needs to act, it calls Search. Search repeatedly samples a state (line 3-4) and simulates forward execution given this state is the true underlying system state. The Simulate procedure is recursive. We first check whether the current tree node is a goal belief. This is done be regressing the negation of the goal formula ¬G through the history of the current node. For goal beliefs, the value is 0, and we can stop. Our implementation of POMCP also stops deepening the tree after a predefined threshold Max TreeDepth. If that threshold is reached (line 12), we run a Rollout to compute an estimation for the cost of reaching the goal from this node (line 13). In line 15 we check whether this node has already been expanded, and if not, we compute its children. We do so only for applicable actions whose preconditions are satisfied in the current belief (line 17). Again, this is computed using regression over the history. We now select an action a using the UCT exploration-exploitation criterion (line 21), and sample a next state and an observation (lines 22-27). We call Simulate recursively in line 28. Lines 29-32 update the value of the current node. Lines 28,29 update the counters for the executed action and received observation. Line 31 computes the value for the action as a weighted average over all observations. Line 32 computes the value of the node as the minimal cost among all actions. Our value update, which we empirically found to be more useful, is different than the original POMCP, which uses incremental updates, and more similar to the value update in DESPOT [18]. The Rollout procedure receives as input the current simulated state s, as well as a set B of states (particles) in the node from which the rollout begins. B is used by some of our rollout heuristics, as we explain below. The rollout executes actions given the heuristic rollout policy until the goal has been reached, or a maximal number of steps has been reached. This paper is available on arxiv under CC 4.0 license. Authors: (1) Oded Blumenthal, Software and Information Systems Engineering, Ben Gurion University, Israel; (2) Guy Shani, Software and Information Systems Engineering, Ben Gurion University, Israel. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Oded Blumenthal, Software and Information Systems Engineering, Ben Gurion University, Israel; (2) Guy Shani, Software and Information Systems Engineering, Ben Gurion University, Israel. Table of Links Introduction Background Related Work POMCP for Stochastic Contingent Planning Domain Independent Heuristics for POMCP Empirical Evaluation Conclusion and References Introduction Introduction Background Background Related Work Related Work POMCP for Stochastic Contingent Planning POMCP for Stochastic Contingent Planning Domain Independent Heuristics for POMCP Domain Independent Heuristics for POMCP Empirical Evaluation Empirical Evaluation Conclusion and References Conclusion and References 4 POMCP for Stochastic Contingent Planning We focus here on goal-oriented POMDP domains specified as stochastic contingent planning problems. We now define this concept formally. This definition does not support noisy observations, however, this is not truly a limitation. One can compile a noisy observation into a deterministic observation over an artificial fact whose value changes stochastically. Consider, for example, a sensor that nosily detects whether there is a wall in front of a robot. Instead of noisily observing whether there is a wall, we can deterministically detect a green light that is lit when the sensor (stochastically) detects a wall. That is, we can observe the green light without noise, but the green light is only noisily correlated with the existence of a wall. Algorithm 1 describes the POMCP implementation for stochastic contingent planning problems. When the agent needs to act, it calls Search. Search repeatedly samples a state (line 3-4) and simulates forward execution given this state is the true underlying system state. The Simulate procedure is recursive. We first check whether the current tree node is a goal belief. This is done be regressing the negation of the goal formula ¬G through the history of the current node. For goal beliefs, the value is 0, and we can stop. Our implementation of POMCP also stops deepening the tree after a predefined threshold Max TreeDepth. If that threshold is reached (line 12), we run a Rollout to compute an estimation for the cost of reaching the goal from this node (line 13). In line 15 we check whether this node has already been expanded, and if not, we compute its children. We do so only for applicable actions whose preconditions are satisfied in the current belief (line 17). Again, this is computed using regression over the history. We now select an action a using the UCT exploration-exploitation criterion (line 21), and sample a next state and an observation (lines 22-27). We call Simulate recursively in line 28. Lines 29-32 update the value of the current node. Lines 28,29 update the counters for the executed action and received observation. Line 31 computes the value for the action as a weighted average over all observations. Line 32 computes the value of the node as the minimal cost among all actions. Our value update, which we empirically found to be more useful, is different than the original POMCP, which uses incremental updates, and more similar to the value update in DESPOT [18]. The Rollout procedure receives as input the current simulated state s, as well as a set B of states (particles) in the node from which the rollout begins. B is used by some of our rollout heuristics, as we explain below. The rollout executes actions given the heuristic rollout policy until the goal has been reached, or a maximal number of steps has been reached.

Rollout Heuristics: POMCP for Stochastic Contingent Planning

A Strange Metal Creature Comes to Earth on a Dreadful Mission

একটি অদ্ভুত ধাতব প্রাণী একটি ভয়ঙ্কর মিশনে পৃথিবীতে আসে

Eine seltsame Metallkreatur kommt auf einer schrecklichen Mission auf die Erde

무서운 임무를 위해 지구에 온 이상한 금속 생물

Garip Bir Metal Yaratık Korkunç Bir Görevle Dünya'ya Geliyor

The Island of Doctor Moreau, by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here. IV. AT THE SCHOONER’S RAIL IV. AT THE SCHOONER’S RAIL That night land was sighted after sundown, and the schooner hove to. Montgomery intimated that was his destination. It was too far to see any details; it seemed to me then simply a low-lying patch of dim blue in the uncertain blue-grey sea. An almost vertical streak of smoke went up from it into the sky. The captain was not on deck when it was sighted. After he had vented his wrath on me he had staggered below, and I understand he went to sleep on the floor of his own cabin. The mate practically assumed the command. He was the gaunt, taciturn individual we had seen at the wheel. Apparently he was in an evil temper with Montgomery. He took not the slightest notice of either of us. We dined with him in a sulky silence, after a few ineffectual efforts on my part to talk. It struck me too that the men regarded my companion and his animals in a singularly unfriendly manner. I found Montgomery very reticent about his purpose with these creatures, and about his destination; and though I was sensible of a growing curiosity as to both, I did not press him. We remained talking on the quarter deck until the sky was thick with stars. Except for an occasional sound in the yellow-lit forecastle and a movement of the animals now and then, the night was very still. The puma lay crouched together, watching us with shining eyes, a black heap in the corner of its cage. Montgomery produced some cigars. He talked to me of London in a tone of half-painful reminiscence, asking all kinds of questions about changes that had taken place. He spoke like a man who had loved his life there, and had been suddenly and irrevocably cut off from it. I gossiped as well as I could of this and that. All the time the strangeness of him was shaping itself in my mind; and as I talked I peered at his odd, pallid face in the dim light of the binnacle lantern behind me. Then I looked out at the darkling sea, where in the dimness his little island was hidden. This man, it seemed to me, had come out of Immensity merely to save my life. To-morrow he would drop over the side, and vanish again out of my existence. Even had it been under commonplace circumstances, it would have made me a trifle thoughtful; but in the first place was the singularity of an educated man living on this unknown little island, and coupled with that the extraordinary nature of his luggage. I found myself repeating the captain’s question. What did he want with the beasts? Why, too, had he pretended they were not his when I had remarked about them at first? Then, again, in his personal attendant there was a bizarre quality which had impressed me profoundly. These circumstances threw a haze of mystery round the man. They laid hold of my imagination, and hampered my tongue. Towards midnight our talk of London died away, and we stood side by side leaning over the bulwarks and staring dreamily over the silent, starlit sea, each pursuing his own thoughts. It was the atmosphere for sentiment, and I began upon my gratitude. “If I may say it,” said I, after a time, “you have saved my life.” “Chance,” he answered. “Just chance.” “I prefer to make my thanks to the accessible agent.” “Thank no one. You had the need, and I had the knowledge; and I injected and fed you much as I might have collected a specimen. I was bored and wanted something to do. If I’d been jaded that day, or hadn’t liked your face, well—it’s a curious question where you would have been now!” This damped my mood a little. “At any rate,” I began. “It’s a chance, I tell you,” he interrupted, “as everything is in a man’s life. Only the asses won’t see it! Why am I here now, an outcast from civilisation, instead of being a happy man enjoying all the pleasures of London? Simply because eleven years ago—I lost my head for ten minutes on a foggy night.” He stopped. “Yes?” said I. “That’s all.” We relapsed into silence. Presently he laughed. “There’s something in this starlight that loosens one’s tongue. I’m an ass, and yet somehow I would like to tell you.” “Whatever you tell me, you may rely upon my keeping to myself—if that’s it.” He was on the point of beginning, and then shook his head, doubtfully. “Don’t,” said I. “It is all the same to me. After all, it is better to keep your secret. There’s nothing gained but a little relief if I respect your confidence. If I don’t—well?” He grunted undecidedly. I felt I had him at a disadvantage, had caught him in the mood of indiscretion; and to tell the truth I was not curious to learn what might have driven a young medical student out of London. I have an imagination. I shrugged my shoulders and turned away. Over the taffrail leant a silent black figure, watching the stars. It was Montgomery’s strange attendant. It looked over its shoulder quickly with my movement, then looked away again. It may seem a little thing to you, perhaps, but it came like a sudden blow to me. The only light near us was a lantern at the wheel. The creature’s face was turned for one brief instant out of the dimness of the stern towards this illumination, and I saw that the eyes that glanced at me shone with a pale-green light. I did not know then that a reddish luminosity, at least, is not uncommon in human eyes. The thing came to me as stark inhumanity. That black figure with its eyes of fire struck down through all my adult thoughts and feelings, and for a moment the forgotten horrors of childhood came back to my mind. Then the effect passed as it had come. An uncouth black figure of a man, a figure of no particular import, hung over the taffrail against the starlight, and I found Montgomery was speaking to me. “I’m thinking of turning in, then,” said he, “if you’ve had enough of this.” I answered him incongruously. We went below, and he wished me good-night at the door of my cabin. That night I had some very unpleasant dreams. The waning moon rose late. Its light struck a ghostly white beam across my cabin, and made an ominous shape on the planking by my bunk. Then the staghounds woke, and began howling and baying; so that I dreamt fitfully, and scarcely slept until the approach of dawn. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (1994). The Island of Doctor Moreau. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/159/159-h/159-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

The Island of Doctor Moreau: IV. AT THE SCHOONER’S RAIL

Tavern Lecture One: A Fantastical Discourse on Blockchain, Metaverses and NFTs

Double Happiness

দ্বিগুন খুশি

Doppeltes Glück

Doble felicidad

Double bonheur

दोहरी खुशी

ダブルハピネス

행복이 두배

Felicidade em dobro

Двойное счастье

Çifte mutluluk

Hạnh phúc nhân đôi

双倍快乐

This Pharmacist Said Prisoners Wouldn’t Feel Pain During Lethal Injection, But Was That Really True?

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greenlight.guru

201-500 employees

Since 2013

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