scholarly journals Compact Tree Encodings for Planning as QBF

2018 ◽  
Vol 21 (62) ◽  
pp. 103 ◽  
Author(s):  
Olivier Gasquet
Keyword(s):  
Artificial Intelligence ◽  
Execution Time ◽  
Boolean Formula ◽  
Benchmark Problems ◽  
Sat Solvers ◽  
Quantified Boolean Formula ◽  
And Performance ◽  
Near Future ◽  
Planning Problems ◽  
Better Than

Considerable improvements in the technology and performance of SAT solvers has made their use possible for the resolution of various problems in artificial intelligence, and among them that of generating plans. Recently, promising Quantified Boolean Formula (QBF) solvers have been developed and we may expect that in a near future they become as efficient as SAT solvers. So, it is interesting to use QBF language that allows us to produce more compact encodings. We present in this article a translation from STRIPS planning problems into quantified propositional formulas. We introduce two new Compact Tree Encodings: CTE-EFA based on Explanatory frame axioms, and CTE-OPEN based on causal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in [Cashmore et al. 2012]. In terms of execution time over benchmark problems, CTE-EFA and CTE-OPEN always performed better than CTE-NOOP.

scholarly journals Compact Tree Encodings for Planning as QBF

2018 ◽  
Vol 21 (62) ◽  
pp. 103-113
Author(s):  
Olivier Gasquet ◽  
Dominique Longin ◽  
Fr´ed´eric Maris ◽  
Pierre R´egnier ◽  
Ma¨el Valais
Keyword(s):  
Artificial Intelligence ◽  
Execution Time ◽  
Boolean Formula ◽  
Benchmark Problems ◽  
Sat Solvers ◽  
Quantified Boolean Formula ◽  
And Performance ◽  
Near Future ◽  
Planning Problems ◽  
Better Than

Considerable improvements in the technology and performance of SAT solvers has made their use possible for the resolution of various problems in artificial intelligence, and among them that of generating plans. Recently, promising Quantified Boolean Formula (QBF) solvers have been developed and we may expect that in a near future they become as efficient as SAT solvers. So, it is interesting to use QBF language that allows us to produce more compact encodings. We present in this article a translation from STRIPS planning problems into quantified propositional formulas. We introduce two new Compact Tree Encodings: CTE-EFA based on Explanatory frame axioms, and CTE-OPEN based on causal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in [Cashmore et al. 2012]. In terms of execution time over benchmark problems, CTE-EFA and CTE-OPEN always performed better than CTE-NOOP.

scholarly journals Replanning in Domains with Partial Information and Sensing Actions

2012 ◽  
Vol 45 ◽  
pp. 565-600 ◽  
Author(s):  
R. I. Brafman ◽  
G. Shani
Keyword(s):  
Original Problem ◽  
Partial Information ◽  
The State ◽  
Belief State ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Online Planning ◽  
Sensing Actions ◽  
Planning Problems ◽  
Better Than

Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, non-stochastic domains with partial information and sensing actions, presenting a new planner: SDR (Sample, Determinize, Replan). At each step we generate a solution plan to a classical planning problem induced by the original problem. We execute this plan as long as it is safe to do so. When this is no longer the case, we replan. The classical planning problem we generate is based on the translation-based approach for conformant planning introduced by Palacios and Geffner. The state of the classical planning problem generated in this approach captures the belief state of the agent in the original problem. Unfortunately, when this method is applied to planning problems with sensing, it yields a non-deterministic planning problem that is typically very large. Our main contribution is the introduction of state sampling techniques for overcoming these two problems. In addition, we introduce a novel, lazy, regression-based method for querying the agent's belief state during run-time. We provide a comprehensive experimental evaluation of the planner, showing that it scales better than the state-of-the-art CLG planner on existing benchmark problems, but also highlighting its weaknesses with new domains. We also discuss its theoretical guarantees.

scholarly journals Multi-Agent Planning with Baseline Regret Minimization

2017 ◽  
Author(s):  
Feng Wu ◽  
Shlomo Zilberstein ◽  
Xiaoping Chen
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Benchmark Problems ◽  
Minimization Algorithm ◽  
Generation Algorithm ◽  
Regret Minimization ◽  
Multi Agent ◽  
Planning Problems ◽  
Multi Agent Planning ◽  
Better Than

We propose a novel baseline regret minimization algorithm for multi-agent planning problems modeled as finite-horizon decentralized POMDPs. It guarantees to produce a policy that is provably better than or at least equivalent to the baseline policy. We also propose an iterative belief generation algorithm to effectively and efficiently minimize the baseline regret, which only requires necessary iterations to converge to the policy with minimum baseline regret. Experimental results on common benchmark problems confirm its advantage comparing to the state-of-the-art approaches.

scholarly journals Organic chemistry synthesis problem as artificial intelligence planning.

2021 ◽  
Author(s):  
Arman Masoumi
Keyword(s):  
Organic Chemistry ◽  
Artificial Intelligence ◽  
Functional Groups ◽  
Organic Synthesis ◽  
Chemical Reactions ◽  
Benchmark Problems ◽  
Logical Language ◽  
Artificial Intelligence Planning ◽  
Planning Problems ◽  
Novel Algorithm

This thesis formulates organic chemistry synthesis problems as Artificial Intelligence planning problems and uses a combination of techniques developed in the field of planning to solve organic synthesis problems. To this end, a methodology for axiomatizing organic chemistry is developed, which includes axiomatizing molecules and functional groups, as well as two approaches for representing chemical reactions in a logical language amenable to reasoning. A novel algorithm for planning specific to organic chemistry is further developed, based on which a planner capable of identifying 75 functional groups and chemical classes is implemented with a knowledge base of 55 generic chemical reactions. The performance of the planner is empirically evaluated on two sets of benchmark problems and analytically compared with a number of competing algorithms. v

scholarly journals Organic chemistry synthesis problem as artificial intelligence planning.

2021 ◽  
Author(s):  
Arman Masoumi
Keyword(s):  
Organic Chemistry ◽  
Artificial Intelligence ◽  
Functional Groups ◽  
Organic Synthesis ◽  
Chemical Reactions ◽  
Benchmark Problems ◽  
Logical Language ◽  
Artificial Intelligence Planning ◽  
Planning Problems ◽  
Novel Algorithm

This thesis formulates organic chemistry synthesis problems as Artificial Intelligence planning problems and uses a combination of techniques developed in the field of planning to solve organic synthesis problems. To this end, a methodology for axiomatizing organic chemistry is developed, which includes axiomatizing molecules and functional groups, as well as two approaches for representing chemical reactions in a logical language amenable to reasoning. A novel algorithm for planning specific to organic chemistry is further developed, based on which a planner capable of identifying 75 functional groups and chemical classes is implemented with a knowledge base of 55 generic chemical reactions. The performance of the planner is empirically evaluated on two sets of benchmark problems and analytically compared with a number of competing algorithms. v

scholarly journals Realization and optimization of a new spike detection algorithm logic circuit

2021 ◽  
Vol 2113 (1) ◽  
pp. 012039
Author(s):  
Yuefeng Gao ◽  
Qingxing Liu ◽  
Hengji Ye
Keyword(s):  
Artificial Intelligence ◽  
Time Delay ◽  
Integrated Circuit ◽  
Binary Code ◽  
Detection Algorithm ◽  
Interdisciplinary Cooperation ◽  
Logic Circuit ◽  
And Performance ◽  
The Relationship ◽  
Better Than

Abstract At present, artificial intelligence has increasingly become the most promising and essential major. The interdisciplinary cooperation between the integrated circuit and artificial intelligence brings infinite possibilities. In the convolutional neural network area, in order to achieve the valuable output, the comparison between values is often encountered. The output is obtained by comparison between the result after the convolution calculation with the designed threshold. Therefore, our team not only design a 4-bit binary comparator hardware logic circuit to complete the task but also discuss and verify the feasibility and performance characteristics of the program from the perspective of energy and time delay. As for the overall framework, we design a convenient circuit that converts the complement code into the original code and chooses a CLA adder to accomplish this part. Using this kind of adder can ideally help us reduce the time delay at the expense of a complicated circuit schematic. In the comparator part, we design a high-quality circuit framework. The strategy of our circuit is to compare the relationship between the four-bit binary code and the threshold bit by bit from MSB to LSB, which performs better than the original 4-bit comparator, and we design two outputs that can legibly illustrate the relationship between two values. We use logic effort to discuss the normalized delay in our project. Besides, we find the connection between the energy and the delay by calculation. Finally, we design a trade-off function to make the optimization of energy and delay together with respect to voltage.

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals Why a Virtual Assistant for Moral Enhancement When We Could have a Socrates?

2021 ◽  
Vol 27 (4) ◽  
Author(s):  
Francisco Lara
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
New Technologies ◽  
Personal Autonomy ◽  
Moral Enhancement ◽  
Moral Decisions ◽  
Educational Methodology ◽  
Better Than

AbstractCan Artificial Intelligence (AI) be more effective than human instruction for the moral enhancement of people? The author argues that it only would be if the use of this technology were aimed at increasing the individual's capacity to reflectively decide for themselves, rather than at directly influencing behaviour. To support this, it is shown how a disregard for personal autonomy, in particular, invalidates the main proposals for applying new technologies, both biomedical and AI-based, to moral enhancement. As an alternative to these proposals, this article proposes a virtual assistant that, through dialogue, neutrality and virtual reality technologies, can teach users to make better moral decisions on their own. The author concludes that, as long as certain precautions are taken in its design, such an assistant could do this better than a human instructor adopting the same educational methodology.

Sign in / Sign up

Export Citation Format

Share Document