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.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

scholarly journals IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs

2019 ◽  
Vol 33 ◽  
pp. 6062-6069 ◽  
Author(s):  
Yanlin Han ◽  
Piotr Gmytrasiewicz
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Neural Computing ◽  
Neural Network Architecture ◽  
Markov Decision ◽  
Planning Algorithm ◽  
Multi Agent ◽  
Partially Observable ◽  
Multi Agent Planning

This paper introduces the IPOMDP-net, a neural network architecture for multi-agent planning under partial observability. It embeds an interactive partially observable Markov decision process (I-POMDP) model and a QMDP planning algorithm that solves the model in a neural network architecture. The IPOMDP-net is fully differentiable and allows for end-to-end training. In the learning phase, we train an IPOMDP-net on various fixed and randomly generated environments in a reinforcement learning setting, assuming observable reinforcements and unknown (randomly initialized) model functions. In the planning phase, we test the trained network on new, unseen variants of the environments under the planning setting, using the trained model to plan without reinforcements. Empirical results show that our model-based IPOMDP-net outperforms the other state-of-the-art modelfree network and generalizes better to larger, unseen environments. Our approach provides a general neural computing architecture for multi-agent planning using I-POMDPs. It suggests that, in a multi-agent setting, having a model of other agents benefits our decision-making, resulting in a policy of higher quality and better generalizability.

scholarly journals Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

2019 ◽  
Vol 33 ◽  
pp. 4295-4303 ◽  
Author(s):  
Rung-Tzuo Liaw ◽  
Chuan-Kang Ting
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Benchmark Problems ◽  
Good Estimator ◽  
Multiple Tasks ◽  
Effectiveness And Efficiency ◽  
Evolutionary Multitasking

Evolutionary multitasking is a significant emerging search paradigm that utilizes evolutionary algorithms to concurrently optimize multiple tasks. The multi-factorial evolutionary algorithm renders an effectual realization of evolutionary multitasking on two or three tasks. However, there remains room for improvement on the performance and capability of evolutionary multitasking. Beyond three tasks, this paper proposes a novel framework, called the symbiosis in biocoenosis optimization (SBO), to address evolutionary many-tasking optimization. The SBO leverages the notion of symbiosis in biocoenosis for transferring information and knowledge among different tasks through three major components: 1) transferring information through inter-task individual replacement, 2) measuring symbiosis through intertask paired evaluations, and 3) coordinating the frequency and quantity of transfer based on symbiosis in biocoenosis. The inter-task individual replacement with paired evaluations caters for estimation of symbiosis, while the symbiosis in biocoenosis provides a good estimator of transfer. This study examines the effectiveness and efficiency of the SBO on a suite of many-tasking benchmark problems, designed to deal with 30 tasks simultaneously. The experimental results show that SBO leads to better solutions and faster convergence than the state-of-the-art evolutionary multitasking algorithms. Moreover, the results indicate that SBO is highly capable of identifying the similarity between problems and transferring information appropriately.

scholarly journals Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

2021 ◽  
Author(s):  
Weijia Zhang
Keyword(s):  
Medical Imaging ◽  
Supervised Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Results ◽  
Weakly Supervised Learning ◽  
Variational Autoencoder ◽  
Label Prediction ◽  
Weakly Supervised ◽  
Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

Imperialist Competitive Algorithm with Updated Assimilation for the Solution of Real Valued Optimization Problems

2018 ◽  
Vol 27 (02) ◽  
pp. 1850005
Author(s):  
Zhavat Sherinov ◽  
Ahmet Ünveren ◽  
Adnan Acan
Keyword(s):  
Euclidean Distance ◽  
Optimization Problems ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Imperialist Competitive Algorithm ◽  
Benchmark Problems ◽  
Pearson Correlation Coefficient ◽  
Competitive Algorithm ◽  
Imperialistic Competitive Algorithm ◽  
Better Than

In this paper, an improved imperialistic competitive algorithm is presented for real-valued optimization problems. A new method is introduced for the movement of colonies towards their imperialist, which is called assimilation. The proposed method uses Euclidean distance along with Pearson correlation coefficient as an operator for assimilating colonies with respect to their imperialists. Applications of the proposed algorithm to classical and recently published hard benchmark problems, and statistical analysis associated with the corresponding experimental results illustrated that the achieved success is significantly better than a number of state-of-the art methods.

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.

An Effective Dynamic Evolutionary Algorithm for Engineering Problems

2012 ◽  
Vol 542-543 ◽  
pp. 294-301
Author(s):  
Qing Zhang ◽  
San You Zeng ◽  
Hai Qing Ye ◽  
Zheng Jun Li ◽  
Hong Yong Jing
Keyword(s):  
Evolutionary Algorithms ◽  
State Of The Art ◽  
Global Search ◽  
Benchmark Problems ◽  
The Third ◽  
Dynamic Technique ◽  
Effective Dynamic ◽  
Engineering Problems ◽  
General Test ◽  
Better Than

A dynamic evolutionary algorithms (DEA) is designed to solve engineering problems in this paper. The DEA algorithm makes two differences. (1) Dynamic technique is used to handle equality constraints. (2) Two unrelated crossovers (linear crossover and uniform crossover) are combined in the algorithm for avoiding duplicate search and then helping global search. In solving engineering problems, three steps are taken: a DEA algorithm is designed first, then after tested by general benchmark problems, it is improved, and the third step is that the improved DEA algorithm is applied to solve engineering problems. The general test suggests our DEA algorithm outperforms the compared state-of-the-art other algorithms. The experimental results in solving 5 engineering problems indicate that our method works much better than the compared state-of-the-art algorithms, especially, in global search.

Fast Strong Planning for FOND Problems with Multi-Root Directed Acyclic Graphs

2014 ◽  
Vol 23 (06) ◽  
pp. 1460028 ◽  
Author(s):  
Andres Calderon Jaramillo ◽  
Jicheng Fu ◽  
Vincent Ng ◽  
Farokh B. Bastani ◽  
I-Ling Yen
Keyword(s):  
State Of The Art ◽  
Solution Space ◽  
Strong Solutions ◽  
Search Space ◽  
Directed Acyclic Graphs ◽  
The State ◽  
Benchmark Problems ◽  
Acyclic Graphs ◽  
Planning Problems ◽  
Improve Planning

Recently, the state-of-the-art AI planners have significantly improved planning efficiency on Fully Observable Nondeterministic planning (FOND) problems with strong cyclic solutions. These strong cyclic solutions are guaranteed to achieve the goal if they terminate, implying that there is a possibility that they may run into indefinite loops. In contrast, strong solutions are guaranteed to achieve the goal, but few planners can effectively handle FOND problems with strong solutions. In this study, we aim to address this difficult, yet under-investigated class of planning problems: FOND planning problems with strong solutions. We present a planner that employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. Based on the characteristics of MRDAG, we develop heuristics to ensure planning towards the relevant search direction and design optimizations to prune the search space to further improve planning efficiency. We perform extensive experiments to evaluate MRDAG, the heuristics, and the optimizations for pruning the search space. Experimental results show that our strong algorithm achieves impressive performance on a variety of benchmark problems: on average it runs more than three orders of magnitude faster than the state-of-the-art planners, MBP and Gamer, while demonstrating significantly better scalability.

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 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.

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