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Using Game Theory for Los Angeles Airport Security
Articles Using Game Theory for Los Angeles Airport Security James Pita, Manish Jain, Fernando Ordóñez, Christopher Portway, Milind Tambe, Craig Western, Praveen Paruchuri, and Sarit Kraus n Security at major locations of economic or political impor tance is a key concern around P the world, particularly given the threat of ter- rotecting national infrastructure such as airports, historical rorism. Limited security resources prevent full landmarks, or a location of political or economic importance is security coverage at all times, which allows a challenging task for police and security agencies around the adversaries to observe and exploit patterns in world, a challenge that is exacerbated by the threat of terrorism. selective patrolling or mon itoring; for exam- Such protection of important locations includes tasks such as ple, they can plan an attack avoiding existing monitoring all entrances or inbound roads and checking pa trols. Hence, randomized patrolling or monitoring is impor tant, but randomization inbound traffic. However, limited resources imply that it is typ- must provide distinct weights to dif ferent ically impossible to provide full security cover age at all times. actions based on their complex costs and Furthermore, adversaries can observe se curity arrangements benefits. To this end, this article describes a over time and exploit any predictable patterns to their advan- promising transition of the lat est in multia- tage. Randomizing schedules for pa trolling, checking, or moni- gent algorithms into a deployed application. toring is thus an important tool in the police arsenal to avoid In particular, it describes a software assistant the vulnerability that comes with predictability. -
Classical Planning in Deep Latent Space
Journal of Artificial Intelligence Research 1 (2020) 1-15 Submitted 1/1; published 1/1 (Table of Contents is provided for convenience and will be removed in the camera-ready.) Contents 1 Introduction 1 2 Preliminaries and Important Background Concepts 4 2.1 Notations . .4 2.2 Propositional Classical Planning . .4 2.3 Processes Required for Obtaining a Classical Planning Model . .4 2.4 Symbol Grounding . .5 2.5 Autoencoders and Variational Autoencoders . .7 2.6 Discrete Variational Autoencoders . .9 3 Latplan: Overview of the System Architecture 10 4 SAE as a Gumbel-Softmax/Binary-Concrete VAE 13 4.1 The Symbol Stability Problem: Issues Caused by Unstable Propositional Symbols . 15 4.2 Addressing the Systemic Uncertainty: Removing the Run-Time Stochasticity . 17 4.3 Addressing the Statistical Uncertainty: Selecting the Appropriate Prior . 17 5 AMA1: An SAE-based Translation from Image Transitions to Ground Actions 19 6 AMA2: Action Symbol Grounding with an Action Auto-Encoder (AAE) 20 + 7 AMA3/AMA3 : Descriptive Action Model Acquisition with Cube-Space AutoEncoder 21 7.1 Cube-Like Graphs and its Equivalence to STRIPS . 22 7.2 Edge Chromatic Number of Cube-Like Graphs and the Number of Action Schema in STRIPS . 24 7.3 Vanilla Space AutoEncoder . 27 7.3.1 Implementation . 32 7.4 Cube-Space AE (CSAE) . 33 7.5 Back-to-Logit and its Equivalence to STRIPS . 34 7.6 Precondition and Effect Rule Extraction . 35 + arXiv:2107.00110v1 [cs.AI] 30 Jun 2021 8 AMA4 : Learning Preconditions as Effects Backward in Time 36 8.1 Complete State Regression Semantics . -
Discovering Underlying Plans Based on Shallow Models
Discovering Underlying Plans Based on Shallow Models HANKZ HANKUI ZHUO, Sun Yat-Sen University YANTIAN ZHA, Arizona State University SUBBARAO KAMBHAMPATI, Arizona State University XIN TIAN, Sun Yat-Sen University Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or action models in hand. Previous approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing action models to best explain the observed actions, assuming that complete action models are available. In real world applications, however, target plans are often not from plan libraries, and complete action models are often not available, since building complete sets of plans and complete action models are often difficult or expensive. In this paper we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions. DUP explores the EM-style (Expectation Maximization) framework to capture local contexts of actions and discover target plans by optimizing the probability of target plans, while RNNPlanner aims to leverage long-short term contexts of actions based on RNNs (recurrent neural networks) framework to help recognize target plans. In the experiments, we empirically show that our approaches are capable of discovering underlying plans that are not from plan libraries, without requiring action models provided. We demonstrate the effectiveness of our approaches by comparing its performance to traditional plan recognition approaches in three planning domains. -
Learning to Plan from Raw Data in Grid-Based Games
EPiC Series in Computing Volume 55, 2018, Pages 54{67 GCAI-2018. 4th Global Con- ference on Artificial Intelligence Learning to Plan from Raw Data in Grid-based Games Andrea Dittadi, Thomas Bolander, and Ole Winther Technical University of Denmark, Lyngby, Denmark fadit, tobo, [email protected] Abstract An agent that autonomously learns to act in its environment must acquire a model of the domain dynamics. This can be a challenging task, especially in real-world domains, where observations are high-dimensional and noisy. Although in automated planning the dynamics are typically given, there are action schema learning approaches that learn sym- bolic rules (e.g. STRIPS or PDDL) to be used by traditional planners. However, these algorithms rely on logical descriptions of environment observations. In contrast, recent methods in deep reinforcement learning for games learn from pixel observations. However, they typically do not acquire an environment model, but a policy for one-step action selec- tion. Even when a model is learned, it cannot generalize to unseen instances of the training domain. Here we propose a neural network-based method that learns from visual obser- vations an approximate, compact, implicit representation of the domain dynamics, which can be used for planning with standard search algorithms, and generalizes to novel domain instances. The learned model is composed of submodules, each implicitly representing an action schema in the traditional sense. We evaluate our approach on visual versions of the standard domain Sokoban, and show that, by training on one single instance, it learns a transition model that can be successfully used to solve new levels of the game. -
Noam Hazon – CV
Noam Hazon – CV Department of Computer Science 23/1 Yehuda Hanasi st. Ariel University Petah Tiqwa, Israel Ariel, Israel (972)-50-5404321 [email protected] https://www.ariel.ac.il/wp/noam-hazon/ Education Ph.D. 2007-2010 Bar Ilan University, Ramat Gan, Israel Department of Computer Science Research area: Artificial Intelligence, Multi Agent Systems Topic: Social Interaction under Uncertainty in Multi Agent Systems Advisors: Prof. Sarit Kraus and Prof. Yonatan Aumann M.Sc. 2004-2005 Bar Ilan University, Ramat Gan, Israel Department of Computer Science Graduated summa cum laude, with excellence Research area: Multi Robot Systems Topic: Robust and Efficient Multi Robot Coverage Thesis grade: 95 Advisor: Prof. Gal A. Kaminka B.Sc. 2001-2003 Bar Ilan University, Ramat Gan, Israel Department of Computer Science Graduated summa cum laude in Computer Science (extended), with excellence Work Experience Technical Senior Lecturer 2018-present Ariel University, Ariel, Israel Department of Computer Science Lecturer 2014-2017 Ariel University, Ariel, Israel Department of Computer Science Postdoctoral Fellow 2013 Bar Ilan University, Ramat Gan, Israel Department of Computer Science Member of the AIM Lab Hosted by: Prof. Sarit Kraus Postdoctoral Fellow 2011-2012 Carnegie Mellon University, Pittsburgh, Pennsylvania Robotics Institute Member of the Advanced Agent-Robotics Technology Lab Hosted by: Prof. Katia Sycara Software Engineer 2003-2006 Applied Materials Inc. Developing and programming infrastructure components for the inspection machine's operating system, using C++ (Windows) Teaching 2008-2010 Teaching Assistant, Bar Ilan University, Israel a. Introduction to Artificial Intelligence b. Computer structure 2006 Lecturer, The Jerusalem College of Technology, Israel Computer structure Honors, Scholarships and Awards 2020 Blue sky idea award (2nd place) at the AAAI-20 senior member presentation track (sponsored by CCC) 2017 Outstanding lecturer, Ariel University 2011 Finalist for Best Paper award, AAMAS-11 2008 Clore Foundation Scholarship for Ph.D. -
Learning Complex Action Models with Quantifiers and Logical Implications
Artificial Intelligence 174 (2010) 1540–1569 Contents lists available at ScienceDirect Artificial Intelligence www.elsevier.com/locate/artint Learning complex action models with quantifiers and logical implications ∗ Hankz Hankui Zhuo a,b, Qiang Yang b, , Derek Hao Hu b,LeiLia a Department of Computer Science, Sun Yat-sen University, Guangzhou, China, 510275 b Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong article info abstract Article history: Automated planning requires action models described using languages such as the Planning Received 2 January 2010 Domain Definition Language (PDDL) as input, but building action models from scratch is a Received in revised form 4 September 2010 very difficult and time-consuming task, even for experts. This is because it is difficult to Accepted 5 September 2010 formally describe all conditions and changes, reflected in the preconditions and effects of Available online 29 September 2010 action models. In the past, there have been algorithms that can automatically learn simple Keywords: action models from plan traces. However, there are many cases in the real world where Action model learning we need more complicated expressions based on universal and existential quantifiers, Machine learning as well as logical implications in action models to precisely describe the underlying Knowledge engineering mechanisms of the actions. Such complex action models cannot be learned using many Automated planning previous algorithms. In this article, we present a novel algorithm called LAMP (Learning Action Models from Plan traces), to learn action models with quantifiers and logical implications from a set of observed plan traces with only partially observed intermediate state information. -
The Hebrew University of Jerusalem School of Computer Science and Engineering Evaluation Report
Committee for the Evaluation of Computer Science Study Programs The Hebrew University of Jerusalem School of Computer Science and Engineering Evaluation Report November 2014 Contents Chapter 1: Background……………………………………………………………………………...……. 3 Chapter 2: Committee Procedures ………...…………………….………………....................…… 5 Chapter 3: Evaluation of Computer Science Study Program at The Hebrew University of Jerusalem…………….………………………..........…. 6 Chapter 4: General Recommendations and Timetable………………………………….… 15 Appendices: Appendix 1 – Letter of Appointment Appendix 2 – Schedule of the visit Appendix 3- CHE standards for studies in Computer Science 2 Chapter 1: Background The Council for Higher Education (CHE) decided to evaluate study programs in the field of Computer Science during the academic year of 2012-2013. Following the decision of the CHE, the Minister of Education, who serves ex officio as Chairperson of the CHE, appointed a Committee consisting of: Prof. Maurice Herlihy - Computer Science Department, Brown University, USA - Committee Chair Prof. Robert L. Constable - Computer Science Department ,Cornell University, USA1 Prof. David Dobkin - Department of Computer Science, Princeton University, USA2 Prof. Sarit Kraus - Department of Computer Science, Bar Ilan University, Israel3 Prof. Dmitry Feichtner-Kozlov - Department of Mathematics, Bremen University, Germany Prof. Joe Turner, Jr. - (Emeritus) - Department of Computer Science, Clemson University, USA - ABET Representative Prof. Moshe Vardi - Department of Computer Science, Rice University, USA Ms. Maria Levinson-Or served as the Coordinator of the Committee on behalf of the CHE. Within the framework of its activity, the Committee was requested to:4 1. Examine the self-evaluation reports, submitted by the institutions that provide study programs in Computer Science, and to conduct on-site visits at those institutions. 1 In accordance with the CHE's policy, Prof. -
![Arxiv:1803.02208V1 [Cs.AI] 4 Mar 2018 Subbarao Kambhampati Arizona State University E-Mail: Rao@Asu.Edu 2 Hankz Hankui Zhuo Et Al](https://docslib.b-cdn.net/cover/2306/arxiv-1803-02208v1-cs-ai-4-mar-2018-subbarao-kambhampati-arizona-state-university-e-mail-rao-asu-edu-2-hankz-hankui-zhuo-et-al-2322306.webp)
Arxiv:1803.02208V1 [Cs.AI] 4 Mar 2018 Subbarao Kambhampati Arizona State University E-Mail: [email protected] 2 Hankz Hankui Zhuo Et Al
Noname manuscript No. (will be inserted by the editor) Discovering Underlying Plans Based on Shallow Models Hankz Hankui Zhuo · Yantian Zha · Subbarao Kambhampati the date of receipt and acceptance should be inserted later Abstract Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Pre- vious approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by execut- ing domain models to best explain the observed actions, assuming that complete do- main models are available. In real world applications, however, target plans are often not from plan libraries, and complete domain models are often not available, since building complete sets of plans and complete domain models are often difficult or expensive. In this paper we view plan libraries as corpora and learn vector represen- tations of actions using the corpora; we then discover target plans based on the vector representations. Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions. DUP explores the EM-style framework to capture local contexts of actions and discover target plans by optimizing the probability of target plans, while RNNPlanner aims to lever- age long-short term contexts of actions based on RNNs (recurrent neural networks) framework to help recognize target plans. In the experiments, we empirically show that our approaches are capable of discovering underlying plans that are not from plan libraries, without requiring domain models provided. We demonstrate the effective- ness of our approaches by comparing its performance to traditional plan recognition approaches in three planning domains. -
Towards Learning in Probabilistic Action Selection: Markov Systems and Markov Decision Processes
Towards Learning in Probabilistic Action Selection: Markov Systems and Markov Decision Processes Manuela Veloso Carnegie Mellon University Computer Science Department Planning - Fall 2001 Remember the examples on the board. 15-889 – Fall 2001 Veloso, Carnegie Mellon Different Aspects of “Machine Learning” Supervised learning • – Classification - concept learning – Learning from labeled data – Function approximation Unsupervised learning • – Data is not labeled – Data needs to be grouped, clustered – We need distance metric Control and action model learning • – Learning to select actions efficiently – Feedback: goal achievement, failure, reward – Search control learning, reinforcement learning 15-889 – Fall 2001 Veloso, Carnegie Mellon Search Control Learning Improve search efficiency, plan quality • Learn heuristics • (if (and (goal (enjoy weather)) (goal (save energy)) (state (weather fair))) (then (select action RIDE-BIKE))) 15-889 – Fall 2001 Veloso, Carnegie Mellon Learning Opportunities in Planning Learning to improve planning efficiency • Learning the domain model • Learning to improve plan quality • Learning a universal plan • Which action model, which planning algorithm, which heuristic control is the most efficient for a given task? 15-889 – Fall 2001 Veloso, Carnegie Mellon Reinforcement Learning A variety of algorithms to address: • – learning the model – converging to the optimal plan. 15-889 – Fall 2001 Veloso, Carnegie Mellon Discounted Rewards “Reward” today versus future (promised) reward • $100K + $100K + $100K + . • INFINITE! -
Barbara J. Grosz
BARBARA J. GROSZ Higgins Professor of Natural Sciences, Tel. (617) 495-3673; Fax (617) 496-1066 School of Engineering and Applied Sciences Email: [email protected] Harvard University Tel. (617) 495-3673; Fax (617) 496-1066 Cambridge, Massachusetts 02138, USA http://grosz.seas.harvard.edu EDUCATION A.B., Mathematics, Cornell University, 1969 M.A., Computer Science, University of California at Berkeley, 1971 Ph.D., Computer Science, University of California at Berkeley, 1977 PROFESSIONAL EXPERIENCE Harvard University, Higgins Professor of Natural Sciences, 2001-; Dean, Radcliffe Institute for Advanced Study, 2008-2011; Interim Dean, Radcliffe Institute for Advanced Study, 2007-08; Dean of Science, Radcliffe Institute for Advanced Study 2001-2007; Gordon McKay Professor of Computer Science, 1986–2001. SRI International, Artificial Intelligence Center, Sr. Staff Scientist, 1983-86; Program Director, Natural Language and Representation, 1982-83; Senior Computer Scientist, 1981-82; Computer Scientist, 1977-81; Research Mathematician, 1973-77. Center for the Study of Language and Information, SRI International and Stanford University: Co- founder; Executive Committee and Principal Researcher, 1983-86; Advisory Panel, 1986-87. VISITING AND CONSULTING POSITIONS Stanford University, Consulting Associate Professor, Computer Science, 1985-87; Consulting Associate Professor, Computer Science and Linguistics, 1984-85; Visiting Faculty, Department of Computer Science, Fall Quarter, 1982. Brown University, Visiting Professor of Computer Science -
Outline of Machine Learning
Outline of machine learning The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of computer science[1] (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. Contents What type of thing is machine learning? Branches of machine learning Subfields of machine learning Cross-disciplinary fields involving machine learning Applications of machine learning Machine learning hardware Machine learning tools Machine learning frameworks Machine learning libraries Machine learning algorithms Machine learning methods Dimensionality reduction Ensemble learning Meta learning Reinforcement learning Supervised learning Unsupervised learning Semi-supervised learning Deep learning Other machine learning methods and problems Machine learning research History of machine learning Machine learning projects Machine learning organizations Machine learning conferences and workshops Machine learning publications -
From Unlabeled Images to PDDL (And Back)
Classical Planning in Latent Space: From Unlabeled Images to PDDL (and back) Masataro Asai, Alex Fukunaga Graduate School of Arts and Sciences University of Tokyo Abstract Current domain-independent, classical planners re- ? quire symbolic models of the problem domain and Initial state Goal state image Original instance as input, resulting in a knowledge acquisi- image (black/white) Mandrill image tion bottleneck. Meanwhile, although recent work in deep learning has achieved impressive results in Figure 1: An image-based 8-puzzle. many fields, the knowledge is encoded in a subsym- bolic representation which cannot be directly used [ ] by symbolic systems such as planners. We propose Mnih et al., 2015; Graves et al., 2016 . However, the current LatPlan, an integrated architecture combining deep state-of-the-art in pure NN-based systems do not yet provide learning and a classical planner. Given a set of un- guarantees provided by symbolic planning systems, such as labeled training image pairs showing allowed ac- deterministic completeness and solution optimality. tions in the problem domain, and a pair of images Using a NN-based perceptual system to automatically pro- representing the start and goal states, LatPlan uses vide input models for domain-independent planners could a Variational Autoencoder to generate a discrete greatly expand the applicability of planning technology and latent vector from the images, based on which a offer the benefits of both paradigms. We consider the problem PDDL model can be constructed and then solved of robustly, automatically bridging the gap between such sub- by an off-the-shelf planner. We evaluate LatPlan us- symbolic representations and the symbolic representations ing image-based versions of 3 planning domains: required by domain-independent planners.