The 2014 General Video Game Playing Competition

This paper presents the framework, rules, games, controllers, and results of the first General Video Game Playing Competition, held at the IEEE Conference on Computational Intelligence and Games in 2014. The competition proposes the challenge of creating controllers for general video game play, where a single agent must be able to play many different games, some of them unknown to the participants at the time of submitting their entries. This test can be seen as an approximation of general artificial intelligence, as the amount of game-dependent heuristics needs to be severely limited. The games employed are stochastic real-time scenarios (where the time budget to provide the next action is measured in milliseconds) with different winning conditions, scoring mechanisms, sprite types, and available actions for the player. It is a responsibility of the agents to discover the mechanics of each game, the requirements to obtain a high score and the requisites to finally achieve victory. This paper describes all controllers submitted to the competition, with an in-depth description of four of them by their authors, including the winner and the runner-up entries of the contest. The paper also analyzes the performance of the different approaches submitted, and finally proposes future tracks for the competition

Discovering qualitative empirical laws

In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition

Assess Intermediate Force Capabilities (IFC) and concept of operations for application during the Competition Phase in an environment of GPC

NPS NRP Technical ReportIntermediate force capabilities represent a strategic risk mitigation investment that are designed to provide warfighters tools to compete below the level of major armed conflict without losing credibility in the information. This research examines the effects of a set of intermediate force capabilities to assess the strategic impact on a near peer adversary during the competition phase in a 'gray zone' scenario. The effort will seek to gain insights and identify challenges to the employment of IFCs, through several venues, including leveraging defense analysis and operations research department faculty and students and utilizing the NPS warfighting continuum of Joint Campaign Analysis and Wargaming Applications courses.HQMC Plans, Policies & Operations (PP&O)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

EIES 2 : a distributed architecture for supporting group work

The Computerized Conferencing Center (CCCC) at New Jersey Institute of Technology (NJIT) has been researching on-line group communications for 17 years by developing and studying tools to advance the collective intelligence . The Electronic Information Exchange System 2 (EIES2) provides a research, development and operational environment for distributed computer supported cooperative work (CSCW) systems. The EIES 2 distributed Smalltalk processor provides for rapid prototyping and implementation of muti-media CSCW facilities in the network environment. The Smalltalk support of the object model, and meta-language properties make it ideally suited for incremental development CSCW applications. The EIES2 communication environment supports a decentralized network architecture. Modern standards are used in the implementation of data structures, communication interfaces and database. The EIES2 application layer protocols support use ASN.1 data representation to access to an object-oriented distributed database via X.ROS remote operation services. EIES2 can serve as a foundation on which group work systems may be built and defines protocols that can allow them to inter-operate. An initial system presents a powerful metaphor of conferences and activities which provides an extensible framework upon which to add group work applications. Work to date has provided structures for information exchange, inquiry networking, information filtering, the on-line virtual classroom, and group decision support. This paper presents the system architecture model used for EIES2 and describes the implementation and current applications

Solving and Verifying the Boolean Pythagorean Triples Problem via Cube-and-Conquer

We solved a long-outstanding open problem in Ramsey theory, using SAT solving

Using phase space attractors to evaluate system safety constraint enforcement : case study in space shuttle mission control procedure rework

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Vita. Cataloged from PDF version of thesis.Includes bibliographical references (p. 390-409).As the complexity and influence of engineering systems in modern society increases, so too does their potential to create counterintuitive and catastrophic accidents. Increasingly, the accidents encountered in these systems are defying the linearized notions of accident causality that-though developed for the simpler engineered systems of the past-are prevalently used for accident prevention today. In this dissertation, an alternative approach to accident prevention based on systems theory-the Systems-Theoretic Accident Model and Processes (STAMP) and STAMP-based hazard analysis (STPA)-is augmented with the notion of using phase space attractors to evaluate how well STAMP safety control structures enforce system safety constraints. Phase space attractors are mathematical results that emerge from the behavior of systems with dynamic structures that draw or constrain these systems to specific regions of their phase space in spite of a range of conditions. Accordingly, the goal in using this notion for the evaluation of safety constraint enforcement is to identify and analyze the attractors produced by a safety control structure to determine if it will adequately "attract" the system to safe states in spite of a range of unforeseeable conditions. Support for this approach to evaluating STAMP safety control structures is provided through the study of a safety control structure in an existing complex, socio-technical system. This case study is focused on a safety control process-referred to as Procedure Rework-used in Space Shuttle Mission Control to update procedures during in-flight operations as they are invalidated by changes in the state of the Space Shuttle and its environment.(cont.) Simulation models of procedure rework are developed through physical and human factors principles and calibrated with data from five Space Shuttle missions; producing simulation results with deviations from the historical data that are-as characterized by Theil Inequality Statistics-small and primarily due to cycles and noise that are not relevant to the models' purpose. The models are used to analyze the attractor produced by the Procedure Rework Process across varied conditions, including a notional crewed spacecraft mission to a distant celestial body. A detrimental effect in the process is identified-and shown to be potentially far more severe than light delay on a mission to a distant celestial body-and approaches to mitigating the effect are explored. Finally, the analysis conducted is described as a generalizeable process for using phase space attractors to evaluate system safety constraint enforcement in engineering systems.by Brandon D. Owens.Ph.D

Creating Network Attack Priority Lists by Analyzing Email Traffic Using Predefined Profiles

Networks can be vast and complicated entities consisting of both servers and workstations that contain information sought by attackers. Searching for specific data in a large network can be a time consuming process. Vast amounts of data either passes through or is stored by various servers on the network. However, intermediate work products are often kept solely on workstations. Potential high value targets can be passively identified by comparing user email traffic against predefined profiles. This method provides a potentially smaller footprint on target systems, less human interaction, and increased efficiency of attackers. Collecting user email traffic and comparing each word in an email to a predefined profile, or a list of key words of interest to the attacker, can provide a prioritized list of systems containing the most relevant information. This research uses two experiments. The functionality experiment uses randomly generated emails and profiles, demonstrating MAPS (Merritt\u27s Adaptive Profiling System)ability to accurately identify matches. The utility experiment uses an email corpus and meaningful profiles, further demonstrating MAPS ability to accurately identify matches with non-random input. A meaningful profile is a list of words bearing a semantic relationship to a topic of interest to the attacker. Results for the functionality experiment show MAPS can parse randomly generated emails and identify matches with an accuracy of 99 percent or above. The utility experiment using an email corpus with meaningful profiles, shows slightly lower accuracies of 95 percent or above. Based upon the match results, network attack priority lists are generated. A network attack priority list is an ordered list of systems, where the potentially highest value systems exhibit the greatest fit to the profile. An attacker then uses the list when searching for target information on the network to prioritize the systems most likely to contain useful data

Automated HW/SW co-design for edge AI : State, challenges and steps ahead

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world using edge sensors and actuators. For IoT systems, there is now a strong trend to move the intelligence from the cloud to the edge or the extreme edge (known as TinyML). Yet, this shift to edge AI systems requires to design powerful machine learning systems under very strict resource constraints. This poses a difficult design task that needs to take the complete system stack from machine learning algorithm, to model optimization and compression, to software implementation, to hardware platform and ML accelerator design into account. This paper discusses the open research challenges to achieve such a holistic Design Space Exploration for a HW/SW Co-design for Edge AI Systems and discusses the current state with three currently developed flows: one design flow for systems with tightly-coupled accelerator architectures based on RISC-V, one approach using loosely-coupled, application-specific accelerators as well as one framework that integrates software and hardware optimization techniques to built efficient Deep Neural Network (DNN) systems.publishedVersionPeer reviewe