Showing papers on "Applications of artificial intelligence published in 2012"

Game AI revisited

Abstract: More than a decade after the early research efforts on the use of artificial intelligence (AI) in computer games and the establishment of a new AI domain the term ``game AI'' needs to be redefined. Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation and pathfinding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would significantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a significant potential for the design of better games.This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI field and evidently put the game AI term under a new perspective. These game AI flagship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities.

Artificial Intelligence in Civil Engineering

Abstract: Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

Thinking Inside the Box: Controlling and Using an Oracle AI

Abstract: There is no strong reason to believe that human-level intelligence represents an upper limit of the capacity of artificial intelligence, should it be realized. This poses serious safety issues, since a superintelligent system would have great power to direct the future according to its possibly flawed motivation system. Solving this issue in general has proven to be considerably harder than expected. This paper looks at one particular approach, Oracle AI. An Oracle AI is an AI that does not act in the world except by answering questions. Even this narrow approach presents considerable challenges. In this paper, we analyse and critique various methods of controlling the AI. In general an Oracle AI might be safer than unrestricted AI, but still remains potentially dangerous.

Introducing Variable Importance Tradeoffs into CP-Nets

Abstract: The ability to make decisions and to assess potential courses of action is a corner-stone of many AI applications, and usually this requires explicit information about the decision-maker s preferences. IN many applications, preference elicitation IS a serious bottleneck.The USER either does NOT have the time, the knowledge, OR the expert support required TO specify complex multi - attribute utility functions. IN such cases, a method that IS based ON intuitive, yet expressive, preference statements IS required. IN this paper we suggest the USE OF TCP - nets, an enhancement OF CP - nets, AS a tool FOR representing, AND reasoning about qualitative preference statements.We present AND motivate this framework, define its semantics, AND show how it can be used TO perform constrained optimization.

The role of artificial intelligence in software engineering

Abstract: There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. The work is typified by recent advances in Search Based Software Engineering, but also by long established work in Probabilistic reasoning and machine learning for Software Engineering. This paper explores some of the relationships between these strands of closely related work, arguing that they have much in common and sets out some future challenges in the area of AI for SE.

Safety Engineering for Artificial General Intelligence

Abstract: Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In particular, we challenge the scientific community to develop intelligent systems that have human-friendly values that they provably retain, even under recursive self-improvement.

A Big-Data Perspective on AI: Newton, Merton, and Analytics Intelligence

Abstract: The flood of big data in cyberspace will require immediate actions from the AI and intelligent systems community to address how we manage knowledge. Besides new methods and systems, we need a total knowledge-management approach that willl require a new perspective on AI. We need "Merton's systems" in which machine intelligence and human intelligence work in tandem. This should become a normal mode of operation for the next generation of AI and intelligent systems.

Renewing the respect for similarity

Abstract: In psychology, the concept of similarity has traditionally evoked a mixture of respect, stemming from its ubiquity and intuitive appeal, and concern, due to its dependence on the framing of the problem at hand and on its context. We argue for a renewed focus on similarity as an explanatory concept, by surveying established results and new developments in the theory and methods of similarity-preserving associative lookup and dimensionality reduction — critical components of many cognitive functions, as well as of intelligent data management in computer vision. We focus in particular on the growing family of algorithms that support associative memory by performing hashing that respects local similarity, and on the uses of similarity in representing structured objects and scenes. Insofar as these similarity-based ideas and methods are useful in cognitive modeling and in AI applications, they should be included in the core conceptual toolkit of computational neuroscience.

Contemporary Artificial Intelligence

Abstract: The notion of artificial intelligence (AI) often sparks thoughts of characters from science fiction, such as the Terminator and HAL 9000. While these two artificial entities do not exist, the algorithms of AI have been able to address many real issues, from performing medical diagnoses to navigating difficult terrain to monitoring possible failures of spacecrafts. Exploring these algorithms and applications, Contemporary Artificial Intelligence presents strong AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more. One of the first AI texts accessible to students, the book focuses on the most useful problem-solving strategies that have emerged from AI. In a student-friendly way, the authors cover logic-based methods; probability-based methods; emergent intelligence, including evolutionary computation and swarm intelligence; data-derived logical and probabilistic learning models; and natural language understanding. Through reading this book, students discover the importance of AI techniques in computer science.

Interactive narrative: a novel application of artificial intelligence for computer games

Abstract: Game Artificial Intelligence (Game AI) is a sub-discipline of Artificial Intelligence (AI) and Machine Learning (ML) that explores the ways in which AI and ML can augment player experiences in computer games. Storytelling is an integral part of many modern computer games; within games stories create context, motivate the player, and move the action forward. Interactive Narrative is the use of AI to create and manage stories within games, creating the perception that the player is a character in a dynamically unfolding and responsive story. This paper introduces Game AI and focuses on the open research problems of Interactive Narrative.

Teaching game AI through Minecraft mods

Abstract: One of the issues with teaching artificial intelligence (AI) for games is that many AI algorithms work in theory, but have production consequences in terms of speed or memory when actually used in a game. We report on the benefits and drawbacks of modifying or “modding” the commercial game Minecraft for a course on Game AI. This was done to give students the experience of dealing with a commercial game environment where they would have to worry about production consequences with their algorithms. The course was run as an upper level undergraduate elective during the fall of 2011 and included assignments on dynamic terrain generation, character behavior, and world events.

Bio-inspired artificial intelligence

Abstract: After its fall somewhere in the ′80s and the subsequent advent of the Internet in the early ′90s brought about the resurgence of AI in the soft world. About the same time AI was blended with a specifically different flavor, a flavor that was inspired by Mother Nature. This came to be known as Bio-inspired AI. This transition from classical to biologically inspired AI was mostly because early researchers always imagined that their so called intelligent algorithms formulated using classical techniques could easily be bolted on to make-believe general purpose robots that never ever existed then and even now.

AI Reloaded: Objectives, Potentials, and Challenges of the Novel Field of Brain-Like Artificial Intelligence

Abstract: The general objective of Artificial Intelligence (AI) is to make machines – particularly computers – do things that require intelligence when done by humans. In the last 60 years, AI has significantly progressed and today forms an important part of industry and technology. However, despite the many successes, fundamental questions concerning the creation of human-level intelligence in machines still remain open and will probably not be answerable when continuing on the current, mainly mathematic-algorithmically-guided path of AI. With the novel discipline of Brain-Like Artificial Intelligence, one potential way out of this dilemma has been suggested. Brain-Like AI aims at analyzing and deciphering the working mechanisms of the brain and translating this knowledge into implementable AI architectures with the objective to develop in this way more efficient, flexible, and capable technical systems This article aims at giving a review about this young and still heterogeneous and dynamic research field.

Case Acquisition Strategies for Case-Based Reasoning in Real-Time Strategy Games.

Abstract: Real-time Strategy (RTS) games are complex domains which are a significant challenge to both human and artificial intelligence (AI). For that reason, and although many AI approaches have been proposed for the RTS game AI problem, the AI of all commercial RTS games is scripted and offers a very static behavior subject to exploits. In this paper, we will focus on a case-based reasoning (CBR) approach to this problem, and concentrate on the process of case-acquisition. Specifically, we will describe 7 different techniques to automatically acquire plans by observing human demonstrations and compare their performance when using them in the Darmok 2 system in the context of an RTS game.

Philosophy and Theory of Artificial Intelligence

Abstract: Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here.

SCAIL: An integrated Starcraft AI system

Abstract: We present the work on our integrated AI system SCAIL, which is capable of playing a full round of the Real-Time Strategy game Starcraft. Our system makes use of modern AI techniques such as particle filtering, on-line machine learning, drive-based motivation systems and artificial emotions, used to find novel structure in the dynamic playing environment, which is exploited by both high and low-level control systems. We employ a principled architecture, capable of expressing high level goal-directed behaviour. We provide an overview of our system, and a comparative evaluation against the in-game AIs of Starcraft, as well as thirteen third party systems. We go on to detail how the techniques and tools we introduce provide advantages to our system over the current state-of-the-art, resulting in improved performance when competing against those systems.

Foundations of Knowledge Base Management: Contributions from Logic, Databases, and Artificial Intelligence Applications

Abstract: This book is based on material from current research projects and cooperations and from a recent workshop in the area of Knowledge Base Management Systems. It contains 25 revised papers and related discussions that concentrate on the integration of Database Technology (deductive databases, extended relational technology, object-oriented systems) and Artificial Intelligence (in particular logic programming and knowledge representation). The emphasis of the book is on the integration of DB/AI technology required for knowledge Base Management Systems. The book isolates major conceptual contributions, systems extensions, and reseach directions that lead towards that goal. This book is a European counterpart to another volume in the Topics in Information Systems Series, "On Knowledge Base Management Systems", resulting from a North American workshop and edited by M. Brodie and J. Mylopoulos, which concentrates on theoretical results and the more abstract levels of Knowledge Base Management.

Artificial Intelligence Applications to Traffic Engineering

Abstract: In recent years the applications of advanced information technologies in the field of transportation have affected both road infrastructures and vehicle technologies The development of advanced transport telematics systems and the implementation of a new generation of technological options in the transport environment have had a significant impact on improved traffic management, efficiency and safety This volume contains contributions from scientific and academic centres which have been active in this field of research and provides an overview of applications of AI technology in the field of traffic control and management The topics covered are: -- current status of AI in transport -- AI applications in traffic engineering -- in-vehicle AI

Integer sparse distributed memory and modular composite representation

Abstract: Challenging AI applications, such as cognitive architectures, natural language understanding, and visual object recognition share some basic operations including pattern recognition, sequence learning, clustering, and association of related data. Both the representations used and the structure of a system significantly influence which tasks and problems are most readily supported. A memory model and a representation that facilitate these basic tasks would greatly improve the performance of these challenging AI applications. Sparse Distributed Memory (SDM), based on large binary vectors, has several desirable properties: auto-associativity, content addressability, distributed storage, robustness over noisy inputs that would facilitate the implementation of challenging AI applications. Here I introduce two variations on the original SDM, the Extended SDM and the Integer SDM, that significantly improve these desirable properties, as well as a new form of reduced description representation named MCR. Extended SDM, which uses word vectors of larger size than address vectors, enhances its hetero-associativity, improving the storage of sequences of vectors, as well as of other data structures. A novel sequence learning mechanism is introduced, and several experiments demonstrate the capacity and sequence learning capability of this memory. Integer SDM uses modular integer vectors rather than binary vectors, improving the representation capabilities of the memory and its noise robustness. Several experiments show its capacity and noise robustness. Theoretical analyses of its capacity and fidelity are also presented. A reduced description represents a whole hierarchy using a single high-dimensional vector, which can recover individual items and directly be used for complex calculations and procedures, such as making analogies. Furthermore, the hierarchy can be reconstructed from the single vector. Modular Composite Representation (MCR), a new reduced description model for the representation used in challenging AI applications, provides an attractive tradeoff between expressiveness and simplicity of operations. A theoretical analysis of its noise robustness, several experiments, and comparisons with similar models are presented. My implementations of these memories include an object oriented version using a RAM cache, a version for distributed and multi-threading execution, and a GPU version for fast vector processing.

Computer-Aided Engineering for Manufacture

Abstract: 1 Introduction.- Evolution of computer aided manufacture (CAM).- Automation and CAM.- 2 Numerical control.- History of numerical control.- The conventional numerical control concept.- programming.- Real-time control of machine tools.- 3 Computer technology.- Computer assisted programming.- Automatic programmed tooling (APT).- Adaptive control.- Machinability data banks.- 4 Communications networking.- Conventional ways of connecting terminals.- Uses of computer networks.- Local area networks (LANs).- Wide area networks (WANs).- Network architecture: protocols and standards.- Data transmission.- 5 Computer process control monitoring.- Information provided by monitoring.- Supervisory computer control.- Communications networks for factory monitoring.- Programmable logic controllers (PLCs).- Input/output interfaces.- Shop floor information systems.- 6 The integration of CAD and CAM.- The evolution of CADCAM.- The concept of integration.- Fundamentals of CAD.- CAM software.- CADCAM database.- Where does CADCAM improve productivity?.- Defining CADCAM project objectives.- Procedures to be followed in a CADCAM implementation.- 7 Robotics technology and applications.- Definition of an industrial robot.- Basic components of industrial robots.- Robot performance characteristics.- Commercial robots.- Future developments.- Problem areas.- Future trends.- 8 Flexible manufacturing systems.- The growth of flexible processing and handling.- FMS characteristics.- Flexibility.- Computer control functions.- Material handling in the FMS.- FMS-GT connection.- Prospects for FMS in UK industry.- 9 Computer aided production management.- Objectives of CAPM.- Functions of CAPM.- Stock recording and control.- Material requirements planning (MRP).- Capacity requirements planning (CRP).- Process planning.- CAPM package systems.- Cost estimation and financial justification of CAPM.- The effects of CAPM on the UK manufacturing industry.- Computer integrated manufacturing.- 10 Artificial intelligence in manufacturing.- Fifth generation computer systems.- Expert systems.- Expert system languages.- Problem solving and planning.- Diagnostic problems.- Application of AI in engineering design and manufacture.- Application of AI in computer aided process planning.- Limitations of existing AI applications.- Management guidelines towards AI implementation.- The future for artificial intelligence.- Research directions for AI in manufacturing.- References and Bibliography.

A brief overview of artificial intelligence in South Africa

Abstract: One of the consequences of the growth in AI research in South Africa in recent years is the establishment of a number of research hubs involved in AI activities ranging from mobile robotics and computational intelligence, to knowledge representation and reasoning, and human language technologies. In this survey we take the reader through a quick tour of the research being conducted at these hubs, and touch on an initiative to maintain and extend the current level of interest in AI research in the country.

Philosophers & Futurists, Catch Up! Response to The Singularity

Abstract: Responding to Chalmers' The Singularity (2010), I argue that progress towards self-improving AIs is already substantially beyond what many futurists and philosophers are aware of. Instead of rehashing well-trodden topics of the previous millennium, let us start focusing on relevant new millennium results. All indented paragraphs of this paper are quotes taken from Chalmers' paper of 2010, who mentions Good's informal speculations (1965) on ultraintelligent self-improving machines: The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. Chalmers speculates that some sort of meta-evolution could be used to build more and more intelligent machines called AI, AI+, AI++...: The process of evolution might count as an indirect example: less intel- ligent systems have the capacity to create more intelligent systems by reproduction, variation and natural selection. This version would then come to the same thing as an evolutionary path to AI and AI++. (...) If we produce an AI by machine learning, it is likely that soon after we will be able to improve the learning algorithm and extend the learning pro- cess, leading to AI+. If we produce an AI by artificial evolution, it is

Computational intelligence for human-friendly robot partners based on multi-modal communication

Abstract: This paper discusses the multi-modal communication for robot partners based on computational intelligence in informationally structured space. First, we explain recognition methods of touch interface, voice recognition, human detection, gesture recognition used in the multi-modal communication. Furthermore, we propose a conversation system to realize the multi-modal communication with a person. Finally, we show several experimental results of the proposed method, and discuss the future direction on this research.

Control of Efficient Intelligent Robotic Gripper Using Fuzzy Inference System

Abstract: In the last few years the applications of artificial intelligence techniques have been used to convert human experience into a form understandable by computers. Advanced control based on artificial intelligence techniques is called intelligent control. Intelligent systems are usually described by analogies with biological systems by, for example, looking at how human beings perform control tasks, recognize patterns, or make decisions. Fuzzy logic is a way to make machines more intelligent enabling them to reason in a fuzzy manner like humans. Fuzzy logic, proposed by Lotfy Zadeh in 1965, emerged as a tool to deal with uncertain, imprecise, or qualitative decision-making problems (Zadeh, 1965).

Real-Time Adaptive GPU Multiagent Path Planning

Abstract: Publisher Summary This chapter presents a graphics processing units (GPU) path planning algorithm that is derived from the sequential A* algorithm to allow massively parallel, real-time execution. Many types of computer games involve player and nonplayer characters moving over terrain. In real-time strategy games the player doesn't control any characters directly. Instead, the player selects a group of characters (or agents) and then selects a target position with the mouse. The target position can be in a known position or an unknown position. The agents then have to find their own way to the target position. If they observe that their current trajectory is blocked after they have started moving, they have to search for another path. A* is the most famous algorithm for finding cost-minimal paths in state spaces, which are usually represented as graphs. The search performed by A* is ideal for off-line artificial intelligence applications, but it is not suitable for computer games where agents have to search paths in real time. Real-Time Adaptive A* is a real-time heuristic search method that chooses its local search spaces in a very fine-grained way. The main idea is to update the heuristics of all states in the local search space very quickly and to save the heuristics to speed up future A* searches. This approach uses a variable called look ahead, which specifies the largest number of states to expand during an A* search. The key aim in designing and implementing a RTAA* multiagent path plan in the GPU is to reduce the memory required for all states.

Difference Between Artificial Intelligence and Traditional Methods

Abstract: The domain of artificial intelligence (AI) is wide. Originally AI was defined as the discipline of computers that show intelligent human behavior. Now AI also refers to computer systems that show complex behavior similar to living systems like swarms, ant colonies, microbiology, and neural systems. There is a distinction between “‘strong” AI—computer functions that really have strong similarities with intelligent human reasoning and show some kind of self awareness—and “weak” AI—computer applications that deal with limited application areas and contain some practical knowledge and seem to have some intelligent features, such as expert systems and heuristic search algorithms. The focus of engineering applications of AI such as the ones considered in this circular is more on weak AI. However, it should be noted that the boundaries between strong and weak AI are not sharp and often AI systems move from one type to the other. A general distinction between “ordinary” computer systems and AI is the complexity of the AI computer systems.

ECAI 2012 : 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France : including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track : proceedings

Abstract: Artificial intelligence (AI) plays a vital part in the continued development of computer science and informatics. The AI applications employed in fields such as medicine, economics, linguistics, philosophy, psychology and logical analysis, not forgetting industry, are now indispensable for the effective functioning of a multitude of systems. This book presents the papers from the 20th biennial European Conference on Artificial Intelligence, ECAI 2012, held in Montpellier, France, in August 2012. The ECAI conference remains Europe's principal opportunity for researchers and practitioners of Artificial Intelligence to gather and to discuss the latest trends and challenges in all subfields of AI, as well as to demonstrate innovative applications and uses of advanced AI technology. ECAI 2012 featured four keynote speakers, an extensive workshop program, seven invited tutorials and the new Frontiers of Artificial Intelligence track, in which six invited speakers delivered perspective talks on particularly interesting new research results, directions and trends in Artificial Intelligence or in one of its related fields. The proceedings of PAIS 2012 and the System Demonstrations Track are also included in this volume, which will be of interest to all those wishing to keep abreast of the latest developments in the field of AI.

Differential Protection of Generator by Using Neural Network, Fuzzy Neural and Fuzzy Neural Petri Net

Abstract: This paper deals with the applications of Artificial Intelligence techniques for detecting internal faults in Power generators. Three techniques are used which are Neural Net (NN), Fuzzy Neural Net (FNN) and Fuzzy Neural Petri Net (FNPN) to implement differential protection of generator. MATLAB toolbox has been used for simulations and generation of faults data for training the programs for different faults cases and to implement the relays. Results of different fault cases are presented and these results are compared among the three implemented techniques of relays and with the conventional differential relay of generator. Keywords: Differential Protection, Generator Internal Faults, Neural Net, Fuzzy Neural and Fuzzy Neural Petri Net. 1. INTRODUCTION Synchronous generator is the most important element of power system. Generators do experience short circuits and abnormal electrical conditions. In many cases, equipment damage due to these events can be reduced or prevented by proper generator protection. Generators need to protect from abnormal conditions, when subjected to these conditions, damage or complete failure can occur within seconds, thus requiring automatic detection and tripping. All faults associated with synchronous generators may be classified as either insulation failures or abnormal running conditions [1, 2]. An insulation failure in the stator winding will result in an inter-turn fault, a phase fault or a ground fault, etc. At present the generators are protected against almost all kind of faults using differential methods of protection. Differential relays, in particular the digital ones, are used to detect stator faults of generators. Electric power utilities and industrial plants use electromechanical and solid-state relays for protecting synchronous generators [3]. With the advent of digital technology have made significant progress in developing protection systems based on digital techniques [4,5]. Protection relaying is just as much a candidate for application of pattern recognition. The majority of power system protection techniques are involved in defining the system state through identifying the pattern of current waveforms measured at the relay location. This means that the development of adaptive protection can be essentially treated as a problem of pattern recognition. Artificial Intelligences (AIs) are powerful in pattern recognition and classification. They possess excellent features such as generalization capability, noise immunity, robustness and fault tolerance. AI-based techniques have been used in power system protection and encouraging results are obtained [6, 7]. Artificial neural network is a kind of network structure based on modern biology nervous system research, which shows great application potential on equipment diagnosis by its capabilities of parallel distributed processing, associative memory and self learning. Through learning on multiple types of fault samples, a single NN can memorize characteristics of such faults, thus a single NN can