Showing papers on "Applications of artificial intelligence published in 2014"

The Cambridge Handbook of Artificial Intelligence

Abstract: Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. AI applications are transforming the way we interact with each other and with our environment, and work in artificially modeling intelligence is offering new insights into the human mind and revealing new forms mentality can take. This volume of original essays presents the state of the art in AI, surveying the foundations of the discipline, major theories of mental architecture, the principal areas of research, and extensions of AI such as artificial life. With a focus on theory rather than technical and applied issues, the volume will be valuable not only to people working in AI, but also to those in other disciplines wanting an authoritative and up-to-date introduction to the field.

A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems

Abstract: Reconfigurable manufacturing systems (RMS) are considered the future of manufacturing, being able to overcome both dedicated (DMS) and flexible manufacturing systems (FMS). In fact, they provide significant cost and time reductions in the launch of new products, and in the integration of new manufacturing processes into existing systems. The goals of RMS design are the extension of the production variety, the adaption to rapid changes in the market demand, and the minimization of the investment costs. Despite the interest of many authors, the debate on RMS is still open due to the lack of practical applications. This work is a review of the state-of-the-art on the design of cellular RMS, compared to DMS, by means of optimization. The problem addressed belongs to the NP-Hard family of combinatorial problem. The focus is on non-exact meta-heuristic and artificial intelligence methods, since these have been proven to be effective and robust in solving complex manufacturing design problems. A wide investigation on the most recurrent techniques in DMS and RMS literature is performed at first. A critical analysis over these techniques is given in the end.

NaturalLI: Natural Logic Inference for Common Sense Reasoning

Abstract: Common-sense reasoning is important for AI applications, both in NLP and many vision and robotics tasks. We propose NaturalLI: a Natural Logic inference system for inferring common sense facts ‐ for instance, that cats have tails or tomatoes are round ‐ from a very large database of known facts. In addition to being able to provide strictly valid derivations, the system is also able to produce derivations which are only likely valid, accompanied by an associated confidence. We both show that our system is able to capture strict Natural Logic inferences on the FraCaS test suite, and demonstrate its ability to predict common sense facts with 49% recall and 91% precision.

A Review of Real-Time Strategy Game AI

Abstract: This literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the indus- try aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either in- applicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investi- gation: bridging the gap between academia and industry. Fi- nally, the areas of spatial reasoning, multi-scale AI, and co- operation are found to require future work, and standardised evaluation methods are proposed to produce comparable re- sults between studies.

Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review

Abstract: Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.

Artificial Intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters

Abstract: Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA-CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.

The path to more general artificial intelligence

Abstract: A small-N comparative analysis of six different areas of applied artificial intelligence (AI) suggests that the next period of development will require a merging of narrow-AI and strong-AI approaches. This will be necessary as programmers seek to move beyond developing narrowly defined tools to developing software agents capable of acting independently in complex environments. The present stage of artificial intelligence development is propitious for this because of the exponential increases in computer power and in available data streams over the last 25 years, and because of better understanding of the complex logic of intelligence. Applied areas chosen for examination were heart pacemakers, socialist economic planning, computer-based trading, self-driving automobiles, surveillance and sousveillance and artificial intelligence in medicine.

Chapter 11 – Designing and Modeling Solar Energy Systems

Abstract: Chapter 11 presents methods used for the design and modeling of solar systems. These include the f-chart method and program and comprise sections on the design of liquid-based solar heating systems, various corrections required for the storage capacity, collector flow rate and load heat exchanger size; the design of air-based solar heating systems, various corrections required for the pebble-bed storage size and air flow-rate correction; the design of solar service-water systems, and the use of the f-chart for the design of thermosiphon solar water-heating systems. The section concludes with some general remarks and a brief overview of the f -chart program. This is followed by the utilizability method and examines the hourly utilizability, daily utilizability, and the design of active systems using both the hourly and the daily utilizability methods. Subsequently the Φ¯, f-chart method is presented and include the corrections for the storage tank losses and heat exchanger correction, followed by the unutilizability method, which include direct gain systems, collector storage walls, and active collection with passive storage systems. The chapter includes also a description of the various programs that can be used for the modeling and simulation of solar systems and include the TRNSYS, WATSUN, and Polysun simulation programs. This is followed by a short description of the artificial intelligence techniques used in renewable energy systems modeling, performance prediction, and control. It includes a description of the artificial neural networks and comprises sections on biological and artificial neurons, artificial neural network principles, network parameters selection, and a description of the basic neural network architectures, like the back-propagation, the general regression neural network, and the group method data handling neural network. Then genetic algorithms are described as well as their applications in solar energy systems, followed by a short description of GenOpt and TRNopt programs. Subsequently, fuzzy logic is examined and includes membership functions, logical operations, if-then rules, fuzzy inference systems, and fuzzy systems applications in solar energy systems. Finally, hybrid systems are described, which employ more than one artificial intelligence applications. The chapter concludes with an analysis of the limitations of simulations.

The role of artificial intelligence in robotics

Abstract: – This paper aims to provide an insight into the use of artificial intelligence (AI) in robotics. , – Following an introduction to AI, this paper provides an overview of the application of AI to robotics. Mobile robots are then discussed, together with the various AI techniques employed and under development. The application of the OpenCog artificial general intelligence architecture is then considered and the paper concludes with a brief discussion. , – This shows that many AI concepts are being applied to humanoid, mobile and other classes of robots. Significant progress has been made and many innovative AI strategies are being studied which often seek to emulate aspects of human intelligence. Much development activity is being driven by military interests but as yet, the level of intelligence exhibited by the most advanced robots is at best equivalent to that of a very young child. Several academics argue that more rapid progress will arise from a closer integration of AI and robotic research. , – This article discusses the role of AI in robotics and provides details of number of robotic developments involving a range of AI concepts.

Artificial Intelligence: From programs to solvers

Abstract: Artificial Intelligence is a brain child of Alan Turing and his universal programmable computer. During the 60’s and 70’s, AI researchers used computers for exploring intuitions about intelligence and for writing programs displaying intelligent behavior. A significant change occurred however in the 80’s, as many AI researchers moved from the early AI paradigm of writing programs for ill-defined problems to writing solvers for well-defined mathematical models like Constraint Satisfaction Problems, Strips Planning, SAT, Bayesian Networks, Partially Observable Markov Decision Processes, and General Game Playing. Solvers are programs that take a compact description of a particular model instance and automatically compute its solution. Unlike the early AI programs, solvers are general as they must deal with any instance that fits the model. Many ideas have been advanced to address this crisp computational challenge from which a number of lessons can be drawn. In this paper, I revisit the problem of generality in AI, look at the way in which this ’Models and Solvers’ agenda addresses the problem, and discuss the relevance of this agenda to the grand AI goal of a computational account of intelligence and human cognition.

A novel approach to integrate artificial potential field and fuzzy logic into a common framework for robots autonomous navigation

Abstract: Artificial potential field and fuzzy logic are efficient approaches for mobile robots autonomous navigation. However, both have advantages and drawbacks. Their integration into a common control scheme can significantly improve the performances of the resulting hybrid controller. In this article, we propose a novel hybrid approach in order to better exploit their advantages. The present work contributes in three aspects: first, the proposed control scheme integrates interval type-2 fuzzy logic concepts with artificial potential field concepts into a common framework in order to better exploit their advantages. Second, the proposed control scheme is a simple and realizable design for real-time implementation because only 15 fuzzy rules are sufficient to control the mobile robot. Third, the proposed control scheme is a synthesized design which utilizes both heuristic knowledge and the sampled input–output data pairs. An implementation in real-time on an omnidirectional mobile robot validates the effectivenes...

Deduction of fighting-game countermeasures using the k-nearest neighbor algorithm and a game simulator

Abstract: This paper proposes an artificial intelligence algorithm that uses the k-nearest neighbor algorithm to predict its opponent's attack action and a game simulator to deduce a countermeasure action for controlling an in-game character in a fighting game. This AI algorithm (AI) aims at achieving good results in the fighting-game AI competition having been organized by our laboratory since 2013. It is also a sample AI, called MizunoAI, publicly available for the 2014 competition at CIG 2014. In fighting games, every action is either advantageous or disadvantageous against another. By predicting its opponent's next action, our AI can devise a countermeasure which is advantageous against that action, leading to higher scores in the game. The effectiveness of the proposed AI is confirmed by the results of matches against the top-three AI entries of the 2013 competition.

Artificial Intelligence Applications in Distance Education

Abstract: As a result of the rise of modern knowledge society, it has been highly required to have newer approaches and innovations in the sense of educational processes. Because of this, different methods and technologies have appeared to make the expressed requirements real. At this point, information and communication technologies have had a great role in remarkable improvements. From this perspective, the Distance Education approach and its related techniques like ELearning, M-Learning...etc. are popular and strong elements for today’s world. In addition to the related effectiveness of the E-Learning, researchers also perform more scientific studies to support E-Learning or improve its functions and features to provide better conditions within the learning and teaching process. Today, one of the most remarkable improvement ways is using Artificial Intelligence techniques to support classical Distance Education approaches-techniques or develop newer ones to continue development of the subject area.

Technologies and Applications of Artificial Intelligence

Abstract: A method for estimating age and gender using multiple local patches is proposed in this thesis. We use the histogram of rotation-invariant local binary pattern as our features to train the SVM model. We further introduce the shifting and scaling of the local patches to enhance the accuracy of the estimation. Our proposed method not only provides accurate results but also can be incorporated with other methods to further improve their accuracy.

Space Applications of Artificial Intelligence

Abstract: We are pleased to introduce the space application issue articles in this issue of AI Magazine. The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations.

Use of Neural Networks and Artificial Intelligence Tools for Modeling, Characterization, and Forecasting in Material Engineering

Abstract: This chapter presents the theoretical basis concerning the broad possibilities offered by the contemporary applications of artificial intelligence tools, especially artificial neural networks in the field of material engineering. The examples of own research pursued at the Institute of Engineering Materials and Biomaterials of the Silesian University of Technology, including the modeling and simulation of different properties of engineering materials, are presented. Discussed separately is a pioneering project of implementing artificial neural networks in order to predict the development trends of materials surface engineering.

Learning to play fighting game using massive play data

Abstract: Designing fighting game AI has been a challenging problem because the program should react in real-time and require expert knowledge on the combination of actions. In fact, most of entries in 2013 fighting game AI competition were based on expert rules. In this paper, we propose an automatic policy learning method for the fighting game AI bot. In the training stage, the AI continuously plays fighting games against 12 bots (10 from 2013 competition entries and 2 examples) and stores massive play data (about 10 GB). UCB1 is used to collect the data actively. In the testing stage, the agent searches for the similar situations from the logs and selects skills with the highest rewards. In this way, it is possible to construct the fighting game AI with minimum expert knowledge. Experimental results show that the learned agent can defeat two example bots and show comparable performance against the winner of 2013 competition.

Game design for classical AI

Abstract: Reasoning using expressive symbolic representations is a central theme of AI research, yet there are surprisingly few deployed games, even within the AIIDE research community, that use this sort of “classical” AI. This is partly due to practical and methodological issues, but also due to fundamental mismatches between current game genres and classical AI systems. I will argue that if we want to build games that leverage high-end classical AI techniques like commonsense reasoning and natural language processing, we will also have to develop new game genres and mechanics that better exploit those capabilities. I will also present a design sketch of a game that explores potential game mechanics for classical AI.

Research on Artificial Intelligence Technology of Electrical Automation Control

Abstract: Progress of society and human longevity require more developed productive forces and more intelligent human economic life in order to save precious human time. Innovation in the field of electrical automation control needs artificial human support, and artificial intelligence advantage in terms of automation and control in this area are indeed able to get great play. This paper describes the connotation and characteristics of artificial intelligence technology. As well as the status of electrical automation control artificial intelligence technology. The applications of artificial intelligence technology are analyzed and discussed in the electric automation industry. Electrical automated production widely uses artificial intelligence technology to improve the productivity of electronic devices, reduce the harsh production costs, and improve production efficiency and competitiveness, which is an important guarantee for long-term development. The applications of artificial intelligence technology promote the development and progress of automation and solve difficult control problems.

Why Has Artificial Intelligence Failed? And How Can it Succeed?

Abstract: In the 1960s, pioneers in artificial intelligence made grand claims that AI systems would surpass human intelligence before the end of the 20th century. Except for beating the world chess champion in 1997, none of the other predictions have come true. But AI research has contributed a huge amount of valuable technology, which has proved to be successful on narrow, specialized problems. Unfortunately, the field of AI has fragmented into those narrow specialties. Many researchers claim that their specialty is the key to solving all the problems. But the true key to AI is the knowledge that there is no key. Human intelligence comprises every specialty that anyone in any culture or civilization has ever dreamed of. Each one is adequate for a narrow range of applications. The power of human intelligence comes from the ability to relate, combine, and build on an open-ended variety of methods for different applications. Successful AI systems require a framework that can support any and all such combinations.

Ontological Modelling for Intelligent E-learning

Abstract: The evolution of scientific research as well as the development of the relevant technologies and methodologies in the area of distance learning fall within the scope of the objectives the Hellenic Open University whose mission is to provide distance education at both undergraduate and postgraduate level. To develop intelligent e-learning applications, we capture the semantics of the Computer Science module concepts with the use of ontologies, by following a specific methodology to build domain knowledge ontologies. We illustrate our approach by presenting the construction of the ontologies of the Artificial Intelligence & Expert Systems module.

Evolving Artificial Neural Networks applied to generate virtual characters

Abstract: Computer game industry is one of the most profitable nowadays. Although this industry has evolved fast in the last years in different fields, Artificial Intelligence (AI) seems to be stuck. Many games still make use of simple state machines to simulate AI. New models can be designed and proposed to replace this jurassic technique. In this paper we propose the use of Artificial Neural Networks (ANN) as a new model. ANN will be then in charge of receiving information from the game (sensors) and carry out actions (actuators) according to the information received. The search for the best ANN is a complex task that strongly affects the task performance while often requiring a high computational time. In this work, we present ADANN, a system for the automatic evolution and adaptation of artificial neural networks based on evolutionary ANN (EANN). This approach use Genetic Algorithm (GA) that evolves fully connected Artificial Neural Network. The testing game is called Unreal Tournament 2004. The new agent obtained has been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition [1], and have showed a significant improvement in the humannesss ratio. Additionally, we have confronted our approach and CCBot3 (winner of BotPrize competition in 2010) to First-person believability assessment (BotPrize original judging protocol), demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.

Using AI to model quality attribute tradeoffs

Abstract: Many AI techniques have been applied to goal-oriented requirements engineering. However, such techniques have focused mostly on the intellectual challenge and ignored the engineering challenge of RE at scale. We discuss some of these existing approaches. We then introduce some early work that aims to add contextual quality attribute information to leverage the power of AI techniques and tools with real-world engineering. We believe this will address some of the acquisition and context problems that have plagued AI in RE.

Applications of artificial intelligence technologies in credit scoring: A survey of literature

Abstract: Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms including the support vector machines, artificial neural networks, genetic algorithms, genetic programming algorithms and their hybridization.

Is Intelligence Artificial

Abstract: Our understanding of intelligence is directed primarily at the human level. This paper attempts to give a more unifying definition that can be applied to the natural world in general and then Artificial Intelligence. The definition would be used more to verify a relative intelligence, not to quantify it and might help when making judgements on the matter. While correct behaviour is the preferred definition, a metric that is grounded in Kolmogorov's Complexity Theory is suggested, which leads to a measurement about entropy. A version of an accepted AI test is then put forward as the 'acid test' and might be what a free-thinking program would try to achieve. Recent work by the author has been more from a direction of mechanical processes, or ones that might operate automatically. This paper agrees that intelligence is a pro-active event, but also notes a second aspect to it that is in the background and mechanical. The paper suggests looking at intelligence and the conscious as being slightly different, where consciousness is this more mechanical aspect. In fact, a surprising conclusion can be a passive but intelligent brain being invoked by active and less intelligent senses.

Is Model-Based Robot Programming a Mirage? A Brief Survey of AI Reasoning in Robotics

Abstract: Researchers in AI and Robotics have in common the desire to “make robots intelligent”, evidence of which can be traced back to the earliest AI systems. One major contribution of AI to Robotics is the model-centered approach, whereby intelligence is the result of reasoning in models of the world which can be changed to suit different environments, physical capabilities, and tasks. Dually, robots have contributed to the formulation and resolution of challenging issues in AI, and are constantly eroding the modeling abstractions underlying AI problem solving techniques. Forty-eight years after the first AI-driven robot, this article provides an updated perspective on the successes and challenges which lie at the intersection of AI and Robotics.

Intelligent Multi-platform Middleware for Wireless Sensor and Actuator Networks

Abstract: Wireless Sensor and Actuator Networks (WSAN), composed by small sensing nodes for acquisition, collection and analysis of data, are often employed for communication between Internet objects However the WSAN have some problems such as sensors’ energetic consumption and CPU load The massive storage capacity, large processing speeds and the rapid elasticity makes Cloud Computing a very good solution to these problems To efficiently manage devices’ resources, and achieve efficient communication with various platforms (cloud, mobile), this paper proposes a middleware that allows flows of AI applications’ execution to be transferred between a device and the cloud

Imitation learning for combat system in RTS games with application to starcraft

Abstract: Unlike the situation with regard to board games, artificial intelligence (AI) for real-time strategy (RTS) games usually suffers from an infinite number of possible future states. Furthermore, it must handle the complexity quickly. This constraint makes it difficult to build AI for RTS games with current state-of-the-art intelligent techniques. This paper proposes the use of imitation learning based on a human player's replays, which allows the AI to mimic the behaviors. During game play, the AI exploits the replay repository to search for the best similar moment from an influence map representation. This work focuses on combat in RTS games, considering the spatial configuration and unit types. Experimental results show that the proposed AI can defeat well-known competition entries a large percentage of the time.