Artificial Intelligence Search Techniques Research Articles

A Linear Time Pattern Based Algorithm for N-Queens Problem

The n-queens problem is the placing of n number of queens on an nxn chessboard so that no two queens attack each other. This problem is important due to various usage fields such as VLSI testing, traffic control job scheduling, data routing, dead-lock or blockage prevention, digital image processing and parallel memory storage schemes mentioned in the literature. Besides, this problem has been used as a benchmark for developing new Artificial Intelligence search techniques. However, it is known that backtracking algorithms, one of the most frequently used algorithms to solve this problem, cannot produce all solutions in large n values due to the exponentially growing time complexity. Therefore, various methods have been proposed for producing one or more solutions, not all solutions for each n value. In this study, a pattern based approach that produces at least one unique solution for all n values (n>3) was detected. By using this pattern, a new algorithm that produces at least one unique solution for even very large n values in linear time was developed. The developed algorithm with О(n) time complexity produces quite faster solution to n-queens problem and even in some values, this algorithm produces (n-1)/2 unique solutions in linear time.

Artificial intelligence-aided design: Smart Design for sustainable city development

Current planning and design decision support systems show limitations in the integration of design, science, and computation. Planning support systems with manual design and post-design evaluations impose major challenges in exploring huge design spaces. Generative design systems largely neglect the wicked nature of design problems and lack appropriate representation methods and simulation tools at the urban scale. To tackle those challenges, this research developed a Smart Design framework featuring urban design decision-making reinforced by artificial intelligence-aided design (AIAD). The Smart Design framework treats urban design as an emergent pattern formation processes with contextualized and dynamic objectives. The framework integrates design thinking, advanced artificial intelligence search techniques (e.g. genetic algorithms), urban scale performance simulations, and participation to better inform decision-making. Through four major stages, the framework combines the ideas of Science for Design and Design in Science. The significance and potential of the Smart Design framework are demonstrated in an urban design study of Gangnam superblocks in Seoul, South Korea. The study explores sustainable urban forms in the high-density, super-complex, and hyper-consumptive environment of Gangnam, which can also be found in many other Asian contexts. The case study illustrates how the framework identifies design solutions for sustainable city development in the process of participatory decision-making through the co-evolution of design problems and solutions.

Using a permutation representation genetic algorithm to implement a complex pick up/drop off mathematical model

The Variable Message Sign Problem (VMSP) aims to optimise the delivery, collection and maintenance of Variable Message Signs. Firstly, the problem was formulated as a rigorous mathematical model. It was then reformulated using a permutation representation removing the need for some hard constraints and the formation of illegal sub-tours within the solution. The reformulated model was shown to be solvable using different artificial intelligence search techniques. The problem of parameter selection was then solved using self-adaption, which produced superior solutions with >99.9% confidence by avoiding numerous local optima on the fitness landscape.

Development of a Car Racing Simulator Game Using Artificial Intelligence Techniques

This paper presents a car racing simulator game calledRacer, in which the human player races a car against three game-controlled cars in a three-dimensional environment. The objective of the game is not to defeat the human player, but to provide the player with a challenging and enjoyable experience. To ensure that this objective can be accomplished, the game incorporates artificial intelligence (AI) techniques, which enable the cars to be controlled in a manner that mimics natural driving. The paper provides a brief history of AI techniques in games, presents the use of AI techniques in contemporary video games, and discusses the AI techniques that were implemented in the development ofRacer.A comparison of the AI techniques implemented in the Unity platform with traditional AI search techniques is also included in the discussion.

Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions

Abstract: For the past three decades or so the flexible flow shop (FFS) scheduling problem has attracted many researchers. Numerous research articles have been published on this topic. This study reviews research on the FFS scheduling problem from the past and the present. The solution approaches reviewed range from the optimum to heuristics and to artificial intelligence search techniques. I not only discuss the details from the selected methods and compare them, but also provide insights and suggestions for future research.

An AI-based automatic power network topology processor

This paper describes a new power network topology processor which has been successfully used in two practical power system application programs. Artificial intelligence (AI) techniques have been used and this provides several advantages. AI's frame representation method is used to represent electric network configuration. This avoids manual numbering on a large amount of circuit breaker terminals/circuits. Rule-based method has been adopted for substation's bus configuration analysis instead of the search method. This makes the analysis simple and adaptable to new types of bus configurations just by adding corresponding rules. AI's search techniques have been applied for network graph search making the search algorithm simpler and easier to be programmed. Some results from the application of the proposed topology processor are also included in the paper.

A genetic-based fault-tolerant routing strategy for multiprocessor networks

AI-based search techniques have been adapted as viable, topology-independent fault-tolerant routing strategies on multiprocessor networks [P.K.K. Loh, Artificial intelligence search techniques as fault-tolerant routing strategies, Parallel Computing 22 (8) (1996) 1127–1147]. These fault-tolerant routing strategies are viable with the exception that the routes obtained were non-minimal. This meant that a large number of redundant node traversals were made in reaching the destination, increasing the likelihood of encountering further faulty network components. Here, we investigate the adaptation of a genetic-heuristic algorithm combination as a fault-tolerant routing strategy. Our results show that this hybrid fault-tolerant routing strategy produces minimal or near-minimal routes. Under certain fault conditions, this new strategy outperforms the heuristic AI-based ones with a significant reduction in the number of redundant traversals.

Balancing and sequencing mixed-model U-lines with a co-evolutionary algorithm

A mixed-model production line is such a line where a variety of product models are produced. In U-lines used in the just-in-time production system, the strategy of mixing product models is often employed to provide various types of products to customers on time. Line balancing and model sequencing problems are important for an efficient use of mixed-model U-lines. Although the two problems are tightly interrelated with each other, prior research has considered them separately or sequentially. In this paper, a new approach using an artificial intelligence search technique, called co-evolutionary algorithm, is proposed to solve the two problems at the same time. To promote population diversity and search efficiency in the algorithm, we adopt strategies of localized evolution and steady-state reproduction, and develop methods of selecting environmental individuals and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. The experimental results demonstrate that the proposed algorithm outperforms existing approaches.

Sequencing in mixed model assembly lines: A genetic algorithm approach

The mixed model assembly lines are becoming increasingly popular in a wide area of industries. We consider the sequencing problem in mixed model assembly lines, which is critical for efficient utilization of the lines. We extend standard formulation of the problem to allow a hybrid assembly line, in which closed and open workstations are intermixed, and sequence-dependent setup time. A new approach using an artificial intelligence search technique, called genetic algorithm, is proposed. A genetic representation suitable for the problem is investigated, and genetic control parameters that yield good results are empirically found. A new genetic operator, Immediate Successor Relation Crossover (ISRX), is introduced and several existing ones are modified. An extensive experiment is carried out to determine a proper choice of the genetic operators. The performance of the genetic algorithm is compared with those of heuristic algorithm and of branch-and-bound method. The results show that our algorithm greatly reduces the computation time and its solution is very close to the optimal solution. We have identified the ISRX operator to play a significant role in improving the performance.

Artificial intelligence search techniques as fault-tolerant routing strategies

Most fault-tolerant routing strategies are topology dependent or constrained to tolerate specific classes of faults. Artificial Intelligence search techniques, on the other hand, are potentially adaptive and can be applied to a generic problem space. We investigate the performance of such search techniques modified for use as fault-tolerant routing strategies on a problem space simulating a multiprocessor network of generic topology. This virtual network comprises processors individually equipped with a communications router designed to support message routing. The performances of these modified search techniques are compared for up to 20% of randomly generated link faults (with a processor failure being simulated by total link failure). The results show that the heuristic class of search techniques are potentially suitable for adaptation as fault-tolerant routing strategies on multiprocessor networks.

Developing transportable agricultural decision support systems: Part 2. An example

Abstract Information in the area of developing transportable decision support systems (DSSs) (agricultural or otherwise) has been scarce. Therefore, developers of DSSs have had some difficulties in constructing DSSs which could be widely used. In the first part of this two-part paper series, a conceptual framework was introduced which proposes methods for making DSSs transportable. This framework serves as a checklist and includes recommendations about general implementation, user interaction, data management, and model aspects. To illustrate the implementation of these recommendations, the development of a DSS within a project called SYBIL is discussed in detail. Therein, portable and public domain tools have been used to build the DSS with a graphical user interface (GUI) which satisfies the general implementation aspects of the framework. Further, the development of a flexible data management system has been essential in the project so that different types of data can be easily handled without changing the models. In order to allow models embedded within the DSS to be successfully transported between regions, a novel artificial intelligence (AI) adaptation methodology was implemented. The main component of this adaptation methodology is a genetic algorithm (GA), an AI search technique. By linking a GA to an agricultural model, the model becomes more robust because the model is able to adapt to the region in which it is being used. Overall, by using the framework criteria to select/create these tools/components, the DSS has been made easy to transport and disseminate.

A dynamic selection algorithm for globally optimal subsets

A new subset selection algorithm is proposed, based on artificial intelligence search techniques. It selects the globally best subset with the optimal size from an element set without exhaustive enumeration. Existing procedures either do not guarantee the optimality of the selected subset or do not determine optimal subset size. Exhaustive searches, on the other hand, are generally computationally unfeasible. The adoptability of the present algorithm is proved. The experiments, in the applications of the algorithm to clustering and feature selection, show its effectiveness.

Unidirectional and bidirectional search algorithms

Four classifications of artificial intelligence search techniques are discussed: unidirectional uniprocessor, bidirectional uniprocessor, unidirectional multiprocessor, and bidirectional multiprocessor search techniques. Wave-shaping PBA* (WS-PBA*) and search-space-clustering PBA*, (SSC-PBA*), two bidirectional AI search techniques, are compared. It is concluded that by maintaining a small number of formed clusters SSC-PBA* will be significantly faster than major wave-shaping bidirectional search algorithms. >

Natural scene recognition using locus search

This paper describes an image understanding system that can recognize photographs of the city of Pittsburgh. It uses an effective and efficient artificial intelligence search technique called Locus. The system builds a three-dimensional model of the city and uses information from hypothesized two-dimensional views to label images. It currently achieves less than 20% error when labeling photographs, given a knowledge base of over fifty objects. In addition, the system can determine the angle of view around the city with approximately 40° of error. The paper formulates image understanding as a problem of search; shows how Locus search can be used to label images; describes the many sources of knowledge used in the interpretation; explores extensions to the use of knowledge; and presents some experimental results. New issues presented here include the multidimensionality of Locus search and techniques for automatically acquiring scene knowledge.