A literature review on artificial intelligence
ABSTRACT Research on artificial intelligence in the last two decades has greatly improved perfor-mance of both manufacturing and service systems. Currently, there is a dire need for an article that presents a holistic literature survey of worldwide, theoretical frameworks and practical experiences in the field of artificial intelligence. This paper reports the state-of-the-art on artificial intelligence in an integrated, concise, and elegantly distilled manner to show the experiences in the field. In particular, this paper provides a broad review of recent developments within the field of artificial intelligence (AI) and its applications. The work is targeted at new entrants to the artificial intelligence field. It also reminds the experienced researchers about some of the issue they have known.
- SourceAvailable from: Luis M. de Campos[show abstract] [hide abstract]
ABSTRACT: Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.Journal of Artificial Intelligence Research - JAIR. 06/2011; 18.
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ABSTRACT: This paper presents a new classi er combination technique based on the DempsterShafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from dierent classi ers. However, since each of the available methods that estimates the evidence of classi ers has its own limitations, we propose here a new implementation which adapts to training data so that the overall mean square error is minimized. The proposed technique is shown to outperform most available classi er combination methods when tested on three dierent classi cation problems.12/2002;
Article: Planning by Rewriting[show abstract] [hide abstract]
ABSTRACT: Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.10/2001;
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International Journal of Information and Management Sciences
Volume 19, Number 4, pp. 535-570, 2008
A Literature Review on Artificial Intelligence
S. A. Oke
University of Lagos
Research on artificial intelligence in the last two decades has greatly improved perfor-
mance of both manufacturing and service systems. Currently, there is a dire need for an
article that presents a holistic literature survey of worldwide, theoretical frameworks and
practical experiences in the field of artificial intelligence. This paper reports the state-of-
the-art on artificial intelligence in an integrated, concise, and elegantly distilled manner to
show the experiences in the field. In particular, this paper provides a broad review of recent
developments within the field of artificial intelligence (AI) and its applications. The work is
targeted at new entrants to the artificial intelligence field. It also reminds the experienced
researchers about some of the issue they have known.
Keywords: AI, Neural Network, Business Efficiency, Genetic Algorithms, Fuzzy Logic.
In the 21stcentury artificial intelligence (AI) has become an important area of re-
search in virtually all fields: engineering, science, education, medicine, business, account-
ing, finance, marketing, economics, stock market and law, among others (Halal (2003),
Masnikosa (1998), Metaxiotis et al. (2003), Raynor (2000), Stefanuk and Zhozhikashvili
(2002), Tay and Ho (1992) and Wongpinunwatana et al. (2000)). The field of AI has
grown enormously to the extent that tracking proliferation of studies becomes a difficult
task (Ambite and Knoblock (2001), Balazinski et al. (2002), Cristani (1999) and Goyache
(2003)). Apart from the application of AI to the fields mentioned above, studies have
been segregated into many areas with each of these springing up as individual fields of
knowledge (Eiter et al. (2003), Finkelstein et al. (2003), Grunwald and Halpern (2003),
Guestrin et al. (2003), Lin (2003), Stone et al. (2003) and Wilkins et al. (2003)).
Received January 2007; Revised and Accepted September 2007.
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536 International Journal of Information and Management Sciences, Vol. 19, No. 4, December, 2008
1.1. The challenge of the AI field
This work grew out of the challenges that AI possesses in view of the rise and grow-
ing nature of information technology worldwide that has characterised business- and
non-business organisational development (Barzilay et al. (2002), Baxter et al. (2001),
Darwiche and Marquis (2002), Gao and Culberson (2002), Tennenholtz (2002) and
The necessity for research in AI is being motivated by two factors that are (i) to
give the new entrants into the AI field an understanding of the basic structure of the
AI literature (Brooks (2001), Gamberger and Lavrac (2002), Kim (1995), Kim and Kim
(1995), Patel-Schneider and Sebastiani (2003) and Zanuttini (2003)). As such, the litera-
ture discussed here answers the common query, “why must I study AI?” (ii) the upsurge
of interest in AI that has prompted an increased interest and huge investments in AI
Interested researchers from all disciplines wish to be aware of the work of others
in their field, and share the knowledge gleaned over the years (Rosati (1999), Kaminka
et al. (2002), Bod (2002), Acid and De Campos (2003), Walsh and Wellman (2003),
Kambhampati (2000) and Barber (2000)). By sharing AI knowledge, new techniques
and approaches can be developed so that a greater understanding of the field can be
gained. To these ends, this paper has also been written for researchers in AI so they
can continue in their efforts aimed at developing this area of concentration through
newly generated ideas. Consequently, they would be able to push forward the frontier of
knowledge in AI.
In the section that follows this paper presents a brief explanation of some important
areas in Artificial Intelligence. This is to introduce the readers into the wide-ranging
topics that AI encompasses. In another section, a comprehensive review of the literature
along the major categories of artificial intelligence is presented. The review raises some
important questions with serious research implications for those who are interested in
carrying out research artificial intelligence. These questions if well addressed will solve
some unresolved technical and non-technical issues carried over from the last decade to
the present time.
1.2. An overview of the AI field
On a very broad account the areas of artificial intelligence are classified into sixteen
categories (Becker et al. (2000), Singer et al. (2000), Chen and Van Beek (2001), Hong
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A Literature Review on Artificial Intelligence 537
(2001) and Stone et al. (2001)). These are: reasoning, programming, artificial life, be-
lief revision, data mining, distributed AI, expert systems, genetic algorithms, systems,
knowledge representation, machine learning, natural language understanding, neural net-
works, theorem proving, constraint satisfaction, and theory of computation (Peng and
Zhang (2007), Zhou et al. (2007) and Wang et al. (2007)). Since many readers of this
article may require a glance view of the AI field, the author has utilised a flow diagram to
illustrate the whole structure of this paper, and the relationship among the diverse fields
of AI, as presented in Figure 1. What follows is a brief discussion of some of the impor-
tant areas of AI (Chan and Darwiche (2002), Pool and Zhang (2003), Bhattacharyya and
Keerthi (2001), Chawla et al. (2002), Al-Ani and Deriche (2002) and Xu and Li (2000)).
These descriptions only account for a selected number of areas.
The first major area considered here is that of reasoning. Research on reasoning has
evolved from the following dimensions: case-based, non-monotonic, model, qualitative,
automated, spatial, temporal and common sense.
For an illustrative example, the case-based reasoning (CBR) is briefly discussed. In
CBR, a set of cases stored in a case base is the primary source of knowledge. Cases
represent specific experience in a problem-solving domain, rather than general rules.
The main activities when solving problems with cases are described in the case-based
reasoning cycle. This cycle proposes the four steps: relieve, reuse, revise and retain.
First, the new problem to be solved must be formally described as a case (new case).
Then, a case that is similar to the current problem is retrieved from the case base.
The solution contained in this retrieved case is reused to solve the new problem with
a new solution obtained and presented to the user who can verify and possibly revise
the solution. The revised case (or the experience gained during the case-based problem
solving process) is then retained for future problem solving. Detailed information on
“dimensions” or how they are related could be obtained from the relevant sources listed
in the references (Debruyne and Bessiere (2001), Halpern (2000), Halpern (2001), Renz
and Nebel (2001), Singh et al. (2002) and Straccia (2001)).
1.2.2. Genetic algorithm
The second major area of AI treated here is Genetic Algorithm (GA). This is a
search algorithm based on the mechanics of natural selection and natural genetics. It is
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Figure 1. Illustration concerning the relationship among the diverse fields of AI.
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A Literature Review on Artificial Intelligence539
an iterative procedure maintaining a population of structures that are candidate solu-
tions to specific domain challenges. During each generation the structures in the current
population are rated for their effectiveness as solutions, an don the basis of these evalua-
tions, a new population of candidate structures is formed using specific genetic operators
such as reproduction, cross over and mutation.
1.2.3. Expert system
The third aspect of AI discussed here is expert system. An expert system is com-
puter software that can solve a narrowly defined set of problems using information and
reasoning techniques normally associated with a human expert. It could also be viewed
as a computer system that performs at or near the level of a human expert in a particular
field of endeavour.
1.2.4. Natural language understanding
Natural language generation (NLG) systems are computer software systems that
produce texts in English and other human languages, often from non-linguistic input
data. NLG systems, like most AI systems, need substantial amounts of knowledge that
is difficult to acquire. In general terms, these problems were due to the complexity,
novelty, and poorly understood nature of the tasks the systems attempted, and were
worsened by the fact that people write so differently (Reiter et al. (2003)).
1.2.5. Knowledge representation (KR)
Knowledge bases are used to model application domains and to facilitate access to
stored information. Research on KR originally concentrated around formalisms that
are typically tuned to deal with relatively small knowledge base, but provide powerful
reasoning services, and are highly expressive.
2. The Artificial Intelligence Literature
2.1. Reasoning in artificial intelligence
The theory and practice of reasoning in artificial intelligence has extensive docu-
mentation (Atkinson and Bench-Capon (2007)). Researchers have worked in terms of:
(i) development of axioms that give sound and complete axiomazation for the logic of
reasoning; (ii) the theoretical properties of the algorithms used for qualitative temporal