An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

相同知识背景下技能之间迁移机制的研究

Since the middle of 1980's, the mechanisms of transfer of training between cognitive subskills rest on the same body of declarative knowledge has been highly concerned. The dominant theory is theory of common element (Singley & Anderson, 1989) which predict that there will be little or no transfer between subskills within the same domain when knowledge is used in different ways, even though the subskills might rest on a common body of declarative knowledge. This idea is termed as "principle of use specificity of knowledge" (Anderson, 1987). Although this principle has gained some empirical evidence from different domains such as elementary geometry (Neves & Anderson, 1981) and computer programming (McKendree & Anderson, 1987), it is challenged by some research (Pennington et al., 1991; 1995) in which substantially larger amounts of transfer of training was found between substills that rest on a shared declarative knowledge but share little procedures (production rules). Pennington et al. (1995) provided evidence that this larger amounts of transfer are due to the elaboration of declarative knowledge. Our research provide a test of these two different explanation, by considering transfer between two subskills within the domain of elementary geometry and elementary algebra respectively, and the inference of learning method ("learning from examples" and "learning from declarative-text") and subject ability (high, middle, low) on the amounts of transfer. Within the domain of elementary geometry, the two subskills of generating proofs" (GP) and "explaining proofs" (EP) which are rest on the declarative knowledge of "theorems on the characters of parallelogram" share little procedures. Within the domain of elementary algebra, the two subskills of "calculation" (C) and "simplification" (S) which are rest on the declarative knowledge of "multiplication of radical" share some more procedures. The results demonstrate that: 1. Within the domain of elementary geometry, although little transfer was found between the two subskills of GP and EP within the total subjects, different results occurred when considering the factor of subject's ability. Within the high level subjects, significant positive transfer was found from EP to GP, while little transfer was found on the opposite direction (i. e. from GP to EP). Within the low level subjects, significant positive transfer was found from EP to GP, while significant negative transfer was found on the opposite direction. For the middle level subject, little transfer was found between the two subskills. 2. Within the domain of elementary algebra, significant positive transfer was found from S to C, while significant negative transfer was found on the opposite direction (i. e. from C to S), when considering the total subjects. The same pattern of transfer occurred within the middle level subjects and low level subject. Within the high level subjects, no transfer was found between the two subskills. 3. Within theses two domains, different learning methods yield little influence on transfer of training between subskills. Apparently, these results can not be attributed to either common procedures or elaboration of declarative knowledge. A kind of synthetic inspection is essential to construct a reasonable explanation of these results which should take into account the following three elements: (1) relations between the procedures of subskills; (2) elaboration of declarative knowledge; (3) elaboration of procedural knowledge. 排Excluding the factor of subject, transfer of training between subskills can be predicted and explained by analyzing the relations between the procedures of two subskills. However, when considering some certain subjects, the explanation of transfer of training between subskills must include subjects' elaboration of declarative knowledge and procedural knowledge, especially the influence of the elaboration on performing the other subskill. The fact that different learning methods yield little influence on transfer of training between subskills can be explained by the fact that these two methods did not effect the level of declarative knowledge. Protocol analysis provided evidence to support these hypothesis. From this research, we conclude that in order to expound the mechanisms of transfer of training between cognitive subskills rest on the same body of declarative knowledge, three elements must be considered synthetically which include: (1) relations between the procedures of subskills; (2) elaboration of declarative knowledge; (3) elaboration of procedural knowledge.

Taxonomic Syntax for First-Order Inference

Most knowledge representation languages are based on classes and taxonomic relationships between classes. Taxonomic hierarchies without defaults or exceptions are semantically equivalent to a collection of formulas in first order predicate calculus. Although designers of knowledge representation languages often express an intuitive feeling that there must be some advantage to representing facts as taxonomic relationships rather than first order formulas, there are few, if any, technical results supporting this intuition. We attempt to remedy this situation by presenting a taxonomic syntax for first order predicate calculus and a series of theorems that support the claim that taxonomic syntax is superior to classical syntax.

Descriptive Simulation: Combining Symbolic and Numerical Methods in the Analysis of Chemical Reaction Mechanisms

The Kineticist's Workbench is a computer program currently under development whose purpose is to help chemists understand, analyze, and simplify complex chemical reaction mechanisms. This paper discusses one module of the program that numerically simulates mechanisms and constructs qualitative descriptions of the simulation results. These descriptions are given in terms that are meaningful to the working chemist (e.g., steady states, stable oscillations, and so on); and the descriptions (as well as the data structures used to construct them) are accessible as input to other programs.

Automatic Recognition of Tractability in Inference Relations

A procedure is given for recognizing sets of inference rules that generate polynomial time decidable inference relations. The procedure can automatically recognize the tractability of the inference rules underlying congruence closure. The recognition of tractability for that particular rule set constitutes mechanical verification of a theorem originally proved independently by Kozen and Shostak. The procedure is algorithmic, rather than heuristic, and the class of automatically recognizable tractable rule sets can be precisely characterized. A series of examples of rule sets whose tractability is non-trivial, yet machine recognizable, is also given. The technical framework developed here is viewed as a first step toward a general theory of tractable inference relations.

Quantitative Inference in a Mechanical Design Compiler

This paper presents the ideas underlying a program that takes as input a schematic of a mechanical or hydraulic power transmission system, plus specifications and a utility function, and returns catalog numbers from predefined catalogs for the optimal selection of components implementing the design. It thus provides the designer with a high level "language" in which to compose new designs, then performs some of the detailed design process for him. The program is based on a formalization of quantitative inferences about hierarchically organized sets of artifacts and operating conditions, which allows design compilation without the exhaustive enumeration of alternatives.

Different mechanisms for the formation of acetaldehyde and ethanol on the Rh-based catalysts

Different mechanisms for the formation of acetaldehyde and ethanol on the Rh-based catalysts were investigated by the TPR (temperature programmed reaction) method, and the active sites were studied by CO-TPD, TPSR (temperature programmed surface reaction of preadsorbed CO by H-2) and XPS techniques. The TPR results indicated that ethanol and acetaldehyde might be formed through different intermediates, whereas ethanol and methanol might result from the same intermediate. Results of CO-TPD, TPSR, and XPS showed that on the Rh-based catalyst, the structure of the active sites for the formation of C-2-oxygenates is ((RhxRhy+)-Rh-0)-O-Mn+ (M=Mn or Zr, x>>y, 2 less than or equal ton less than or equal to4). The tilt-adsorbed CO species is the main precursor for CO dissociation and the precursor for the formation of ethanol and methanol. Most of the linear and geminal adsorbed CO species desorbed below 500 K. Based on the suggested model of the active sites, detailed mechanisms for the formation of acetaldehyde and ethanol are proposed. Ethanol is formed by direct hydrogenation of the tilt-adsorbed CO molecules, followed by CH2 insertion into the surface CH2-O species and the succeeding hydrogenation step. Acetaldehyde is formed through CO insertion into the surface CH3-Rh species followed by hydrogenation, and the role of the promoters was to stabilize the intermediate of the surface acetyl species. (C) 2000 Academic Press.

The Role of Chemical Mechanisms in Neural Computation and Learning

Most computational models of neurons assume that their electrical characteristics are of paramount importance. However, all long-term changes in synaptic efficacy, as well as many short-term effects, are mediated by chemical mechanisms. This technical report explores the interaction between electrical and chemical mechanisms in neural learning and development. Two neural systems that exemplify this interaction are described and modelled. The first is the mechanisms underlying habituation, sensitization, and associative learning in the gill withdrawal reflex circuit in Aplysia, a marine snail. The second is the formation of retinotopic projections in the early visual pathway during embryonic development.

Hypothesizing Device Mechanisms: Opening Up the Black Box

I describe an approach to forming hypotheses about hidden mechanism configurations within devices given external observations and a vocabulary of primitive mechanisms. An implemented causal modelling system called JACK constructs explanations for why a second piece of toast comes out lighter, why the slide in a tire gauge does not slip back inside when the gauge is removed from the tire, and how in a refrigerator a single substance can serve as a heat sink for the interior and a heat source for the exterior. I report the number of hypotheses admitted for each device example, and provide empirical results which isolate the pruning power due to different constraint sources.

TYPICAL: A Knowledge Representation System for Automated Discovery and Inference

TYPICAL is a package for describing and making automatic inferences about a broad class of SCHEME predicate functions. These functions, called types following popular usage, delineate classes of primitive SCHEME objects, composite data structures, and abstract descriptions. TYPICAL types are generated by an extensible combinator language from either existing types or primitive terminals. These generated types are located in a lattice of predicate subsumption which captures necessary entailment between types; if satisfaction of one type necessarily entail satisfaction of another, the first type is below the second in the lattice. The inferences make by TYPICAL computes the position of the new definition within the lattice and establishes it there. This information is then accessible to both later inferences and other programs (reasoning systems, code analyzers, etc) which may need the information for their own purposes. TYPICAL was developed as a representation language for the discovery program Cyrano; particular examples are given of TYPICAL's application in the Cyrano program.

Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior

Huelse, M., Wischmann, S., Manoonpong, P., Twickel, A.v., Pasemann, F.: Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior. In: M. Lungarella, F. Iida, J. Bongard, R. Pfeifer (Eds.) 50 Years of Artificial Intelligence, LNCS 4850, Springer, 186 - 195, 2007.