Showing papers on "Adaptive reasoning published in 2007"

The logical thinking process : a systems approach to complex problem solving

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Complex Problem Solving: The European Perspective—10 Years After

Abstract: Complex Problem Solving (CPS) is a term that was introduced about 30 years ago in Germany by Dietrich Dorner This movement established not only a new type of problem to be studied, a type that differed from “simple” problem solving in terms of complexity, temporal dynamics, and other attributes, but also a new method, namely, the use of computer-simulated microworlds In this chapter, we focus on some of the issues that have been at the center of attention in the complex problem solving literature during the last years The chapter is divided into four parts In the first part, we briefly re-describe the historic roots of modern research on complex problem solving and establish a working definition of the concept In part two, we discuss one specific issue that has been of interest in the complex problem solving community lately, namely, the question to what extent, if at all, complex problem solving performance might be related to intelligence We discuss some of the older, and much of the most recent, research that has been concerned with exploring the link between intelligence and complex problem solving In doing so, we differentiate between explicit and implicit complex problem solving In the third part, we focus on the question to what extent, if at all, complex problem solving can be empirically distinguished from “simple” problem solving, and if there are domain specific or domain general principles at work In the final part of the chapter, we present and discuss ten myths about complex problem solving that we believe very much hampers scientific progress in the area at the present time

Reasoning and Problem Solving

Abstract: Conceptions of human reasoning have shifted from norms based on standard logic to emergence of reasoning from more fundamental processes. Reasoning entails operating on internal, cognitive representations of segments of the world, to yield decisions and actions that are adaptive in the person's environment. New methods for analyzing children's reasoning, including information integration theory and microgenetic analysis, have led to new understanding of processes, including verbal strategies, mental models, and analogy. New methods have been developed for assessing complexity of children's reasoning, including the cognitive complexity and control theory and relational complexity theory. Empirical studies indicate that the number of variables that children can relate in a single representation increases from 1 at age 1 year, 2 at 2 years, 3 at 5 years and 4 at 11 years. Complexity accounts for a large amount of variance in reasoning and complexity estimates are consistent across domains. New principles have been defined for simplifying complex tasks and overcoming capacity limitations. New theories of categorization include prototypes based on family resemblance and theory-based categories. Infants progress from perceptual to conceptual categorization. Understanding of relations between categories develops through early childhood, the relation between a category and its complement are understood by 3-year olds, and hierarchical classification and class inclusion are typically understood by a median age of five years. Children's understanding of conservation depends on integrating relevant dimensions and its appearance by age 5 years appears to reflect the underlying complexity. However, infants can quantify small sets and recognize changes such as adding or subtracting, while understanding of counting develops throughout middle childhood. Transitivity is fundamental to reasoning about relations and the difficulties experienced by young children reflect the complexity inherent in integrating premises in working memory. However, simpler processes that can be employed with the transitivity of choice paradigm lead to successes by young children and animals. Children's conditional reasoning and syllogistic reasoning can be based on mental models, the construction of which in working memory is influenced by inherent complexity. Development of scientific thinking includes understanding of dimensions time, speed, and distance, and the integration of variables weight and distance in the balance scale. The concept of the earth entails representing complex relations and depends on reconciling culturally transmitted knowledge with everyday experience by taking account of the large diameter of the earth and developing more sophisticated conceptions of gravity. Several promising new lines or research in children's reasoning are identified. Keywords: categories; complexity; logic; reasoning; relations; scientific thinking; working memory

Towards a Model of Context for Case-Based Diagnostic Problem Solving

Abstract: This paper presents a model of context based on the roles and elements of various context types. Two important roles of context are related to the notions of relevance and focus. The former is important for the quality of the results reached by a problem solving or learning task, while the latter is important for the performance efficiency of the task. Problem solving can be viewed as search in a large problem space where search for different entities is invoked at different stages. Context has a pruning effect on search, increasing proportionally to the incompleteness of the information at hand. Depending on the type of memory structure to be searched for, different types of contexts assist the access to memory. We attempt to give an account of the various types of contexts that facilitate memory access and utilization for different type of tasks. The criteria for distinguishing between several types of context elements are presented, and a context ontology based on these criteria is suggested. We then show how this account is integrated with a case-based approach to clinical problem solving.

Mathematical problem solving: an evolving research and practice domain

Abstract: Research programs in mathematical problem solving have evolved with the development and availability of computational tools. I review and discuss research programs that have influenced and shaped the development of mathematical education in Mexico and elsewhere. An overarching principle that distinguishes the problem solving approach to develop and learn mathematics is to conceptualize the discipline as a set of dilemmas or problems that need to be explored and solved in terms of mathematical resources and strategies. In this context, relevant questions that help structure and organize this paper include: What does it mean to learn mathematics in terms of problem solving? To what extent do research programs in problem solving orient curricular proposals? What types of instructional scenarios promote the students’ development of mathematical thinking based on problem solving? What type of reasoning do students develop as a result of using distinct computational tools in mathematical problem solving?

How students view the general nature of their errors

Abstract: In this study we examined how two students viewed the general nature of their proportional reasoning errors as they attempted to generalize numeric situations. Using a teaching experiment methodology we studied the reasoning of two students over 18 instructional sessions. One student, Dallas, appeared to recognize that the proportional reasoning error applied to all cases of a particular problem situation and began to apply this reasoning across problems. The other student, Lloyd, exhibited difficulty seeing the generality of his mistaken use of proportional reasoning and regularly repeated this error during the study. From the data we developed a schematized description of how students view the generality of their errors and tracked the changes in these views over the course of the study. We analyze how Dallas and Lloyd’s perception of errors shaped their understanding of proportional reasoning and provide suggestions for the role errors play in restructuring a student’s conceptual schema.

A framework for combining rules and geo-ontologies

Abstract: Geo-ontologies have a key role to play in the development of the geospatial-semantic web, with regard to facilitating the search for geographical information and resources. They normally hold large amounts of geographic information and undergo a continuous process of revision and update. Hence, means of ensuring their integrity are crucial and needed to allow them to serve their purpose. This paper proposes the use of qualitative spatial reasoning as a tool to support the development of a geo-ontology management system. A new framework for the representation of and reasoning over geo-ontologies is presented using the web ontology language (OWL) and its associated reasoning tools. Spatial reasoning and integrity rules are represented using a spatial rule engine extension to the reasoning tools associated with OWL. The components of the framework are described and the implementation of the spatial reasoning engine is presented. This work is a step towards the realisation of a complete geo-ontology management system for the semantic web.

A Performance Evaluation of Ontology-Based Context Reasoning

Abstract: The CARE middleware aims at supporting context-aware adaptation of Internet services in a mobile computing environment. The CARE hybrid reasoning mechanism is based on a loose interaction between ontological reasoning and efficient reasoning in a restricted logic programming language. In this paper we report recent experimental results on ontology-based context reasoning that support the hybrid approach

Distributed Case-Based Reasoning for Fault Management

Abstract: We outline a distributed case-based reasoning system that exploits various online knowledge sources and reasoning capabilities in a decentralized, self-organizing platform provided by peer-to-peer technologies The goal of the system is to assist operators in finding solutions for faults We present the research motivation and issues in this paper

Rely-guarantee approach to reasoning about aspect-oriented programs

Abstract: Over the last few years, the question of reasoning about aspect-oriented programs has been addressed by a number of authors. In this paper, we present a rely-guarantee approach to such reasoning. The rely-guarantee approach has proven extremely successful in reasoning about concurrent and distributed programs. We show that some of the key problems encountered in reasoning about aspectoriented programs are similar to those encountered in reasoning about concurrent programs; and that the rely-guarantee approach, appropriately modified, helps address these problems. We illustrate our approach with a simple example.

devising a context selection-based reasoning engine for context-aware ubiquitous computing middleware

Abstract: We propose a novel reasoning engine for context-aware ubiquitous computing middleware in this paper. Our reasoning engine supports both rulebased reasoning and machine learning reasoning. Our main contribution is to utilize feature selection method to filter the low-level contexts which are not useful for certain special high-level context reasoning. As a result, rules and learning models in the reasoning engine's knowledge base are refined since useless context have been filtered. The merits of our proposed reasoning engine are described in details in this paper.

Cognitive approach to problem solving of social and economic object development.

Abstract: The basic technique of problem-solving is structurization of knowledge about object and its environment and construction of a cognitive model. The technique includes monitoring of dynamics of factors of the model (their tendencies), analysis of the model structure with the use of SWOT-approach, and modeling that permits to determine and solve semi-structured problems. The technique allows supporting of a vital control task that consists in goal setting of socio-economic object development, as far as solution of discovered problems turns into the system development control task. The application of technique is useful when designing a strategy of development of social and economic objects.

Retrieval and Learning in Analogical Problem Solving

Abstract: EUREKA is a problem-solving system that operates through a form of analogical reasoning. The system was designed to study how relatively low-level memory, reasoning, and learning mechanisms can account for high-level learning in human problem solvers. Thus, EUREKA’s design has focused on issues of memory representation and retrieval of analogies, at the expense of complex problem-solving ability or sophisticated analogical elaboration techniques. Two computational systems for analogical reasoning, ARCS/ACME and MAC/FAC, are relatively powerful and well-known in the cognitive science literature. However, they have not addressed issues of learning, and they have not been implemented in the context of a performance task that can dictate what makes an analogy “good”. Thus, it appears that these different research directions have much to offer each other. We describe the EUREKA system and compare its analogical retrieval mechanism with those in ARCS and MAC/FAC. We then discuss the issues involved in incorporating ARCS and MAC/FAC into a learning problem solver such as EUREKA. We are interested in the low-level memory, learning, and reasoning processes that give rise to improvement in problemsolving behavior over time. EUREKA is the problem-solving architecture we are using to study these processes. An explicit assumption within EUREKA’s design is that all processes are aspects of analogical reasoning. In addition, we designed the system so that the low-level retrieval and matching processes would dominate its behavior. The system does not possess or learn the types of high-level control knowledge found in other problem-solving systems. Our intent is to investigate how much of human learning in problem solving can be modeled with such low-level mechanisms. This paper presents an overview of EUREKA’s architecture and some of the learning results it accounts for. We then turn our attention to two well-known analogical retrieval mechanisms in the cognitive science literature. ARCS (Thagard, Holyoak, Nelson, & Gochfeld, 1990) and MAC/FAC (Gentner & Forbus, 1991) model psychological findings on analogical retrieval and reasoning. However, neither has been examined in the context of a problem-solving system, or in a system that learns with experience. The remainder of the paper focuses on the issues of analogical retrieval and learning, and discusses the possibilities of incorporating these alternative analogical retrieval mechanisms into a problem-solving system. Terminology Before continuing, it is worth defining some terms to avoid future confusion. For analogical reasoning, a basic unit of knowledge is the analogical case, which is further decomposed into a set of concepts and relations between those concepts. For our purposes, every analogical case corresponds to a problem situation. A problem situation is a specific set of relations describing a state of the world, together with a set of goal relations that should be achievable by applying a sequence of operators to that state. Note that cases in EUREKA are a bit different from those in case-based reasoning, where “case” typically denotes an entire problem solution. At any given time, EUREKA will have a current problem situation, for which it must decide on an operator to apply. This is the target problem situation. The analogical reasoning process is generally divided into three stages. First, a retrieval mechanism identifies a number of candidate sources from the potential analogies stored in memory. Next, the set of candidate sources undergo further elaboration to fill out the potential mappings between each source and the target. Finally, evaluation of each candidate source determines how well each candidate will serve as an analogical source for the target. Let us now turn to a description of EUREKA in these terms. An overview of EUREKA Jones (1993) presents the computational details of EUREKA, but here we provide a general overview of the system. EUREKA adopts a reasoning formulation called flexible meansends analysis (Jones & VanLehn, 1994; Langley & Allen, 1991). As described above, each problem situation includes a current world state and a set of goal conditions to which the state should be transformed. Operator selection creates a goal to apply a particular operator to the current state of the problem situation. If the preconditions of the operator can all be matched to the current state, the operator executes, leading to a new problem situation with a different state but the same goals. Otherwise, the system sets up a new problem situation with the same current state, but with the operator’s preconditions as the new goals. EUREKA then treats this new problem situation in a recursive manner. The difference between flexible means-ends analysis and standard means-ends analysis (Ernst & Newell, 1969; Fikes & Nilsson, 1971) is that the flexible form does not require selected operators to apply directly to the current goal conditions (i.e., it is not necessary that the selected operator obviously “reduce any differences”). Rather than using this heuristic to limit search, EUREKA relies on its retrieval and learning mechanisms to control which operators are suggested to apply to any particular problem situation. Because operator selection depends on the entire problem situation (and not just the goals), EUREKA can blend goal-driven and opportunistic behavior when appropriate. Every time EUREKA generates a new problem situation, it stores a representation of the situation (as well as the operator the led to this situation) into its long-term semantic network. Each object and relation in a problem situationbecomes a node in the semantic network. In addition, the network stores nodes representing instances of architecturally defined concepts, such as problem situations and operators. Items are never deleted from long-term memory, and memories are never stored in an abstract form. Rather, the semantic memory stores all the specific problem situations that it encounters. Situations become linked together in memory when they share objects, relations, or object types. If a particular concept from a problem situation already exists in memory, EUREKA increases the trace strengths of the links from the concept, rather than adding a new copy of the concept. When EUREKA is working on a particular problem situation, it must select an operator to apply to the problem. To this end, EUREKA retrieves a subset of the stored problem situations from long-term memory. This small set of candidate sources is further elaborated and evaluated, to see which would provide the best candidate analogy for the current problem situation. EUREKA chooses one candidate stochastically, based on the evaluation score, and identifies the operator associated with that source analogy. Finally, the system creates a goal to apply to the newly mapped operator to the current state. EUREKA proceeds in this manner until it solves the problem or the current solutionpath fails (by exceeding a time limit or detecting a cycle in the solution path). Upon failure, EUREKA does not have the luxury of backtracking, which would allow the system to search the problem space systematically and possibly exhaustively. Rather, EUREKA begins the problem anew from the initial problem situation. The inability to backtrack systematically greatly hinders the system’s ability to solve problems, but we feel that this is a psychologically plausible limitation. The limitation also places further importance on effective learning. The combination of EUREKA’s learning mechanisms and its stochastic selection process encourage the system to explore alternative solution paths on subseqent attempts to solve a problem. However, there is no guarantee that a previous search will not be duplicated. If the system fails to find a solution after a preset number of attempts (50 in our experiments), it abandons the problem completely. Analogical retrieval in EUREKA EUREKA’s analogical reasoner incorporates two stages. The Table 1: EUREKA’s algorithm for spreading activation. Let ACTIVATION_THRESHOLD be 0.01; Let DAMPING_FACTOR be 0.4; Let INITIAL_ACTIVATION be 1.0;

A Framework for using Rough Sets and Formal Concept Analysis in Case Based Reasoning

Abstract: A significant open problem of case based reasoning system is a construction of better knowledge base. We propose a new framework for constructing alternative knowledge base in case based reasoning system based on rough sets and formal concept analysis. Our framework first applies rough set theory for discovering reduced cases required in a case based reasoning system. We then achieve further hierarchical structure of knowledge base using formal concept analysis. The result is the concept lattice knowledge base embedded to our proposed case based reasoning system. A part of case based reasoning system is developed with an example throughout. We also discuss how our proposed framework can be beneficial for a case based reasoning system.

Bilattice based logical reasoning for automated visual surveillance and other applications

Abstract: The primary objective of an automated visual surveillance system is to observe and understand human behavior and report unusual or potentially dangerous activities/events in a timely manner. Automatically understanding human behavior from visual input, however, is a challenging task. The research presented in this thesis focuses on designing a reasoning framework that can combine, in a principled manner, high level contextual information with low level image processing primitives to interpret visual information. The primary motivation for this work has been to design a reasoning framework that draws heavily upon human like reasoning and reasons explicitly about visual as well as non-visual information to solve classification problems. Humans are adept at performing inference under uncertainty by combining evidence from multiple, noisy and often contradictory sources. This thesis describes a logical reasoning approach in which logical rules encode high level knowledge about the world and logical facts serve as input to the system from real world observations. The reasoning framework supports encoding of multiple rules for the same proposition, representing multiple lines of reasoning and also supports encoding of rules that infer explicit negation and thereby potentially contradictory information. Uncertainties are associated with both the logical rules that guide reasoning as well as with the input facts. This framework has been applied to visual surveillance problems such as human activity recognition, identity maintenance, and human detection. Finally, we have also applied it to the problem of collaborative filtering to predict movie ratings by explicitly reasoning about users preferences.

How to elicit sound probabilistic reasoning: Beyond word problems

Abstract: Barbey & Sloman (B&S) conclude that natural frequency theorists have raised a fundamental question: What are the conditions that compel individuals to reason extensionally? We argue that word problems asking for it numerical judgment used by these theorists cannot answer this question. We present evidence that nonverbal tasks call elicit correct intuitions of posterior probability even in preschoolers.

Combining Uncertain Belief Reasoning and Uncertain Metaphor-Based Reasoning

Abstract: An implemented AI reasoning system called ATT-Meta is sketched. It addresses not only AI issues but also ones that are salient in psychology, philosophy, cognitive linguistics, discourse pragmatics and other disciplines. These issues include the Simulation-Theory/Theory-Theory debate and Fauconnier and Turner’s notion of conceptual blending. The system performs metaphor-based reasoning and reasoning about mental states of agents; in particular, it performs metaphor-based reasoning about mental states. Although it relies on built-in knowledge of specific conceptual metaphors, it is flexible in allowing novel discourse manifestations of those metaphors. The metaphorical reasoning and mental-state reasoning facilities are fully integrated into a general framework for uncertain reasoning. A special result of the overall approach is that it enables a unified handling of certain apparently separate discourse phenomena: chained metaphor, personification metaphor, and reports of agents’ own metaphorical thoughts.

The retrieval process in the safrs system with the case-based reasoning approach

Abstract: The invention relates to a pressure pulse dampener device to be used in association with a liquid conduit, said device comprising a pressure vessel having a movable partition therein defining two chambers, each having a port in communication therewith, one of the ports defining a gas port for charging of one of said chambers with gas under pressure and the other, a liquid port, said liquid port having a hollow fitting rigidly secured thereto, said fitting having an inlet port and an outlet port, the fitting having means therein defining a tortuous path for oil under pressure from said inlet port to said outlet port, the liquid port being valve controlled and being in communication with the tortuous path, the device being characterized by incorporating therein in the fluid conduit downstream of the outlet port, a valve controlled outlet through which the liquid may flow, the effective cross sectional area of the outlet being varied automatically responsive to the pressure in the hydraulic system.

Reasoning about the behavior of aspect-oriented programs

Abstract: Aspect-oriented programming (AOP) has become increasingly popular over the last few years. At the same time, reasoning about the behavior of these programs poses serious challenges. In this paper, we present a rely-guarantee- approach to such reasoning. The rely-guarantee approach has proven useful in reasoning about concurrent and distributed programs. We show that some of the key problems encountered in reasoning about aspect-oriented programs are similar to those encountered in reasoning about concurrent programs; and that the rely-guarantee approach, appropriately modified, helps address these problems. We illustrate our approach with a simple example.

Motivated reasoning: Motivated reasoning

Abstract: Our attempts to resolve conflicting information involve parts of the brain thought to handle emotional matters. Knowing about this might influence how you care for your patients and integrate new knowledge. You and I deal with conflicting information all the time. We like to think that we are rational, but the evidence suggests otherwise. A recent study of the functional MRIs of political partisans (1) (and not of paediatricians), showed that the areas of the brain involved in dealing with conflicting information were those associated with emotions, and not cold rational analyses. Indeed, the brain’s pleasure centres lit up with flares of activity when unwelcome information was being rejected. These studies did not show activity in the areas that tend to ‘light up’ when people lie. Thus, it seems eminently reasonable to assume that the rejecting of conflicting information was not a conscious denial of the truth. Psychologists use the term ‘motivated reasoning’ for reasoning that is biased to produce emotionally desirable conclusions (2). Consider how your reasoning can be ‘motivated’. A routine test comes back with an ominous value. If you are like others, your first move is to repeat the test. When the result is reassuring, you accept it. Why should a reassuring test result trump a non-reassuring result? Why not try for the best two out of three? Yet, many of us stop as soon as we are reassured. We very much want to be reassured. Ever notice the sense of relief that you experience when you are reassured. The emotional aspects of the sense of relief cannot be denied. Might your brain’s pleasure centres have lit up on a functional MRI? Consider also that motivated reasoning influences what you read in this journal. This could reflect the motivated reasoning of an author or a reviewer. Some of us are overwhelmed with the amount of progress made in all of paediatrics. This alone is reason for discomfort. If we then also consider the conflicting findings of dif-

Simulation and analysis of controlled multi-representational reasoning processes

Abstract: Multi-representational reasoning processes often show a variety of reasoning paths that can be followed. To analyze such reasoning processes with special attention for differences between individuals, it is required (1) to obtain an overview of the variety of different possibilities and (2) to address navigation and control within the reasoning process. This paper presents a simulation model and a formal analysis method for the dynamics of a controlled reasoning process in which multiple representations play a role. Reasoning strategies to navigate through the space of possible reasoning states are modeled explicitly, and simulated. Simulation results are analyzed by software tools on the basis of formalized dynamic properties. The variety of dynamic properties specified and the variety of traces simulated provides an overview for the individual differences between subjects that have been observed while solving multiplication problems.