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Assessing Responses to Situated Cognition

July 1999

Authors:

North Carolina State University

Situated cognition (SC) claims that knowledge is mostly context-dependent and that symbolic descriptions elicited prior to direct experience are less important than functional units developed via direct experience with the current problem. If this were true, then we would need to modify the knowledge modeling approaches of KA which assume that re-using old symbolic descriptions are a productivity tool for new applications. There are numerous tools which, if added to conventional knowledge modeling, could be said to handle SC (e.g. machine learning, abduction, verification & validation tools, repertory grids, certain frameworks for decision support systems, expert critiquing systems, and ripple-down-rules). However, we require an experiment to assess the effectiveness of these tools as a response to SC. 1 Introduction "What is wanted is not the will to believe, but the will to find out, which is the exact opposite." -- Bertrand Russell "Measure what is measurable, and make measurable w...

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Epistemological Approach to the Process of Practice

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Dec 2008

Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in current approaches is a significant contributor to brittleness. This paper will investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts. This paper argues that the omission of these higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally, these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step towards a philosophy of knowledge that could lead to the modelling of the process of practice.

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Linking modeling tools and the participatory approach for development is not a common combination. Participatory multi-agent system modeling (PMASM) is a tool for sharing viewpoints among stakeholders and facilitating the negotiation process. A key question of this approach is the acquisition and the modeling of the various stakeholders' representations. Our research team, whose Asian branch is represented in this book, tries to formalize the passage from fieldwork to the model by defining a methodology that can be implemented in the field. This methodology adapts knowledge engineering acquisition techniques to in-field stakeholders' representations for PMASM. In a northern Thailand watershed, we pursued implementation tests of this methodology. We first explored two ways to tackle fieldwork (ethnographic and project surveys), both showing weaknesses and strengths. We then built a first-version diagram syntax used for representing individual farmers' representations, and we considered options for analyzing those diagrams. Finally, we tested the elicited representations by leading farmers, through game-like sessions, to rebuild a model of their system structured by ele

A Methodology for Eliciting and Modelling Stakeholders’ Representations with Agent Based Modelling

Conference Paper

Full-text available

Jul 2003
Lect Notes Comput Sci

In the frame of using models to promote dialogue among stakeholders, we are interested in modelling representations as a mean to share viewpoints and opinions. A first trend for modelling representations uses socio-cognitive theories as frameworks for modelling. This article proposes a different approach combining Knowledge Engineering elicitation techniques and Agent Based Modelling. The use of elicitation techniques provides a methodological framework to capture representations directly from stakeholders’ discourse. Agent Based Modelling serves as a modelling structure which enables managing representations’ heterogeneity and conflicting opinions. The proposed methodology is detailed and applied to farmers’ representations of runoff and erosion in south of France. To achieve the construction of a running model of the interviewees’ shared representation, it was necessary to implement theoretical data supplementing the elicited information. We argue that companion modelling approach can help sharing and discussing those non-elicited information with stakeholders.

Building Knowledge Bases with situations to help the cooperative revision

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Nov 1998

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: This paper describes REVINOS, an incremental modeling and cooperative revision tool for Knowledge Bases (KB) expressed with situation nodules. Situation nodules are simple and understandable objects, which allow to specify step by step interactions between the knowledge-based system and his environment. Knowledge representation by situation nodules has been applied for defining the chaining of French Minitel dialogues. This representation can be useful for building "evolutive" KB in an incremental and operational approach. Representations and tools that facilitate the comprehension and revision of the KB are very helpful in this context. The goal of our cooperative revision tool is to let the user concentrate on the modeling of some objects of the KB (situations, actions, facts) while the updating of the chaining rules of situation nodules are made automatically by machine learning techniques. 1. Introduction To facilitate the comprehensibility and the revision of the KB, we propos...

Ontology Models for Supporting Exploratory Information Needs

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Exploratory browsing is potentially an important technique for retrieving text information from large knowledge bases. However, the task of information seeking is complex and it is easy to get lost in a composite network of nodes representing concepts. In this paper, we show how an exploring capability can be supported by ontology-based knowledge. The disadvantages mentioned above are avoided by providing facilities for guiding users' exploratory tasks particularly if they are not experts in the domain. We offer a way for information seekers or explorers to pursue information objects relevant to their tasks and apply task action steps to achieve their goals and intentions. Three types of ontology-based models were proposed that structure and organise information to support information access. The retrieved information is designed for navigating users' information seeking, but not restricting users' options. In addition, we discuss the maintenance of users' task actions to support information re-use purposes.

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Increasingly, researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. For instance, Repertory Grids, Formal Concept Analysis (FCA) and Ripple-Down Rules (RDR) all integrate either implicit or explicit contextual information. However, these methodologies treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts, which aid their ability to generalize. This paper presents a method that models hidden context within a symbolic domain in order to achieve a level of generalisation. The method developed builds on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). RM retains a symbolic core, while using a connection based approach to learn a deeper understanding of the captured knowledge. This method is applied to a number of classification and prediction environments and results indicate that the method can learn the information that experts have difficulty providing.

An Approach for Generalising Symbolic Knowledge

Conference Paper

Dec 2008
Lect Notes Comput Sci

Many researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. However, they generally treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts, which aids generalization. This paper presents a method that models hidden context within a symbolic domain achieving a level of generalisation. Results indicate that the method can learn the information that experts have difficulty providing by generalising the captured knowledge.

35 Kinds of Knowledge Maintenance

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Full-text available

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Situated cognition motivates a review of knowledge maintenance. If knowledge is situated, then a symbolic knowledge base will require modification as the situation changes. We examine the AI and software engineering literature which relates to acquiring and changing programs. Based 5 on a reverse engineering of that literature, we see that strategies have been offered to maintain different types of knowledge: words; sentences; behavioural knowledge; and meta-knowledge. Meta-knowledge divides into problem solving methods; quality knowledge; fix knowledge; social knowledge; and processing activities. We see several kinds of processing activities in 10 the literature: acquire; operationalise; fault; fix; and preserve. To date, no single strategy maintains all types of knowledge or performs all processing activities. However, we can say that many of these processing activities perform some kind of search space reflection. We can use this framework as follows. By using the quality knowled...

Expert and Novice Performance in Solving Physics Problems

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Although a sizable body of knowledge is prerequisite to expert skill, that knowledge must be indexed by large numbers of patterns that, on recognition, guide the expert in a fraction of a second to relevant parts of the knowledge store. The knowledge forms complex schemata that can guide a problem's interpretation and solution and that constitute a large part of what we call physical intuition.

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Existing group decision support systems used in meeting rooms can help teams reach decisions quickly and efficiently. However, the decision models used by these systems are inadequate for many types of problems. This paper describes our laboratory's experience with knowledge acquisition systems and decision support tools. Our studies led us to develop a comprehensive decision model for group decision support systems. This decision model combines current brainstorming-oriented methods, structured text argumentation (using the gIBIS model), repertory grids, possibility tables (morphological charts) and influence diagrams from decision analysis. Each component addresses weaknesses in current group decision support systems. We are assembling these group decision support components together into a group decision workbench.

The Myth of the Computer

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