The MEDIATOR: Analysis of an Early Case‐Based Problem Solver<sup>4</sup>

Kolodner, Janet L.; Simpson, Robert L.

doi:10.1207/s15516709cog1304_2

Cited by 137 publications

(16 citation statements)

References 23 publications

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“…Like other case-based systems (Alterman, 1985;Hammond, 1989;Kolodner, 1985;Kolodner & Simpson, 1989;Simpson, 1985;Riesbeck & Schank, 1989), RUNNER makes the following basic claims:…”

Section: Predictive Features In Case-based Modelsmentioning

confidence: 99%

Case-based learning: Predictive features in indexing

et al. 1994

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Abstract. Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This "predictive features" hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.We present a specific case-based model of plan execution, RUNNER, along with its indices for recognizing when to select particular plans--appropriateness conditions--and how these predictive indices serve to enhance learning. We then discuss how this predictive features claim as implemented in the RUNNER model is then tested in a second set of psychological studies. The results show that learning appropriateness conditions results in greater success in recognizing when a past plan is in fact relevant in current processing, and produces more reliable recall of the related information. This form of collaboration has resulted in a unique integration of computational and empirical efforts to create a model of case-based learning.

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“…Like other case-based systems (Alterman, 1985;Hammond, 1989;Kolodner, 1985;Kolodner & Simpson, 1989;Simpson, 1985;Riesbeck & Schank, 1989), RUNNER makes the following basic claims:…”

Section: Predictive Features In Case-based Modelsmentioning

confidence: 99%

Case-based learning: Predictive features in indexing

et al. 1994

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“…Many early case-based reasoners, such as Persuader [34], Mediator [21], and CAS [38], used a content-addressable dynamic memory [31] based on the CYRUS program [19]. As a byproduct of indexing new cases in memory, structures called memory organization packets (MOPs) [31] are created by this type of memory.…”

Section: Origin Of Context-mediated Behaviormentioning

confidence: 99%

Context-Mediated Behavior

Turner¹

2017

MUC

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ABSTRACT. All effective agents are context-sensitive. This is true for biological agents, and it must also be true for artificial agents if they are to succeed. Context-mediated behavior (CMB) is one approach to endowing artificial agents with context-sensitive, context-appropriate behavior. It relies on explicitly representing contexts -important classes of situations -as contextual schemas and associating with them all the relevant knowledge about that context. The agent can then use these c-schemas to assess its context and their knowledge to behave appropriately. In this paper, we provide an overview of CMB, review the past work on it, discuss what we have learned from more than twenty years of working on the problem, and describe where we are and where we are going with CMB.

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“…Thus, CBR has coped with analytic tasks in systems devoted to prediction [51][52], fault [53][54] and medical diagnosis [55], legal reasoning [56], mediation [57], negotiation [58] [19], tutoring [59] and customer service support [60] [61]. In addition, CBR systems have also tackled synthetic tasks in systems that work in the domains of design [62][63], planning [64] and configuration [65].…”

Section: Temporal Bounding Agent Reasoningmentioning

confidence: 99%