Arrow Research search

Author name cluster

B. Chandrasekaran

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

13 papers
1 author row

Possible papers

13

JAIR Journal 2010 Journal Article

A Constraint Satisfaction Framework for Executing Perceptions and Actions in Diagrammatic Reasoning

  • B. Banerjee
  • B. Chandrasekaran

Diagrammatic reasoning (DR) is pervasive in human problem solving as a powerful adjunct to symbolic reasoning based on language-like representations. The research reported in this paper is a contribution to building a general purpose DR system as an extension to a SOAR-like problem solving architecture. The work is in a framework in which DR is modeled as a process where subtasks are solved, as appropriate, either by inference from symbolic representations or by interaction with a diagram, i.e., perceiving specified information from a diagram or modifying/creating objects in a diagram in specified ways according to problem solving needs. The perceptions and actions in most DR systems built so far are hand-coded for the specific application, even when the rest of the system is built using the general architecture. The absence of a general framework for executing perceptions/actions poses as a major hindrance to using them opportunistically -- the essence of open-ended search in problem solving. Our goal is to develop a framework for executing a wide variety of specified perceptions and actions across tasks/domains without human intervention. We observe that the domain/task-specific visual perceptions/actions can be transformed into domain/task-independent spatial problems. We specify a spatial problem as a quantified constraint satisfaction problem in the real domain using an open-ended vocabulary of properties, relations and actions involving three kinds of diagrammatic objects -- points, curves, regions. Solving a spatial problem from this specification requires computing the equivalent simplified quantifier-free expression, the complexity of which is inherently doubly exponential. We represent objects as configuration of simple elements to facilitate decomposition of complex problems into simpler and similar subproblems. We show that, if the symbolic solution to a subproblem can be expressed concisely, quantifiers can be eliminated from spatial problems in low-order polynomial time using similar previously solved subproblems. This requires determining the similarity of two problems, the existence of a mapping between them computable in polynomial time, and designing a memory for storing previously solved problems so as to facilitate search. The efficacy of the idea is shown by time complexity analysis. We demonstrate the proposed approach by executing perceptions and actions involved in DR tasks in two army applications.

PDF Details DOI

AAAI Conference 2006 Conference Paper

Multimodal Cognitive Architecture: Making Perception More Central to Intelligent Behavior

  • B. Chandrasekaran

I propose that the notion of cognitive state be broadened from the current predicate-symbolic, Language-of-Thought framework to a multi-modal one, where perception and kinesthetic modalities participate in thinking. In contrast to the roles assigned to perception and motor activities as modules external to central cognition in the currently dominant theories in AI and Cognitive Science, in the proposed approach, central cognition incorporates parts of the perceptual machinery. I motivate and describe the proposal schematically, and describe the implementation of a bi-modal version in which a diagrammatic representation component is added to the cognitive state. The proposal explains our rich multimodal internal experience, and can be a key step in the realization of embodied agents. The proposed multimodal cognitive state can significantly enhance the agent’s problem solving.

PDF Details

AIIM Journal 1989 Journal Article

‘Deep’ models and their relation to diagnosis

  • B. Chandrasekaran
  • J.W. Smith
  • Jon. Sticklen

In this paper we distinguish between deep models in the sense of scientific first principles and deep cognitive models where the problem solver has a qualitative symbolic representation of the system or device that accounts for how the system ‘works’. We analyze diagnostic reasoning as an information processing task, identifying the generic types of knowledge (and reasoning) needed for the task to be performed adequately. If these are available, an integrated collection of generic problem solvers can produce a diagnostic conclusion. The need for deep or causal models arises when some or all of these types of knowledge are missing in the problem solver. We provide a typology of different knowledge structures and reasoning processes that play a role in qualitative or functional reasoning and elaborate on functional representations as deep cognitive models for some aspects of causal reasoning in medicine.

Details DOI

KER Journal 1988 Journal Article

An answer to commentators on the paper “Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples”

  • B. Chandrasekaran

I thank the commentators for their time, and generally positive remarks on the promise of the task-specific approach, in particular the generic task (GT) proposal. Johnson and Zualkernan would like a methodology for mapping domain knowledge onto one or more generic tasks so as to solve problems efficiently. This stage of problem and domain analysis in which the kind of reasoning that goes on needs to be analysed in a vocabulary of generic tasks is very important, and in our laboratory we identify this as the epistemic analysis stage. For specific classes of problems we have developed guidelines on how to perform this mapping. For example, Bylander and Smith (Bylander and Smith, 1985) describe a set of criteria and guidelines for mapping medical knowledge into CSRL-like structures for diagnostic reasoning. Similarly Brown (1984) describes criteria for mapping design knowledge into DSPL-like structures.

Details DOI

KER Journal 1988 Journal Article

Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

  • B. Chandrasekaran

Abstract The level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.

Details DOI

AAAI Conference 1987 Conference Paper

Data Validation during Diagnosis: A Step beyond Traditional Sensor Validation

  • B. Chandrasekaran

A well known problem in diagnosis is the difficulty of providing correct diagnostic conclusions in light incorrect or missing data. Traditional approaches to solving this problem, as typified in the domains of various complex mechanical systems, validate data by using various kinds of redundancy in sensor hardware. While such techniques are useful, we propose that another level of redundancy exists beyond the hardware level, the redundancy provided by expectations derived during diagnosis. That is, in the process of exploring the space of possible malfunctions, initial data and intermediate conclusions set up expectations of the characteristics of the final answer. These expectations then provide a basis for judging the validity of the derived answer. We will show how such expectation- based data validation is a natural part of diagnosis as performed by hierarchical classification expert systems.

PDF Details

AAAI Conference 1982 Conference Paper

Deep Versus Compiled Knowledge Approaches to Diagnostic Problem-Solving

  • B. Chandrasekaran

In this paper we argue that given a body of underlying knowledge that is relevant to diagnostic reasoning in a medical domain, it is possible to create a diagnostic structure which has all the relevant aspects of the underlying knowledge "compiled" into it in such a way that all the diagnostic problems in its scope can be solved efficiently, without, generally speaking, any need to access the underlying structures. We indicate what such a diagnostic structure might look like by reference to our medical diagnostic system MDX. We also analyze the role of these knowledge structures in providing explanations of diagnostic reasoning.

PDF Details

AIJ Journal 1981 Journal Article

A theory of spatio-temporal aggregation for vision

  • Bruce E. Flinchbaugh
  • B. Chandrasekaran

A theory of spatio-temporal aggregation is proposed as an explanation for the visual process of grouping together elements in an image sequence whose motions and positions have consistent interpretations as the retinal projections of a coherent or isolated cluster of ‘particles’ in the physical world. Assumptions of confluence and adjacency are made in order to constrain the infinity of possible interpretations to a computationally more manageable domain of plausible interpretations. Confluence and adjacency lead to the derivation of specific rules for grouping which permit the appropriate aggregation of rigid and quasi-rigid objects in motion and at rest under a variety of conditions. The theory is reconciled with existing computational theories of vision so as to complement them, and to provide a useful link in the continual abstraction of visual information.

Details DOI
v2026.09.13