&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

The real-world is a medley of multisensory information and so are our experiences, memories, and responses. As embodied beings, we respond to the information endogenously and in ways derived from self-defining factors. Thus inspired, we attempt formalization of data-structures to facilitate generation of system-bespoke comprehension-granules of the real-world. The conceptualized structures encapsulate multisensory inputs (sourced from the real-world or memories), intrinsic and deliberate emotions, messages (bearing intermittent process-results, queries, multimodal data, etc.) across system modules and memory units, and sensorimotor responses to the inputs. The structural-schematics are anthropomorphic. These variable-length constructs are theoretically platform-independent, support genericity across data-modality and information-inclusion, and provide for representation of novel sensory-data. An epigenome-styled header node for the afferent data-units provides for the activation of intuitive 'fight-flight' behavior. The documentation includes a flow-graph, depicting the translation of information across the data-structures and the different ways of thinking while interpreting a real-world scene or a mind-generated event. Applicability of the structures has been analyzed in the context of comprehension in an embodied mind-machine framework and other similar architectures. Studies herein target contribution to the design of generally intelligent man-machine symbiotic systems. © 2016 IEEE.

Department of Computer Science & Engineering

World University root node

IEEE Transactions on Cognitive and Developmental Systems

Data-Structures for Multisensory Information Processing in an Embodied Machine-Mind

Natural language provides a rich combinatorial mechanism for encoding meanings - a finite set of words can express an unbounded number of thoughts. Framed in 2015 to extend the purpose of Zadeh's Z-numbers, a Z*-number is a perceptual symbol of the meaning of a natural language expression and consequently mentalese – or internal speech. This article, through decomposition of the Z*-macro-parameters into its atomic constituents, presents a model for the endogenous arousal of thoughts during empathetic, bespoke comprehension of the real-world. Based on Minsky's Society of Mind, the framework is founded on the assimilation of multimodal experiences, a sense of ‘unified self’ and its derivatives (choice, interest, curiosity, etc.), objective and subjective components of knowledge, commonsense, and attention dynamics over a real-world scenario. The model attempts emulation of slow and fast thinking, instinctive reactions, learning, deliberation, reflection and self-conscious decisions. The design has been validated against human responses, and aims to contribute to the development of autonomous artificial systems for man-machine symbiosis. © 2017 Elsevier Inc.

Information Sciences

A computational model for the endogenous arousal of thoughts through Z*-numbers

This article documents our efforts on machine-mind design for realworld, understanding. It includes: a) enumeration of essential macroarchitectural elements and working principle of our machine-mind framework, b) definition of a novel paradigm, the Z*-numbers, for encapsulation of essential objective and subjective elements of real-world information, and c) a detailed example illustrating the dynamics of the framework and the role of the Z*-numbers. Our work is based on Minsky's theories of cognition, Zadeh's Z-number philosophy, and human brain processes of comprehension. The ideas herein, envision man-machine symbiosis and aim to contribute to the synthesis of an empathetic artificial mind. Among others, the primary novelty in our design lies in the emphasis on representation of the machine-self and its derivates towards emulation of bespoke real-world comprehension. © 2017 by World Scientific Publishing Co. Pte. Ltd.

Pattern Recognition and Big Data

A machine-mind architecture and Z*-numbers for real-world comprehension

Guided by a polymath approach—encompassing neuroscience, philosophy, psychology and computer science, this article describes a novel ‘cognitive’ computational mind framework for text comprehension in terms of Minsky’s ‘Society of Mind’ and ‘Emotion Machine’ theories. Observing a top-down design method, we enumerate here the macrocosmic elements of the model—the ‘agencies’ and memory constructs, followed by an elucidation on the working principles and synthesis concerns. Besides corroboration of results of a dry-run test by thoughts generated by random human subjects; the completeness of the conceptualized framework has been validated as a consequence of its total representation of ‘text understanding’ functions of the human brain, types of human memory and emulation of the layers of the mind. A brief conceptual comparison, between the architecture and existing ‘conscious’ agents, has been included as well. The framework, though observed here in its capacity as a text comprehender, is capable of understanding in general. A cognitive model of text comprehension, besides contributing to the ‘thinking machines’ research enterprise, is envisioned to be strategic in the design of intelligent plagiarism checkers, literature genre-cataloguers, differential diagnosis systems, and educational aids for children with reading disorders. Turing’s landmark 1950 article on computational intelligence is the principal motivator behind our research initiative. © 2015, Springer Science+Business Media Dordrecht.

Natural Computing

Text comprehension and the computational mind-agencies

Envisaging a futuristic environment of man-machine and machine-machine synergy, this article documents our research on the augmented Z-numbers, the Z∗-numbers, for machine-perception encapsulation. The Z∗-numbers have been envisioned as operands of endogenous machine-mind processes underlying bespoke comprehension of the real world. Besides information-certainty, as in a Z-number, a Z∗-number incorporates context, time and affects as essential factors of subjectivity representation. We have proposed: (a) definitions for certainty and affect parameters - arising out of socio-cultural influences on machine-knowledge, (b) a Z∗-number based rudimentary procedure for natural-language comprehension emulation, and (c) primitive perception-operators for 'machine-mentalese' simulation using Z∗-number information-equivalents. Our work draws from non-symbolic theories of cognition and 'mindfulness', human-mind processes - studied through behavioral experiments, and theories of the 'self' and 'qualia'. The article includes detailed discussions of these experiments and consequent insights, analysis of a theoretical run-through of the defined procedure, and correspondence-studies between the Z∗-number paradigm and philosophies of the self. Our research raises questions on cognitive biases and autogenous mind-processes that highlight crucial practical challenges in the current realization of a synthetic-mind. All ideas herein aim to contribute to studies on the 'self' and its machine-embodiment for the synthesis of an empathetic machine-mind. © 2015 Elsevier Inc.

Z∗-numbers: Augmented Z-numbers for machine-subjectivity representation

This article is centred on two themes. The first is the extension of Zadeh’s basic Z-numbers into a tool for level-2 Computing With Words (CWW) and consequently subjective natural language understanding. We describe an algorithm and new operators (leading to complex or spectral Z-numbers), use them to simulate differential diagnosis, and highlight the inherent strengths and challenges of the Z-numbers. The second theme deals with the design of a, Minsky’s Society of Mind based, natural language comprehending machine-mind architecture. We enumerate its macro-components (function modules and memory units) and illustrate its working mechanism through simulation of metaphor understanding; validating system outputs against human-comprehension responses. The framework uses the aforementioned new Z-number paradigm to precisiate knowledge-frames. The completeness of the conceptualized architecture is analyzed through its coverage of mind-layers (Minsky) and cerebral cortex regions. The research described here draws from multiple disciplines and seeks to contribute to the cognitive-systems design initiatives for man-machine symbiosis. © Springer International Publishing Switzerland 2015

Studies in Fuzziness and Soft Computing

On z-numbers and the machine-mind for natural language comprehension

The Z-number, proposed by Zadeh in the year 2011, is a new fuzzy-theoretic approach to the Computing With Words (CWW) paradigm. It aspires to capture the uncertainty of information conveyed by a sentence, and serve as a model for the precisiation and linguistic summarization of a natural language statement. The Z-number thereby, lends a new dimension to CWW - uniting CWW with Natural Language Processing (NLP). This article is an illumination upon our exploration of the Z-number approach to CWW. Here, we enlist the probable contributions of the Z-number to CWW, present our algorithm for CWW using the Z-number, and describe a simulation of the technique with respect to a real-life example of CWW. In the course of the simulation, we extend the interpretation of the set-theoretic intersection operator to evaluate the intersection of perceptions and discover some of the challenges underlying the implementation of the Z-number in the area of CWW. © 2013 Springer-Verlag Berlin Heidelberg.

The Z-number enigma: A study through an experiment

In the course of comprehension, the human brain makes astounding inferences despite insufficient and ambiguous data - the data mostly being 'words' that frame sentences. Can this neural process of "abstraction of the relevant" be sufficiently modeled such that the computer may be induced to perform the same? This article is our response to this very question; a treatise on our attempt at engineering the basis of such a model, or rather, a methodology of 'relevance cognition'. Drawing inspiration from the way children learn languages, we propose here a corpus and entropy-based methodology that follows a subjectivist approach to granulate and identify statements of consequence in a natural language sample, all within the periphery of a particular context. Besides promoting language grasping abilities of a machine-through simulation of the intuitive process of relevant-information segregation-the methodology aims at reducing overall text-processing costs. The suggested scheme considers 'conceptual keywords' to be the basis of sentential understanding and utilizes the principles of Bayes' conditional probability and Shannon's entropy theorem. Experimental results have been provided to substantiate our logic. Though this article has been formulated over the backdrop of the Z-number approach to computing with words (CWW), it nevertheless applies to research areas in natural language processing (NLP) like text summarization, semantic disambiguation and concept graph generation. © 2013 Elsevier B.V. All rights reserved.

Fulltext

Theoretical Computer Science

Context granulation and subjective-information quantification

The Z-number is a new fuzzy-theoretic concept, proposed by Zadeh in 2011. It extends the basic philosophy of Computing With Words (CWW) to include the perception of uncertainty of the information conveyed by a natural language statement. The Z-number thus, serves as a model of linguistic summarization of natural language statements, a technique to merge human-affective perspectives with CWW, and consequently can be envisaged to play a radical role in the domain of CWW-based system design and Natural Language Processing (NLP). This article presents a comprehensive investigation of the Z-number approach to CWW. We present here: a) an outline of our understanding of the generic architecture, algorithm and challenges underlying CWW in general; b) a detailed study of the Z-number methodology - where we propose an algorithm for CWW using Z-numbers, define a Z-number based operator for the evaluation of the level of requirement satisfaction, and describe simulation experiments of CWW utilizing Z-numbers; and c) analyse the strengths and the challenges of the Z-numbers, and suggest possible solution strategies. We believe that this article would inspire research on the need for inclusion of human-behavioural aspects into CWW, as well as the integration of CWW and NLP.