Deepak Khemani

This paper applies and extends the results from [22] on agent-update frames and their logic. Several interesting examples of actions for forgery and deception, agent-upgrade and agent-downgrade are considered. Going on from the earlier paper, a second interesting children’s story is modelled using these ideas. A dynamic epistemic logic is defined with all these actions and provided with a complete axiomatization. Decision procedures for satisfiability and model checking follow. A planning-oriented approach is also discussed.

This paper focuses on the application of CBR to soil analysis from chromatograms. The shape, size and colour of the chromatogram image are hypothesized to contain important information of the mineral content in the soil. Since chromotogram preparation is cheaper than chemical analysis the goal is to predict the nutrients from the chromatogram image features in the future rather than by direct chemical analysis. The method proposed in this paper will be new, as the current process of chemical analysis of soil is done manually, which is an expensive, time consuming and laborious process. This method of analysis will benefit farmers all across the globe, who are looking for innovative means to obtain their soil characteristics during the process of farming. In this application, the key assumption is that - similar chromatograms have similar soil properties. This paper focuses on the definition of similarity measure and determining the weight model for the feature set needed for the application. © Springer-Verlag Berlin Heidelberg 2008.

Search and recommender systems benefit from effective integration of two different kinds of knowledge. The first is introspective knowledge, typically available in feature-theoretic representations of objects. The second is external knowledge, which could be obtained from how users rate (or annotate) items, or collaborate over a social network. This paper presents a spreading activation model that is aimed at a principled integration of these two sources of knowledge. In order to empirically evaluate our approach, we restrict the scope to text classification tasks, where we use the category knowledge of the labeled set of examples as an external knowledge source. Our experiments show a significantly improved classification effectiveness on hard datasets, where feature value representations, on their own, are inadequate in discriminating between classes.

This paper presents use of a cost graph as a representation of a multi-floor building to enable the multi-floor autonomous navigation capability for a team of robot(s). A method for global path planning on this cost graph have been presented. A navigation stack provides a framework for building autonomous navigation capabilities. A navigation stack which enables use of the proposed approach for navigation in a multi-floor building and enables multi-robot operations has been detailed. The improvements provided by the proposed navigation stack over the existing ROS (Robot Operating System) navigation stack have been explained. A way to integrate multiple local path-execution nodes which can combine together to execute the planned global path has also been explained. The paper also demonstrates the reuse of existing ROS compliant source codes for implementation of the proposed navigation stack, thereby optimizing the use of proven and established technology. Further, the extensions to different components of the existing ROS navigation stack, definition of new ROS messages and action definitions, to enable interaction between the components of the stack has been explained. The paper concludes with a brief study on how the proposed stack can be used for multi-robot operations.

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.

Given the diversity and spatio-temporal scales of dynamic coastal processes, sampling is a challenging task for oceanographers. To meet this challenge new robotic platforms such as Autonomous Underwater Vehicle (AUV) are being increasingly used. For effective water sampling during a mission an AUV should be adaptive to its environment, which requires it to be able to identify these dynamic and episodic ocean features in-situ. We describe the use of Hidden Markov Models (HMM) as a feature detection model used onboard an AUV, an autonomous untethered robot. We show how to build an identification model from data collected during past missions. Then we show how the parameters of the HMM can be optimized using a Genetic Algorithm approach, from models trained with the Baum-Welch algorithm in the initial population. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop a clustering algorithm toward the outlined goal of building interpretable and reconfigurable cluster models; it works by generating rules with disjunctions and conditions on the frequencies of words, to decide on the membership of a document to a cluster. Each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. We show that our approach outperforms the unsupervised decision tree approach by huge margins. We show that the purity and f-measure losses to achieve interpretability are as little as 5% and 3% respectively using our approach. Copyright 2009 ACM.

The phrase artificial intelligence has become common in our current day discourse. Fuelled by successes in machine learning, and applications interacting with us in speech and natural language, many commentators have made a leap of faith that behind these successes is a thinking machine, and this has even stoked fears of machines overcoming humankind. In this two-part article, we look at how close we are to the original quest for creating “machines who think”. In the first part, we trace the evolution of mechanical computers and also the notion of the mind up to the era before digital computers appeared on the horizon.

The quality of a learning algorithm is characterized by the accuracy, stability and comprehensibility of the models it generates. Though ensembles produce accurate and stable classifiers, they are hard to interpret. In this paper, we propose a meta-learning method for ID3 that makes use of an ensemble for gaining accuracy and stability and yet produces a single comprehensible classifier. The main idea here is to generate additional examples at every stage of the decision tree construction process and use them to find the best attribute test. These new examples are classified using the ensemble constructed from the original training set. The number of new examples generated depends on the size of the input attribute space and the input attribute values of new examples are partially determined by the algorithm. Existing work in this area deals with the generation of a fixed number of random examples. Experimental analysis shows that our approach is superior to the existing work in retaining accuracy and stability gains provided by the ensemble classifier. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.

Process changes in manufacturing are often done by trial and error, even when experienced domain personnel are involved. This is mainly due to the fact that in many domains the number of parameters involved is large and there exists only a partial understanding of interrelationships between them. This paper describes a framework for keeping track of process change experiments, before they qualify as full cases. Process changes happen as a result of diagnosis done by the expert, following which some therapy is decided. The paper also presents an algorithm for diagnosis and therapy based on induction on discrimination trees constructed on specific views on the set of problem parameters.