Could you begin with an insight into your background?

I became interested in experience-based qualitative decision making in the early 1990s. The outcome was my doctoral thesis on ‘Diagnostic Case-Based Reasoning’ (ie. using experiential knowledge to diagnose and repair industrial equipment and processes such as extruders), which led to my involvement as a founding member of CaseBank, a start-up in Canada.

It turns out that knowledge required to build and maintain experience-based systems is found within largely unstructured texts. This led me to become deeply interested in the topic, which is now popularly known as text mining and knowledge discovery, and is a thriving area of research. I started a company in 1998 called Terus Technologies, which was dedicated to the extraction and discovery of knowledge from text for use in knowledge-based decision support systems. Unable to sustain Terus only with private funds, I moved to the US in 2000 and continued to research various topics in knowledge-based decision support, machine-learning and knowledge discovery. However, I felt that the industrial research environment I operated within was incompatible with my vision of agile multidisciplinary research for technical innovation and product development.

With two start-up experiences under my belt, in 2005 I was ready to start Knexus Research. My strength derives from the unique combination of an extensive background in engineering, information technology, computer science, product development and business. This enables me to spot scientifi c and technological trends long before they become popular, and to interrelate seemingly disparate scientifi c topics in unprecedented ways to develop innovative and powerful technologies for enterprise-grade applications.

Can you introduce Knexus Research?

I founded Knexus to research and develop artifi cial intelligence (AI) and autonomous

systems approaches for producing innovative, affordable, application products that measurably improved the decision making and operating performance of our clients and users. Knexus is a small business, a professionally managed high-technology lab for rapidly developing, evaluating and showcasing advanced technology concepts.

Our members thrive on methodically breaking down a complex and diffi cult problem, developing AI algorithms and solutions, and putting them together into systems that advance the state of the art. These may range from fi guring out how to make virtual game bots that learn new plays using machine learning to deciphering cognitive models and processes for understanding spatial language for acting in uncertain environments. The same could be used to develop operator consoles for unmanned systems that are robust, intuitive and natural.

What are the Corporation’s proudest accomplishments?

Over the course of Knexus’ nine years of operation, there have been several memorable accomplishments. For instance, our work on federated answering systems for intelligence analysts sponsored by Intelligence Advanced Research Products Activity (IARPA) was showcased to the US Congress. We also won the best overall video award in the fi rst Association for the Advancement of Artifi cial Intelligence (AAAI) video competition.

This year we are on target to publish more than 10 technical articles in peer-reviewed conferences and journals; a signifi cant and proud milestone for us that rivals top university labs of comparable size. Recently, we were awarded a patent on our algorithm to accurately place objects in a 3D world based on natural language commands. On top of these is the release of two products; an autonomy simulator and testbed called eBotworks, which is used by other researchers to study human factors such as trust in autonomous systems,

and Administrative Information Management System (AIMS) an enterprise workfl ow management system used by an organisation with over 300 scientists and technical professionals. Another proud occasion for me is to frequently celebrate the members who demonstrably share in the company’s commitment to long-term interdisciplinary and challenging research and innovation.

How can challenges encountered by mine countermeasures (MCM) missions be defi ned?

MCM mission commanders and planners face two incredibly diffi cult challenges in accomplishing their missions: fi rst, unknown conditions such as mine threats, sea fl oor conditions and unpredictable and frequently changing operating conditions such as non-negotiable high sea states, equipment failures, and rapid crew rotations; and second, accomplishing mission objectives under severe time and resource constraints. What makes this even more challenging is a lack of decision support and planning tools that help planners manage this uncertainty and work around them to accomplish their mission objective. This is comparable to going around without a GPS in a largely unknown part of a city whose road and traffi c conditions are chaotic.

How can these hurdles be overcome?

These can be overcome by a system that is like a MCM Mission Planner’s GPS. Such a system would use carefully selected AI tools that do the following: watch for conditions that have the potential to disrupt operations (equivalent to a traffi c jam); alert the planners and propose adjustments to the plan (suggest routes with side roads that avoid the traffi c jam) that work around such conditions to keep the mission on track; and repeat this process until we accomplish the objective. This is what we are designing Cognitive Architecture for MCM Mission Planning, Execution, and Replanning (CARPE) to do.

Knexus Research Corporation provides intelligent systems R&D services to government and industry. President and founder Dr Kalyan Gupta explains how the Corporation was started, and describes its current research for the Offi ce of Naval Research, USA

Achieving artifi cial intelligence

KALYAN GUPTA MATT MOLINEAUX PHILIP MOORE

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THE UNITED STATES Navy carries out mine countermeasures (MCM) missions to clear mines from designated areas in preparation for other military operations. This is a dynamic, risky and uncertain operating environment, where MCM scheduling decisions must be informed by data gathered from all operating units. Currently, the planning and coordination of various equipment and teams is manpower intensive and potentially error-prone. Time constraints and the rapid pace of changes restrict personnel to examination of only a limited set of schedule alternatives, possibly resulting in missed opportunities and inefficient schedules. There is therefore a need for decision support that utilises the most up-to-date situation data – including, for example, changes in sea state, visibility and weather – to reschedule missions for increased resource utilisation and reduced personnel risk.

The greatest problem with MCM operations is that analysing the impact of situation changes and rescheduling ongoing tasks requires simultaneously juggling many factors that are difficult to track manually. This is further compounded by rapid and unforeseen changes that form a disproportionately large part of each

mission. Automated tools that facilitate mission rescheduling in response to situation changes would enable more effective missions.

CARPE

Dr Kalyan Gupta, founder and President of Knexus Research Corporation, an artificial intelligence (AI) R&D company, is leading his team comprising AI planning expert Matt Molineaux, architect Philip Moore, and senior engineer Bryan Auslander to develop such a decision support tool for MCM missions. They are developing the Cognitive Architecture for MCM Mission Planning, Execution, and Replanning (CARPE) to support the planning staff’s tasks by monitoring the ongoing situation for unexpected changes that have the potential to disrupt a mission. When such changes occur, CARPE performs root cause analysis. It then automatically generates schedule repair recommendations that compensate for and avoid future disruptions. CARPE is being designed to complement and interoperate with an already deployed MCM system, MEDAL-EA, which has no automated rescheduling capabilities, thereby providing the operations personnel with a seamless, one-stop, decision-support experience.

CARPE’s functional architecture is inspired by those of cognitive systems, which typically include components for perception, analysis, planning, action and memory. CARPE includes a situation monitor and analyser to identify disruptive events, a root cause analyser, a schedule repair component and a memory of various detection and analysis models. The planned architecture will interoperate with MEDAL-EA to obtain reported situation data from it and send back revised schedules for operating units as appropriate.

Included in this, Gupta and colleagues proposed a number of innovative solutions. They developed a conversational approach to root cause analysis based on a sophisticated algorithm called DiscoverHistory, previously developed by Matt Molineaux. This approach engages a user in a conversation (ie. a question and answer session) to help them step through a series of tests and isolate the root cause of a detected discrepancy. For instance, CARPE might detect that too few mine-like objects than expected have been found during a detection task. This could be caused by a number of factors such as bad environment knowledge, a faulty sensor

Mine sweepingKnexus Research Corporation, based in National Harbor, Maryland, has been creating a decision support system for mine countermeasures missions to ensure more efficient mission scheduling by US Navy personnel

DR KALYAN GUPTA

WWW.INTERNATIONALINNOVATION.COM 69

A COGNITIVE ARCHITECTURE FOR NAVAL MINE COUNTERMEASURES

OBJECTIVE

To build powerful, reusable, artifically intelligent and autonomy modules that are transparent, testable and configurable.

KEY COLLABORATORS

Philip Moore, Software Architect, Knexus Research Corporation • Matthew Molineaux, Senior Research Scientist, Knexus Research Corporation

FUNDING

Office of Naval Research (Code 321), Small Business Innovation Research (SBIR)

Contract number: N00014-11-C-0422

CONTACT

Dr Kalyan Gupta President and founder

163 Waterfront Street Suite 440 National Harbor, Maryland, 20745 USA

T +1 855 569 7373 x 700 E kalyan.gupta@knexusresearch.com

www.knexusresearch.com

DR KALYAN GUPTA is an artificial intelligence (AI) scientist and entrepreneur with over 20 years of innovation and product development experience. A veteran of two start-ups, Gupta acquires funds, leads and conducts intelligent systems research to develop advanced software prototypes, and transitions them into commercially viable intelligent systems. He has pioneered AI techniques on a wide range of topics and developed intelligent systems applications for activity and threat recognition, multimodal interaction with virtual agents, adaptive web-service brokering, communications prioritisation and conversational diagnosis. Gupta has over 40 publications in reputed journals and peer-reviewed conferences on these topics. He has four published patents and two more are pending. Gupta has led or contributed to the development of 19 scientific and research prototypes and six products. Products from his efforts are in use in manufacturing companies such as DuPont, Bombardier, Information Extraction companies such as Cymfony, Inc and decision support companies such as Tapestry Solutions. He has a PhD in Business Administration from McMaster University, Canada and a Master’s degree in Industrial Engineering from Indian Institute of Technology, Kharagpur, India.

or improper deployment. It can infer these potential causes and suggest diagnostic tests to narrow down the possibilities; a human user interacting with CARPE can then perform the test to gain more information (eg. check whether the sensor is working and or properly calibrated). This diagnostic capability relies on DiscoverHistory, a unique and powerful algorithm that is very different from and superior to standard fault isolation algorithms. It utilises planning knowledge to look back in time and identify the most plausible explanation that links a series of reported observations. In doing so, it can not only find pre-specified faults but also offending events that were not anticipated by engineers, providing a much wider coverage of analysis than previously possible. Gupta describes the potential of CARPE: “We believe it will enable MCM planners to more effectively respond to operating disruptions by helping them objectively explore a much larger range of options than they can manually”.

CLOSR TO SUCCESS

Another innovative answer to the MCM mission execution problem was to improve the rescheduling process. Rescheduling expects to make slight adjustments to the original schedule based on identification of the root cause of disruptions. Gupta explains the difficulties associated with using conventional automated planning approaches for rescheduling: “Automated replanning is problematic as [such algorithms may] suggest wild and impractical changes to the plan”. Gupta and colleagues have therefore created an alternative that avoids such changes: hybrid Case-based Local hierarchical task network Schedule Repair (CLOSR).

CLOSR repairs an existing schedule rather than a much more computationally expensive replan approach. Unusually, it works on second-order knowledge to make repair decisions (ie. knowledge that describes how to manipulate knowledge). CLOSR utilises a memory of schedule repair strategies (ie. a case base) obtained from MCM experts to identify the best case containing an approach to fix the broken schedule. It then uses the information from the case to automatically repair the existing schedule. This enables it to perform fast, minimal and automated changes to the schedule that are consistent with the expectations of a MCM planner.

MISSION WALKTHROUGH

The next task for the programme was to tackle a simulated mission scenario, provided by the Naval Surface Warfare Centre, with a subject matter expert (SME) controlling it. This creates a hypothetical amphibious assault on a beach, as well as details on local climate, environment and expected location of mines. The SME acting as the CARPE user was expected to evaluate the accuracy, validity, transparency, utility and usability of the new software and overall they found that CARPE was a usable and useful addition, illustrating the success of the program. “CARPE is unique and unprecedented in its abilities in that it uses knowledge from

MCM planning doctrine, and experience and strategies of MCM mission planners to schedule repair when disruptive conditions are detected,” explains Gupta. More evaluation is needed, but CARPE evidently has the correct recipe for success.

Gupta and colleagues have also identified various areas for refinement and improvement, including ranking the tests proposed by conversational root cause analysis, improving CLOSR’s ability to infer mission commanders’ intentions and introducing user feedback mechanisms on schedules suggested by CARPE. Full MEDAL-EA integration is also necessary.

BECOMING ARTIFICIALLY INTELLIGENT

Alongside this work for the US Department of Defense, Gupta and colleagues at Knexus have been working on other AI systems. eBotworks represents Gupta’s vision for autonomous AI agents, constituting a virtual testbed for future AI R&D. The project was started with Small Business Innovation Research (SBIR) funding from the Office of Naval Research. The ‘bots’ can be controlled in a variety of simulated indoor and outdoor environments using user-issued chat commands, and researchers can extend eBotworks with their own autonomy and reasoning algorithms, using the simulation software to measure and compare algorithm performance.

Knexus’ long-term research aims revolve around the idea of autonomous systems and cognitive engineering. “This is the process of engineering learning and reasoning modules and the knowledge that goes with them, making them easily accessible and affordable for rapid AI systems exploration and design,” Gupta explains. Knexus therefore will aid the R&D of novel AI systems, propelling cognitive engineering forward.

Knexus’ long-term research

aims revolve around the idea

of autonomous systems and

cognitive engineering

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INTELLIGENCE