My work focuses on building computational agents that assist people in managing their activities in environments in which tempo and complexity outstrip people’s cognitive capacity,such as in coordinating rescue teams in the aftermath of a disaster, or in helping people with dementia manage their everyday lives. A critical challenge faced in such environments is not only that individuals must factor complicated constraints into deciding how and when to act on their own goals, but also that their decisions are further constrained by choices made by others with whom they interact, such as between cooperating teams in disaster relief or between patients and caregivers in an assisted-living facility. An additional challenge in such situations is that the interests of individuals, such as privacy and autonomy, along with slow, costly, uncertain,or otherwise problematic communication may further limitindividuals’ abilities to work together. My work assumes that a computational agent is associated with each individual, and that these agents will work together efficiently to manage individual and joint activities, while maintaining autonomy and privacy to the extent possible.

A plan with rich control structures like branches and loops can usually serve as a general solution that solves multiple planning instances in a domain. However, the correctness of such generalized plans is non-trivial to define and verify, especially when it comes to whether or not a plan works for all of the infinitely many instances of the problem. In this paper, we give a precise definition of a generalized plan representation called an FSA plan, with its semantics defined in the situation calculus. Based on this, we identify a class of infinite planning problems, which we call one-dimensional (1d), and prove a correctness result that 1d problems can be verified by finite means. We show that this theoretical result leads to an algorithm that does this verification practically, and a planner based on this verification algorithm efficiently generates provably correct plans for 1d problems.

There is a huge and growing number of systems that depend on batteries for power supply, ranging from small mobile devices to large high-powered systems such as electrical substations. In most of these systems, there are significant user-benefits or engineering reasons to base the supply on multiple batteries, with load being switched between batteries by a control system. The key to efficient use of multiple batteries lies in the design of effective policies for the management of the switching of load between them. This paper describes work in which we show that automated planning can produce much more effective policies than other approaches to multiple battery load management in the literature.

In this paper we describe a software assistant agent that can proactively assist human users situated in a time-constrained environment to perform normative reasoning--reasoning about prohibitions and obligations--so that the user can focus on her planning objectives. In order to provide proactive assistance, the agent must be able to 1) recognize the user's planned activities, 2) reason about potential needs of assistance associated with those predicted activities, and 3) plan to provide appropriate assistance suitable for newly identified user needs. To address these specific requirements, we develop an agent architecture that integrates user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. This paper presents the agent architecture and discusses practical applications of this approach.

In today's services driven economic environment, it is imperative for organizations to provide better quality service experience to differentiate and grow their business. Customer satisfaction (C-SAT) is the key driver for retention and growth in Retail Banking. Wait time, the time spent by a customer at the branch before getting serviced, contributes significantly to C-SAT. Due to high footfall, it is improbable to improve the wait time of every customer walking in the branch. Therefore, banks in developing countries are strategically looking to segment its customers and services and offer differentiated QoS based service delivery. In this work, we present a system for customer segmentation, and scheduling based on historic value of the customer and characteristics of current service request. We describe the system and give mathematical formulation of the scheduling problem and the associated heuristics. We present results and experience of deployment of this solution in multiple branches of a leading bank in India.

This requires, for Goal-driven autonomy (GDA) is a reflective model example, experts to anticipate what discrepancies can occur, of goal reasoning that controls the focus of an identify what goals can be formulated, and define their agent's planning activities by dynamically relative priority. However, few techniques have been resolving unexpected discrepancies in the world investigated for learning this knowledge, and those that do state, which frequently arise when solving tasks in learn only goal formulation knowledge (Weber et al. 2010; complex environments. GDA agents have Powell et al. 2011). This can be problematic; while these performed well on such tasks by integrating agents may perform well in simple environments, in others a methods for discrepancy recognition, explanation, domain expert might not know the (state) expectations for goal formulation, and goal management. However, executing every action in every state, nor which goal should they require substantial domain knowledge, be pursued to resolve every possible discrepancy, or even including what constitutes a discrepancy and how the space of all possible discrepancies.

Robots must perform tasks efficiently and reliably while acting underuncertainty. One way to achieve efficiency is to give the robot common-sense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertaintyin the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particular environment. Our second contribution is a continual planning system which is able to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on object search tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour.

This paper presents a new Monte-Carlo search algorithm for very large sequential decision-making problems. We apply non-linear regression within Monte-Carlo search, online, to estimate a state-action value function from the outcomes of random roll-outs. This value function generalizes between related states and actions, and can therefore provide more accurate evaluations after fewer rollouts. A further significant advantage of this approach is its ability to automatically extract and leverage domain knowledge from external sources such as game manuals. We apply our algorithm to the game of Civilization II, a challenging multi-agent strategy game with an enormous state space and around 10^21 joint actions. We approximate the value function by a neural network, augmented by linguistic knowledge that is extracted automatically from the official game manual. We show that this non-linear value function is significantly more efficient than a linear value function, which is itself more efficient than Monte-Carlo tree search. Our non-linear Monte-Carlo search wins over 78% of games against the built-in AI of Civilization II.

Deep space missions are characterized by severely constrained communication links. To meet the needs of future missions and increase their scientific return, future space systems will require an increased level of autonomy on-board. In this work, we propose a comprehensive approach to on-board autonomy relying on model-based reasoning, and encompassing many important reasoning capabilities such as plan generation, validation, execution and monitoring, FDIR, and run-time diagnosis. The controlled platform is represented symbolically, and the reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of our framework, implemented within an on-board Autonomous Reasoning Engine. We have evaluated our approach on two case-studies inspired by real-world, ongoing projects, and characterized it in terms of reliability, availability and performance.

Human environments are challenging for robots, which need to be trainable by lay people and learn new behaviours rapidly without disrupting much the ongoing activity. A system that integrates AI techniques for planning and learning is here proposed to satisfy these strong demands. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many alternatives of cause-effect explanations are evaluated in parallel, and the most successful ones are used to generate the operators. The success of a cause-effect explanation is evaluated by a probabilistic estimate that compensates the lack of experience, producing more confident estimations and speeding up the learning in relation to other known estimates. The system operates without task interruption by integrating in the planning-learning loop a human teacher that supports the planner in making decisions. All the mechanisms are integrated and synchronized in the robot using a general decision-making framework. The feasibility and scalability of the architecture are evaluated in two different robot platforms: a Stäubli arm, and the humanoid ARMAR III.