This paper describes a mobile robotic assistant, developed to assist elderly individuals with mild cognitive and physical impairments, as well as support nurses in their daily activities. We present three software modules relevant to ensure successful human--robot interaction: an automated reminder system; a people tracking and detection system; and finally a high-level robot controller that performs planning under uncertainty by incorporating knowledge from low-level modules, and selecting appropriate courses of actions. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide reminders and guidance for elderly residents.

This paper describes Autominder, a cognitive orthotic system intended to help older adults adapt to cognitive decline and continue the satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Autominder achieves this goal by providing adaptive, personalized reminders of (basic, instrumental, and extended) activities of daily living. Cognitive orthotic systems on the market today mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, Autominder uses a range of AI techniques to model an individual's daily plans, observe and reason about the execution of those plans, and make decisions about whether and when it is most appropriate to issue reminders. Autominder is currently deployed on a mobile robot, and is being developed as part of the Initiative on Personal Robotic Assistants for the Elderly (the Nursebot project)

The majority of reminder systems are inflexible; reminders are issued at static, prespecified times. To be effective, cognitive orthotics should reason about what reminders should be issued and when. This paper describes the personalized cognitive orthotic (PCO), a system that uses plan-based reasoning to attain flexibility. PCO relies on local search techniques to generate high-quality reminder plans based on knowledge of the user's plans and her typical behavior. PCO is being developed in concert with other technologies aimed at improved plan management, including systems that update a user's plans and track action execution. We describe the PCO as it is implemented in the Nursebot application: where it provides timely and relevant reminders to elderly people who have cognitive decline that necessitates assistance in managing their daily activities.

The Internet provides access to a wealth of information. For any given topic or application domain there are a variety of available information sources. However, current systems, such as search engines or topic directories in the World Wide Web, offer only very limited capabilities for locating, combining, and organizing information. Mediators, systems that provide integrated access and database-like query capabilities to information distributed over heterogeneous sources, are critical to realize the full potential of meaningful access to networked information. Query planning, the task of generating a cost-efficient plan that computes a user query from the relevant information sources, is central to mediator systems. However, query planning is a computationally hard problem due to the large number of possible sources and possible orderings on the operations to process the data. Moreover, the choice of sources, data processing operations, and their ordering, strongly affects the plan c...

... an opportunity for the design of intelligent technology. This paper focuses on one type of assistive technology, cognitive orthotics, which can help people adapt to cognitive declines and continue satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Existing cognitive orthotics mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, we describe Autominder, a system we have designed that uses AI planning and plan management technology to carefully model an individual's daily plans, attend to and reason about the execution of those plans, and make flexible and adaptive decisions about when it is most appropriate to issue reminders. The paper concentrates on one of Autominder's three main components, the Plan Manager; other papers in this volume describe its other components (Colbry, Peintner, & Pollack 2002; McCarthy & Pollack 2002).

The Nursebot project is a multi-disciplinary, multi-university effort aimed at developing mobile robotic assistants for the elderly.

Planning by Rewriting (PbR) is a new paradigm for efficient high-quality planning that exploits plan rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. Despite the advantages of PbR in terms of scalability, plan quality, and anytime behavior, PbR requires the user to define a set of domain-specific plan rewriting rules which can be difficult and time-consuming. This paper presents an approach to automatically learning the plan rewriting rules based on comparing initial and optimal plans. We report results for several planning domains showing that the learned rules are competitive with manually-specified ones, and in several cases the learning algorithm discovered novel rewriting rules.

A number of current planners make use of automatic domain analysis techniques to extract information such as state invariants or necessary goal orderings from a planning domain. There are also planners that allow the user to explicitly specify additional information intended to improve performance. One such planner is TALplanner, which allows the use of domain-dependent temporal control formulas for pruning a forward-chaining search tree. This leads to the question of how these two approaches can be combined. In this paper we show how to make use of automatically generated state invariants to improve the performance of testing control formulas. We also develop a new technique for analyzing control rules relative to control formulas and show how this often allows the planner to automatically strengthen the preconditions of the operators, thereby reducing time complexity and improving the performance of TALplanner by a factor of up to 400 for the largest problems from the AIPS-2000 competition.

A number of today’s state-of-the-art planners are based on forward state-space search. The impressive performance can be attributed to progress in computing domain independent heuristics that perform well across many domains. However, it is easy to find domains where such heuristics provide poor guidance, leading to planning failure. Motivated by such failures, the focus of this paper is to investigate mechanisms for learning domain-specific knowledge to better control forward search in a given domain. While there has been a large body of work on inductive learning of control knowledge for AI planning, there is a void of work aimed at forward-state-space search. One reason for this may be that it is challenging to specify a knowledge representation for compactly representing important concepts across a wide range of domains. One of the main contributions of this work is to introduce a novel feature space for representing such control knowledge. The key idea is to define features in terms of information computed via relaxed plan extraction, which has been a major source of success for non-learning planners. This gives a new way of leveraging relaxed planning techniques in the context of learning. Using this feature space, we describe three forms of control knowledge—reactive policies (decision list rules and measures of progress) and linear heuristics—and show how to learn them and incorporate them into forward state-space search. Our empirical results show that our approaches are able to surpass state-of-the-art nonlearning planners across a wide range of planning competition domains.

The information resources on the Web are vast, but much of the Web is based on a browsing paradigm that requires someone to actively seek information. Instead, one would like to have information agents that continuously attend to one's personal information needs. Such agents need to be able to extract the relevant information from web sources, integrate data across sites, and execute efficiently in a networked environment. In this paper I describe the technologies we have developed to rapidly construct and deploy information agents on the Web. This includes wrapper learning to convert online sources into agent-friendly resources, query planning and record linkage to integrate data across different sites, and streaming dataflow execution to efficiently execute agent plans. I also describe how we applied this work within the Electric Elves project to deploy a set of agents for continuous monitoring of travel itineraries. 1