Activity scheduling for a robotic caretaker agent for the elderly

A real-time robotic agent that takes care of an elderly person at home will need to schedule various tasks in real time. The deadlines of its tasks are generally soft (missing a deadline by a few minutes in most cases has no serious consequences). Another characteristic is that many tasks are preferably done close to some time points instead of as soon as possible. To support such time management behaviour, we propose to enrich the BDI agent framework with an extension which consists of two processing components, a priority changing function (PCF) selector and a priority controller. The priorities of desires/intentions are represented by their PCFs. A PCF is a function of both time and the utility value of a desire/intention. So it represents both the urgency and the importance (beneficial value) of a desire/intention. We propose a method of constructing PCFs which model the change of priorities of tasks as time passes. Simulation experiments show that sigmoid function can control the activities of an agent better than constant priorities with respect to getting tasks done with smaller mean earliness and smaller mean tardiness. A BDI agent built with this time management mechanism will try to complete its tasks at the right time. The order in which multiple goals and multiple intentions are handled will be flexible and time dependent.

[1]  Alan Dix,et al.  An Introduction to Artificial Intelligence , 1996 .

[2]  Stéphane Donikian,et al.  The orchestration of behaviours using resources and priority levels , 2001 .

[3]  Anand S. Rao,et al.  BDI Agents: From Theory to Practice , 1995, ICMAS.

[4]  Stacy Marsella,et al.  A domain-independent framework for modeling emotion , 2004, Cognitive Systems Research.

[5]  Feng-Jian Wang,et al.  Intention scheduling for BDI agent systems , 2005, 29th Annual International Computer Software and Applications Conference (COMPSAC'05).

[6]  Anand S. Rao,et al.  An architecture for real-time reasoning and system control , 1992, IEEE Expert.

[7]  Michael Wooldridge,et al.  Intention Reconsideration as Discrete Deliberation Scheduling , 2001 .

[8]  Richard Ernest Bellman,et al.  An Introduction to Artificial Intelligence: Can Computers Think? , 1978 .

[9]  Victor R. Lesser,et al.  Implementing soft real-time agent control , 2001, AGENTS '01.

[10]  Shell-Ying Huang,et al.  Dynamic Control of Intention Priorities of Human-Like Agents , 2006, ECAI.

[11]  Stuart J. Russell,et al.  Principles of Metareasoning , 1989, Artif. Intell..

[12]  Shell-Ying Huang,et al.  A General Framework for Parallel BDI Agents , 2006, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[13]  Michael Wooldridge,et al.  The theory and practice of intention reconsideration , 2004, J. Exp. Theor. Artif. Intell..

[14]  Victor R. Lesser,et al.  AgentSpeak(XL): efficient intention selection in BDI agents via decision-theoretic task scheduling , 2002, AAMAS '02.

[15]  Debdeep Banerjee,et al.  Reactive (Re) Planning Agents in a Dynamic Environment , 2006, Intelligent Information Processing.

[16]  Shell-Ying Huang,et al.  An Agent's Activities Are Controlled by His Priorities , 2008, KES-AMSTA.

[17]  Michael T. Cox Metareasoning: A manifesto , 2007 .

[18]  Michael Wooldridge,et al.  Reasoning about rational agents , 2000, Intelligent robots and autonomous agents.

[19]  Marcus J. Huber JAM: a BDI-theoretic mobile agent architecture , 1999, AGENTS '99.

[20]  Nicholas R. Jennings,et al.  Agent Theories, Architectures, and Languages: A Survey , 1995, ECAI Workshop on Agent Theories, Architectures, and Languages.

[21]  A. S. Roa,et al.  AgentSpeak(L): BDI agents speak out in a logical computable language , 1996 .

[22]  Shell-Ying Huang,et al.  A general framework for parallel BDI agents in dynamic environments , 2008, Web Intell. Agent Syst..

[23]  A. Maslow Motivation and Personality , 1954 .