This paper draws its inspiration from current work in reactive planning to guide plan recognition using \plans as recipes". The plan recognition process guided by such a library of plans is called means-end plan recognition. An extension of dynamic logic, called dynamic agent logic, is introduced to provide a formal semantics for means-end plan recognition and its counterpart, means-end plan ex...

We propose an architecture for reactive planning of contents in instruction. It is based on a framework for reactive planning which integrates opportunistic reactions with plan-based (plan-repairs and complete replanning). It is suggested how this framework can implement two radically different teaching styles, tutoring and coaching, as well as their interaction within one system. One additiona...

We propose an architecture for reactive planning of contents in instruction. It is based on a framework for reactive planning which integrates opportunistic reactions with plan-based (plan-repairs and complete replanning). It is suggested how this framework can implement two radically different teaching styles, tutoring and coaching, as well as their interaction within one system. One additiona...

This paper presents a comprehensive study on monoand multi-objective approaches for electrical distribution network design using particle swarmoptimization (PSO). Specifically, two distribution network design problems, i.e., static and expansion planning, are solved using PSO. The network planning involves optimization of both network topology and branch conductor sizes. Both the planning probl...

We present a planning system for selecting policies in probabilistic planning domains. Our system is based on a variant of approximate policy iteration that combines inductive machine learning and simulation to perform policy improvement. Given a planning domain, the system iteratively improves the best policy found so far until no more improvement is observed or a time limit is exceeded. Thoug...

We are engaged in a research project aimed at the synthesis of reactive plans [BDg0, GK91, Kabg0, Mitg0, DKKN93a]. Reactive plans are different from classical plans in that they are specified as a collection of rules or "reactions" of the form S =~ a, where S is a description (perhaps partial) of a situation or state and is an action executable by the agent (such rules have also been called sit...

Autonomous agents that respond intelligently in dynamic, complex environments need to be both reactive and deliberative. Reactive systems have traditionally fared better than deliberative planers in such environments, but are often hard to code and innexible. To ll in some of these gaps, we propose a hybrid system that exploits the strengths of both reactive and deliberative systems. We demonst...

This paper addresses a reactive motion planning framework that allows a humanoid robot, supposedly teleoperated, to perform reaching tasks in complex environments with uncertainty like a damaged plant using measured information like voxel map or point clouds. Since sufficient reactiveness is required for smooth human operation, we have developed an efficient computation method to update the env...

Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization b...