The Combination of Artificial Intelligence and Robotics for a Mobile Robot Application

The combination of artificial intelligence and robotics led to more flexibility and powerful mobile robot applications. The robot and its environment were simulated with a microcomputer to gather statistical information. An application was demonstrated in the laboratory with a mobile robot. The robot recognized an obstacle in its path and generated the necessary changes in its environment so that it could successfully complete a required task

Elicit: Expertise Learner and Intelligent Causal Inference Tool

Knowledge-based systems are tools for solving difficult and complex problems for which standard algorithmic methods of solution are inadequate. The published literature describes very few knowledge-based systems developed to aid in solving complex reasoning tasks encountered in engineering. Planning, monitoring, design, and control tasks are considered to be complex reasoning tasks. Model-based reasoning about physical systems is required, involving time, space, and causality. ELICIT, Expertise Learner and Intelligent Causal Inference Tool, is a knowledge-based system developed to effectively acquire a domain-dependent knowledge base for an Intelligent Simulation Training System (ISTS). An ISTS consists of a graphic computer simulation, an expert system, and an interface. An ISTS is useful for training students in the monitoring and control of simulation objects. To this end, ELICIT accomplishes three goals: 1. achieves an adequate reasoning capability for performing instruction, monitoring, and control related to the graphic computer simulation of a physical system, 2. provides for the automatic acquisition of a knowledge base which is suitable for such reasoning tasks, and 3. maintains a consistent and complete knowledge base. ELICIT acquires an adequate knowledge of the domain to allow situation-dependent reasoning about a specific task. ELICIT\u27s reasoning layer is designed to determi ne appropriate actions to take in the performance of expert behavior in controlling simulation objects. The representation allows the interleaving of causal spatial reasoning with temporal reasoning about sequences of situations. ELICIT accomplishes expert decision-making capability by dynamically modifying predictions of future events to determine what simulation data is needed and when that data should be acquired by the reasoner. A mobile robot simulation was developed to evaluate ELICIT. The robot\u27s goal was to meet with a mobile recharger in an enclosed area and exit through a gate without depleting its energy supply. A robot which accesses positional data from the simulation at regular time intervals was compared to a robot which acquires dynamic spatial simulation information based on expectations of future events. The robot with dynamic prediction capability proved to be more successful at its task. ELICIT represents a significant development in the areas of automated reasoning and knowledge acquisition as related to engineering problem-solving t asks . Specifically, ELICIT provides a representation and acquisition strategy for the knowledge needed for monitoring, control, and instruction in an ISTS

Marriage Of Artificial Intelligence And Robotics In The Manufacturing Environment.

The combination of artificial intelligence and robotics led to more flexibility and powerful mobile robot applications. A robot and its environment were simulated, using a microcomputer, to gather statistical information. An application was demonstrated in the laboratory with a mobile robot. The robot recognized an obstacle in its path and generated the necessary changes in its environment to successfully complete a required task

A Modified Gert Network For Automatic Acquisition Of Temporal Knowledge

This paper describes a technique which has been developed for automatically acquiring temporal knowledge for use in an Intelligent Simulation Training System (ISTS)*. The objective of the ISTS is to train students in monitoring and controlling physical objects in time and space. The ISTS consists of a graphic computer simulation, an expert system, and a user interface. The graphic computer simulation represents the system to be monitored. The trainee responds to routine and/or critical situations as depicted in the simulation by typing in commands to simulation objects. ISTS domain knowledge is automatically acquired by a subsystem known as ELICIT: Expertise Learner and Intelligent Causal Inference Tool. ELICIT incorporates a modified GERT network for representing knowledge concerning dependencies among temporal events. The network is used as a basis for prompting the domain expert for knowledge. In addition, ELICIT draws upon knowledge contained in the network for the development of contingency plans concerning timing of events. This implementation is coded in Common LISP and Joshua (a knowledge representation language developed by Symbolics, Inc.) on a Symbolics 3630 LISP machine. © 1991

Reasoning In Time And Space: Issues In Interfacing A Graphic Computer Simulation With An Expert System For An Intelligent Simulation Training System

An expert-system-graphic-computer-simulation interface for an intelligent simulation training system (ISTS) which is currently under development is described. Important design considerations include the reasoning tasks involved, mechanisms for reasoning about physical systems, and machine perception of simulation data for use by the expert system. It is necessary to design the interface between a graphic computer simulation and an expert system carefully in order to realize automated, intelligent training related to physical systems

Active Rescheduling for Automated Guided Vehicle Systems

This paper examines the use of knowledge-based techniques to generate a framework for the active rescheduling of an automated guided vehicle system in a manufacturing environment. Our approach to active resecheduling uses “cues ” drawn from events on the shop floor to trigger rescheduling. Simulation experiments are used to capture knowledge about the shop floor and various scheduling strategies. An extensible agent architecture is developed to facilitate active resheduling

Supporting Dynamic Adaptive Autonomy for Agent-based Systems

The level of autonomy at which individual agents function is of critical importance to the overall operation of multi-agent systems. The term level of autonomy refers to the type of interactions between an agent and other agents in its system. In well-defined contexts, agents can be designed for a single level of autonomy by predicting the type of problems that will be faced. However, in dynamic systems, the appropriate level of autonomy may depend on the situation. Therefore, substantial performance benefits for agent-based systems can be realized by agents that are capable of dynamically adapting their level of autonomy during system operation. This paper develops a representation for agent autonomy level and discusses how dynamic adaptive autonomy can be used to create flexible multi-agent systems applicable to manufacturing environments. Accepted to Artificial Intelligence and Manufacturing: A Research Planning Workshop Albuquerque, NM Contact person: Leslie Interrante..