{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":23,"n_cols":5,"rows_kept":23,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[16,2,16,3],[17,2,17,3],[21,2,21,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910143892803321"]
[null,null,"Titolo",null,"Advances in Plan-Based Control of Robotic Agents : International Seminar, Dagstuhl Castle, Germany, October 21-26, 2001, Revised Papers / / edited by Michael Beetz, Leonidas Guibas, Joachim Herztberg, Malik Ghallab, Martha E. Pollack"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002"]
[null,null,"ISBN",null,"3-540-37724-7"]
[null,null,"Edizione",null,"[1st ed. 2002.]"]
[null,null,"Descrizione fisica",null,"1 online resource (VIII, 296 p.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 2466"]
[null,null,"Disciplina",null,"629.8/92"]
[null,null,"Soggetti",null,"Automatic control"]
[{"$nulls":4},"Robotics"]
[{"$nulls":4},"Automation"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Control, Robotics, Automation"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Plan-Based Multi-robot Cooperation -- Plan-Based Control for Autonomous Soccer Robots Preliminary Report -- Reliable Multi-robot Coordination Using Minimal Communication and Neural Prediction -- Collaborative Exploration of Unknown Environments with Teams of Mobile Robots -- Mental Models for Robot Control -- Perceptual Anchoring: A Key Concept for Plan Execution in Embedded Systems -- Progressive Planning for Mobile Robots A Progress Report -- Reasoning about Robot Actions: A Model Checking Approach -- Lifelong Planning for Mobile Robots -- Learning How to Combine Sensory-Motor Modalities for a Robust Behavior -- Execution-Time Plan Management for a Cognitive Orthotic System -- Path Planning for Cooperating Robots Using a GA-Fuzzy Approach -- Performance of a Distributed Robotic System Using Shared Communication Channels -- Use of Cognitive Robotics Logic in a Double Helix Architecture for Autonomous Systems -- The dd&p Robot Control Architecture -- Decision-Theoretic Control of Planetary Rovers."]
[null,null,null,"Sommario/riassunto","In recent years, autonomous robots, including Xavier, Martha [1], Rhino [2,3], Minerva,and Remote Agent, have shown impressive performance in long-term demonstrations. In NASA’s Deep Space program, for example, an - tonomous spacecraft controller, called the Remote Agent [5], has autonomously performed a scienti?c experiment in space. At Carnegie Mellon University, Xavier [6], another autonomous mobile robot, navigated through an o?ce - vironment for more than a year, allowing people to issue navigation commands and monitor their execution via the Internet. In 1998, Minerva [7] acted for 13 days as a museum tourguide in the Smithsonian Museum, and led several thousand people through an exhibition. These autonomous robots have in common that they rely on plan-based c- trol in order to achieve better problem-solving competence. In the plan-based approach, robots generate control actions by maintaining and executing a plan that is e?ective and has a high expected utility with respect to the robots’ c- rent goals and beliefs. Plans are robot control programs that a robot can not only execute but also reason about and manipulate [4]. Thus, a plan-based c- troller is able to manage and adapt the robot’s intended course of action — the plan — while executing it and can thereby better achieve complex and changing tasks."]