This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason with, and learn from, qualitative and quantitative descriptions of uncertainty and knowledge. An action language is used for the low-level (LL) and high-level (HL) system descriptions in the architecture, and the definition of recorded histories in the HL is expanded to allow prioritized defaults. For any given goal, tentative plans created in the HL using default knowledge and commonsense reasoning are implemented in the LL using probabilistic algorithms, with the corresponding observations used to update the HL history. Tight coupling between the two levels enables automatic selection of relevant variables and generation of suitable action policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in large and complex domains. The architecture is evaluated in simulation and on physical robots transporting objects in indoor domains; the benefit on robots is a reduction in task execution time of 39 compared with a purely probabilistic, but still hierarchical, approach.
Probabilistic graphical models such as POMDPs have been used to represent knowledge and plan sensing, navigation and interaction for robots @cite_21 @cite_13 . However, these formulations (by themselves) make it difficult to perform commonsense reasoning, e.g., default reasoning and non-monotonic logical reasoning, especially with information not directly relevant to tasks at hand. In parallel, research in classical planning has provided many algorithms for knowledge representation and logical reasoning @cite_2 , but these algorithms require substantial prior knowledge about the domain, task and the set of actions. Many of these algorithms also do not support merging of new, unreliable information from sensors and humans with the current beliefs in a knowledge base. Answer Set Programming (ASP), a non-monotonic logic programming paradigm, is well-suited for representing and reasoning with commonsense knowledge @cite_23 @cite_5 . An international research community has been built around ASP, with applications such as reasoning in simulated robot housekeepers and for representing knowledge extracted from natural language human-robot interaction @cite_10 @cite_8 . However, ASP does not support probabilistic analysis, whereas a lot of information available to robots is represented probabilistically to quantitatively model the uncertainty in sensor input processing and actuation in the real world.
@cite_8 @cite_10 @cite_21 @cite_23 @cite_2 @cite_5 @cite_13
2032560235 2156415207 1489183362 1606891084 2400776907
Answer set programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers; it is applied to solve hard problems in various domains, such as systems biology, wire routing, and space shuttle control. In this paper, we present an application of ASP to housekeeping robotics. We show how the following problems are addressed using computational methods tools of ASP: (1) embedding commonsense knowledge automatically extracted from the commonsense knowledge base ConceptNet, into high-level representation, and (2) embedding (continuous) geometric reasoning and temporal reasoning about durations of actions, into (discrete) high-level reasoning. We introduce a planning and monitoring algorithm for safe execution of plans, so that robots can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. Some of the recoveries require collaboration of robots. We illustrate the applicability of ASP on several housekeeping robotics problems, and report on the computational efficiency in terms of CPU time and memory. This paper presents an effort to enable robots to utilize open-source knowledge resources autonomously for human-robot interaction. The main challenges include how to extract knowledge in semi-structured and unstructured natural languages, how to make use of multiple types of knowledge in decision making, and how to identify the knowledge that is missing. A set of techniques for multi-mode natural language processing, integrated decision making, and open knowledge searching is proposed. The OK-KeJia robot prototype is implemented and evaluated, with special attention to two tests on 11,615 user tasks and 467 user desires. The experiments show that the overall performance improves remarkably due to the use of appropriate open knowledge. 1 Introduction and Overview I Classical Planning 2 Representations for Classical Planning*3 Complexity of Classical Planning*4 State-Space Planning*5 Plan-Space Planning II Neoclassical Planning 6 Planning-Graph Techniques*7 Propositional Satisfiability Techniques*8 Constraint Satisfaction Techniques III Heuristics and Control Strategies 9 Heuristics in Planning*10 Control Rules in Planning*11 Hierarchical Task Network Planning*12 Control Strategies in Deductive Planning IV Planning with Time and Resources 13 Time for Planning*14 Temporal Planning*15 Planning and Resource Scheduling V Planning under Uncertainty 16 Planning based on Markov Decision Processes*17 Planning based on Model Checking*18 Uncertainty with Neo-Classical Techniques VI Case Studies and Applications 19 Space Applications*20 Planning in Robotics*21 Planning for Manufacturability Analysis*22 Emergency Evacuation Planning *23 Planning in the Game of Bridge VII Conclusion 24 Conclusion and Other Topics VIII Appendices A Search Procedures and Computational Complexity*B First Order Logic*C Model Checking Knowledge management and knowledge-based intelligence are areas of importance in today's economy and society, and their exploitation requires representation via the development of a declarative interface whose input language is based on logic. Chitta Baral demonstrates how to write programs that behave intelligently by giving them the ability to express knowledge and reason about it. He presents a language, AnsProlog, for both knowledge representation and reasoning, and declarative problem solving. Many of the results have never appeared before in book form but are organized here for those wishing to learn more about the subject, either in courses or through self-study. Indoor autonomous mobile service robots can overcome their hardware and potential algorithmic limitations by asking humans for help. In this work, we focus on mobile robots that need human assistance at specific spatially-situated locations (e.g., to push buttons in an elevator or to make coffee in the kitchen). We address the problem of what the robot should do when there are no humans present at such help locations. As the robots are mobile, we argue that they should plan to proactively seek help and travel to offices or occupied locations to bring people to the help locations. Such planning involves many trade-offs, including the wait time at the help location before seeking help, and the time and potential interruption to find and displace someone in an office. In order to choose appropriate parameters to represent such decisions, we first conduct a survey to understand potential helpers' travel preferences in terms of distance, interruptibility, and frequency of providing help. We then use these results to contribute a decision-theoretic algorithm to evaluate the possible choices in offices and plan where to proactively seek help. We demonstrate that our algorithm aims to minimize the number of office interruptions as well as task completion time.