Answer Set Planning Under Action Costs

Recently, planning based on answer set programming has been proposed as an approach towards realizing declarative planning systems. In this paper, we present the language Kc, which extends the declarative planning language K by action costs. Kc provides the notion of admissible and optimal plans, which are plans whose overall action costs are within a given limit resp. minimum over all plans (i.e., cheapest plans). As we demonstrate, this novel language allows for expressing some nontrivial planning tasks in a declarative way. Furthermore, it can be utilized for representing planning problems under other optimality criteria, such as computing ``shortest'' plans (with the least number of steps), and refinement combinations of cheapest and fastest plans. We study complexity aspects of the language Kc and provide a transformation to logic programs, such that planning problems are solved via answer set programming. Furthermore, we report experimental results on selected problems. Our experience is encouraging that answer set planning may be a valuable approach to expressive planning systems in which intricate planning problems can be naturally specified and solved

A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity

We propose a new declarative planning language, called K, which is based on principles and methods of logic programming. In this language, transitions between states of knowledge can be described, rather than transitions between completely described states of the world, which makes the language well-suited for planning under incomplete knowledge. Furthermore, it enables the use of default principles in the planning process by supporting negation as failure. Nonetheless, K also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, which shows that the language is very flexible. As we demonstrate on particular examples, the use of knowledge states may allow for a natural and compact problem representation. We then provide a thorough analysis of the computational complexity of K, and consider different planning problems, including standard planning and secure planning (also known as conformant planning) problems. We show that these problems have different complexities under various restrictions, ranging from NP to NEXPTIME in the propositional case. Our results form the theoretical basis for the DLV^K system, which implements the language K on top of the DLV logic programming system.Comment: 48 pages, appeared as a Technical Report at KBS of the Vienna University of Technology, see http://www.kr.tuwien.ac.at/research/reports

Robotics handbook. Version 1: For the interested party and professional

This publication covers several categories of information about robotics. The first section provides a brief overview of the field of Robotics. The next section provides a reasonably detailed look at the NASA Robotics program. The third section features a listing of companies and organization engaging in robotics or robotic-related activities; followed by a listing of associations involved in the field; followed by a listing of publications and periodicals which cover elements of robotics or related fields. The final section is an abbreviated abstract of referred journal material and other reference material relevant to the technology and science of robotics, including such allied fields as vision perception; three-space axis orientation and measurement systems and associated inertial reference technology and algorithms; and physical and mechanical science and technology related to robotics

The DLV System for Knowledge Representation and Reasoning

This paper presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to $\Delta^P_3$-complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of DLV, and by deriving new complexity results we chart a complete picture of the complexity of this language and important fragments thereof. Furthermore, we illustrate the general architecture of the DLV system which has been influenced by these results. As for applications, we overview application front-ends which have been developed on top of DLV to solve specific knowledge representation tasks, and we briefly describe the main international projects investigating the potential of the system for industrial exploitation. Finally, we report about thorough experimentation and benchmarking, which has been carried out to assess the efficiency of the system. The experimental results confirm the solidity of DLV and highlight its potential for emerging application areas like knowledge management and information integration.Comment: 56 pages, 9 figures, 6 table

Action-level intention selection for BDI agents

Belief-Desire-Intention agents typically pursue multiple goals in parallel. However the interleaving of steps in different intentions may result in conflicts, e.g., where the execution of a step in one plan makes the execution of a step in another concurrently executing plan impossible. Previous approaches to avoiding conflicts between concurrently executing intentions treat plans as atomic units, and attempt to interleave plans in different intentions so as to minimise conflicts. However some conflicts cannot be resolved by appropriate ordering of plans and can only be resolved by appropriate interleaving of steps within plans. In this paper, we present SA, an approach to intention selection based on Single-Player Monte Carlo Tree Search that selects which intention to progress at the current cycle at the level of individual plan steps. We evaluate the performance of our approach in a range of scenarios of increasing difficulty in both static and dynamic environments. The results suggest SA out-performs existing approaches to intention selection both in terms of goals achieved and the variance in goal achievement time

Action-level intention selection for BDI agents

Belief-Desire-Intention agents typically pursue multiple goals in parallel. However the interleaving of steps in different intentions may result in conflicts, e.g., where the execution of a step in one plan makes the execution of a step in another concurrently executing plan impossible. Previous approaches to avoiding conflicts between concurrently executing intentions treat plans as atomic units, and attempt to interleave plans in different intentions so as to minimise conflicts. However some conflicts cannot be resolved by appropriate ordering of plans and can only be resolved by appropriate interleaving of steps within plans. In this paper, we present SA, an approach to intention selection based on Single-Player Monte Carlo Tree Search that selects which intention to progress at the current cycle at the level of individual plan steps. We evaluate the performance of our approach in a range of scenarios of increasing difficulty in both static and dynamic environments. The results suggest SA out-performs existing approaches to intention selection both in terms of goals achieved and the variance in goal achievement time

The Incremental Planning System – GSOC’s Next Generation Mission Planning Framework

The paper at hand presents the new generic framework for automated planning and scheduling in future mission planning systems developed at GSOC (German Space Operations Center). It evolved from the experiences made in past and current projects and the evaluation of internal and external requirements for upcoming projects. In customary systems such as the one used within GSOC’s TerraSAR-X/TanDEM-X mission, succeeding planning runs to combine all collected input to a consistent, conflict-free command timeline take place at fix, dedicated points in time, e.g. twice a day. In contrast and as a main difference, with the new system each new input is processed immediately and so a consistent up-to-date timeline is maintained at all times. We show that this approach provides a set of important advantages and new possibilities for spacecraft commanding and user satisfaction. For example, uplink schedules can be flexibly modified due to short-term notifications, or up-to-date, extensive information about the planning state is always available, which means that conflicts can be seen before finally submitting a new request and, if applicable, can be resolved by selecting a suggested solution scenario. The presented system constitutes a generic tool suite which is scalable in performance critical areas, which is configurable to various mission scenarios and which defines a dedicated set of interfaces, specifying the functionality that remains to be implemented by each individual project. The declared goal is that all upcoming GSOC missions will benefit from using the Incremental Planning framework in terms of cost reduction, implementation duration and system robustness

Modularity in answer set programs

Answer set programming (ASP) is an approach to rule-based constraint programming allowing flexible knowledge representation in variety of application areas. The declarative nature of ASP is reflected in problem solving. First, a programmer writes down a logic program the answer sets of which correspond to the solutions of the problem. The answer sets of the program are then computed using a special purpose search engine, an ASP solver. The development of efficient ASP solvers has enabled the use of answer set programming in various application domains such as planning, product configuration, computer aided verification, and bioinformatics. The topic of this thesis is modularity in answer set programming. While modern programming languages typically provide means to exploit modularity in a number of ways to govern the complexity of programs and their development process, relatively little attention has been paid to modularity in ASP. When designing a module architecture for ASP, it is essential to establish full compositionality of the semantics with respect to the module system. A balance is sought between introducing restrictions that guarantee the compositionality of the semantics and enforce a good programming style in ASP, and avoiding restrictions on the module hierarchy for the sake of flexibility of knowledge representation. To justify a replacement of a module with another, that is, to be able to guarantee that changes made on the level of modules do not alter the semantics of the program when seen as an entity, a notion of equivalence for modules is provided. In close connection with the development of the compositional module architecture, a transformation from verification of equivalence to search for answer sets is developed. The translation-based approach makes it unnecessary to develop a dedicated tool for the equivalence verification task by allowing the direct use of existing ASP solvers. Translations and transformations between different problems, program classes, and formalisms are another central theme in the thesis. To guarantee efficiency and soundness of the translation-based approach, certain syntactical and semantical properties of transformations are desirable, in terms of translation time, solution correspondence between the original and the transformed problem, and locality/globality of a particular transformation. In certain cases a more refined notion of minimality than that inherent in ASP can make program encodings more intuitive. Lifschitz' parallel and prioritized circumscription offer a solution in which certain atoms are allowed to vary or to have fixed values while others are falsified as far as possible according to priority classes. In this thesis a linear and faithful transformation embedding parallel and prioritized circumscription into ASP is provided. This enhances the knowledge representation capabilities of answer set programming by allowing the use of existing ASP solvers for computing parallel and prioritized circumscription

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors

Goal reasoning for autonomous agents using automated planning

Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely a plan, which achieves a goal from a given initial state. Most planning research consider goals are provided by a external user, and agents just have to find a plan to achieve them. However, there exist many real world domains where agents should not only reason about their actions but also about their goals, generating new ones or changing them according to the perceived environment. In this thesis we aim at broadening the goal reasoning capabilities of planningbased agents, both when acting in isolation and when operating in the same environment as other agents. In single-agent settings, we firstly explore a special type of planning tasks where we aim at discovering states that fulfill certain cost-based requirements with respect to a given set of goals. By computing these states, agents are able to solve interesting tasks such as find escape plans that move agents in to safe places, hide their true goal to a potential observer, or anticipate dynamically arriving goals. We also show how learning the environment’s dynamics may help agents to solve some of these tasks. Experimental results show that these states can be quickly found in practice, making agents able to solve new planning tasks and helping them in solving some existing ones. In multi-agent settings, we study the automated generation of goals based on other agents’ behavior. We focus on competitive scenarios, where we are interested in computing counterplans that prevent opponents from achieving their goals. We frame these tasks as counterplanning, providing theoretical properties of the counterplans that solve them. We also show how agents can benefit from computing some of the states we propose in the single-agent setting to anticipate their opponent’s movements, thus increasing the odds of blocking them. Experimental results show how counterplans can be found in different environments ranging from competitive planning domains to real-time strategy games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert