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Classical planning is the problem of finding a sequence of actions for achieving a goal from an initial state assuming that actions have deterministic effects. The most effective approach for finding such plans is based on heuristic search guided by heuristics extracted automatically from the problem representation. In this thesis, we introduce alternative approaches for performing inference over the structure of planning problems that do not appeal to heuristic functions, nor to reductions to other formalisms such as SAT or CSP. We show that many of the standard benchmark domains can be solved with almost no search or a polynomially bounded amount of search, once the structure of planning problems is taken into account. In certain cases we can characterize this structure in terms of a novel width parameter for classical planning.
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as ...
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating ...
KI2004wasthe27theditionoftheannualGermanConferenceonArti?cialInt- ligence, which traditionally brings together academic and industrial researchers from all areas of AI and which enjoys increasing international attendance. KI 2004 received 103 submissions from 26 countries. This volume contains the 30 papers that were?nally selected for presentation at the conference. The papers cover quite a broad spectrum of "classical" subareas of AI, like na- ral language processing, neural networks, knowledge representation, reasoning, planning, and search. When looking at this year's contributions, it was exciting to observe that there was a strong trend towards actual real-world applications of AI tech...
Medieval towns were vibrant and complex social environments where diverse groups and lifestyles encountered and influenced each other. Surprisingly, in the study of urban archaeology, the aristocracy, one of the leading and most influential groups in medieval society, has so far been neglected. This book puts "aristocracy in towns" on the archaeological research agenda. The interdisciplinary and comparative study explores the significance and representation of aristocrats and their interaction with civic elites in sea-trading towns of the southwestern Baltic from the 12th to the 14th centuries. Essentially, however, the analysis of urban elite culture leads to discussion of a much more fundamental issue: the informative value of material culture for the investigation of social conditions. The book provides new archaeological approaches to the study of social differentiation in towns, and contributes to a deeper understanding of the complexity of urban social structures.
This book constitutes the refereed proceedings of the 5th European Semantic Web Conference, ESWC 2008, held in Tenerife, Canary Islands, Spain, in June 2008. The 51 revised full papers presented together with 3 invited talks and 25 system description papers were carefully reviewed and selected from a total of 270 submitted papers. The papers are organized in topical sections on agents, application ontologies, applications, formal languages, foundational issues, learning, ontologies and natural language, ontology alignment, query processing, search, semantic Web services, storage and retrieval of semantic Web data, as well as user interfaces and personalization.
Planning is among the characteristic features of intelligence and therefore it is a central research topic of Intellectics since its beginning. Although planning is a very hard task, recent planning systems have achieved an astonishing performance and are applied in various fields. One reason for the success of these systems lies, among others, in the exploitation of structural properties that are present in many but not all problems. The use of such structural properties therefore leads to a specialization on a class of problems. Their exploitation is often conducted by a preprocessing step, i.e., by the application of a special algorithm prior to the search for a plan. This work identifies and examines the class of c-invariants as such a structural property of planning problems. c-Invariants are state invariants and are present in many problems of practical interest. Building on the features of c-invariants, the dissertation presents path reduction, a preprocessing technique that can significantly simplify planning problems. Finally, the work describes an implementation of path reduction and examines its application.
Proceedings of the European Control Conference 1993, Groningen, Netherlands, June 28 – July 1, 1993
This book constitutes the refereed proceedings of the 13th International SPIN workshop on Model Checking Software, SPIN 2006, held in Vienna, Austria in March/April 2006 as satellite event of ETAPS 2006. The 16 revised full papers presented together with three tool presentation papers were carefully reviewed and selected from 44 submissions. The papers are organized in topical sections.
Pesticide usage is increasing worldwide and considered among the main factors contributing to the global decline in biodiversity. This Research Topic provides an overview of the state-of-knowledge regarding non-target effects of herbicides, fungicides, insecticides and rodenticides on a variety of ecosystem functions and organisms. Taxa covered in the contributions include algae, amphibians, aquatic fungi, aquatic insects, bats, bumblebees, butterflies, earthworms, enchytraeids, honeybees, plants, rodents and soil microorganisms. The papers also highlight many gaps in our understanding of non-target effects of pesticides and their consequences for biodiversity and functions of various ecosystems. Overall, it became clear that priorities for future work on pesticides and their effects should more focus on investigating or simulating realistic field situations, i.e., multiple applications of pesticides during the growing season including their temporal and spatial interactions with fauna and flora.