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The International Planning Competition Series began in 1998 and has been running biennially since that date. The competitions have been an important driver for research in the field. In particular, they have resulted in an evolving language for describing planning domains and problems (PDDL), a body of benchmark domains and problems in that language, and the ability to directly compare different generative planning techniques. All of this has contributed to significant advances in both the character and difficulty of problems that can be represented and solved by generative planning techniques. The papers in this JAIR Special Issue cover the 3rd International Planning Competition (IPC-3), held in conjunction with the 6th International Conference on AI Planning and Scheduling (AIPS-02).
The International Planning Competition Series began in 1998 and has been running biennially since that date. The competitions have been an important driver for research in the field. In particular, they have resulted in an evolving language for describing planning domains and problems (PDDL), a body of benchmark domains and problems in that language, and the ability to directly compare different generative planning techniques. All of this has contributed to significant advances in both the character and difficulty of problems that can be represented and solved by generative planning techniques. The papers in this JAIR Special Track cover the 4th International Planning Competition (IPC-4), held in conjunction with the 14th International Conference on Planning and Scheduling (ICAPS-04).
Nonmonotonic reasoning concerns situations when information is incomplete or uncertain. Thus, conclusions drawn lack iron-clad certainty that comes with classical logic reasoning. New information, even if the original one is retained, may change conclusions. Formal ways to capture mechanisms involved in nonmonotonic reasoning, and to exploit them for computation as in the answer set programming paradigm are at the heart of this research area. The six papers accepted for the special track contain significant contributions to the foundations of logic programming under the answer set semantics, to nonmonotonic extensions of description logics, to belief change in restricted settings, and to argumentation.
Over the past two decades, Description Logics (DLs) have grown tremendously in popularity both within the AI community and beyond, due to the balanced trade-off they offer between expressivity and complexity of reasoning. The current success of DLs is the result of many years of rigorous research carried out by the DL community, which has yielded not only beautiful theoretical results but also powerful systems and im- portant practical applications. Notably, DLs provide the logical underpinning of ontology languages (including the W3C standard OWL), making them relevant to a variety of application domains, such as semantic web, medical informatics, life sciences, e-commerce, etc. The objective of this special track is to showcase the best of current DL research. The Track received 17 submissions of which the following seven papers for publication in the special track.
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and effective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a sub-discipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-lingual in order to create multilingual technologies rapidly. The goal of this special track on cross-language algorithms and applications is to present leading research in this area, emphasizing emerging unifying themes that could lead to the development of the science of multi- and cross-lingualism. The papers selected to the track cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-lingual lexical resources, dependency parsing, information retrieval and knowledge representation.
The Journal of Artificial Intelligence Research (JAIR) is pleased to announce the launch of the JAIR Special Track on AI & Society. AI is rarely out of the news. There's a strong appetite within society to understand the impact that technology in general, and AI in particular will have in both the short and long term. The goal of the AI & Society track is to provide scholarly input to the debate around the impact AI will have on society, as well as to provide a forum in which research in AI focused on social good can be presented. All aspects of the impact of AI on society will be covered including but not limited to ethics, philosophy, economics, sociology, psychology, law, history, and politics.
The Journal of Artificial Intelligence Research (JAIR) is pleased to announce the launch of the Special Track on Deep Learning, Knowledge Representation, and Reasoning. The recent success of deep neural networks at tasks such as language modelling, computer vision, and speech recognition has attracted considerable interest from industry and academia. Achieving a better understanding and widespread use of such models involves the use of Knowledge Representation and Reasoning together with sound Machine Learning methodologies and systems. The goal of this special track is to serve as a home for the publication of leading research in deep learning towards cognitive tasks, focusing on applications of neural computation to advanced AI tasks requiring knowledge representation and reasoning. Topics of interest include, but are not limited to: knowledge extraction and reasoning using deep networks, the representation of rich symbolic knowledge by neural systems, the integration of logic and probabilities using neural networks, structure learning and relational learning using recurrent networks, neural-symbolic cognitive agents and models, theory revision and transfer learning with neural networks, applications in natural language understanding, visual intelligence, audio, music and signal processing, multimodal learning, education and assessment, and other tasks of a cognitive nature.
We present improvements in maximum a - posteriori inference for Markov Logic, a widely used SRL formalism. Inferring the most probable world for Markov Logic is NP - hard in general. Several approaches, including Cutting Plane Aggregation (CPA), perform inference through translation to Integer Linear Progra ms. Aggregation exploits context - specific symmetries independently of evidence and reduces the size of the program. We illustrate much more symmetries occurring in long ground clauses that are ignored by CPA and can be exploited by higher - order aggregation s. We propose Full - Constraint - Aggregation, a superior algorithm to CPA which exploits the ignored symmetries via a lift ed translation method and some constraint relaxations. RDBMS and heuristic techniques are involved to improve the overall performance. We introduce Xeggora as an evolutionary extension of RockIt, the query engine that uses CPA. Xeggora evaluation on real - world benchmarks shows progress in efficiency compared to RockIt especially for models with long formulas.
The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.