The Winograd Schema Challenge (WSC) has been proposed as an alternative to the Turing Test for measuring a machine’s intelligence by letting it solve pronoun resolution problems that cannot be tackled by statistical analysis alone, but require commonsense, everyday background knowledge and some form of deeper "understanding" of the question. WSCs are thus hard to solve for machines, but easy for humans.
Many solutions so far are based on machine learning and natural language processing, and achieve results that are hardly better than guessing. Moreover, most knowledge-based approaches to the WSC have been purely theoretical. The goal of this thesis was to develop and implement a knowledge-based WSC solver. In particular, a logic of conditional beliefs called BO is employed that is capable of dealing with incomplete or even inconsistent information (which commonsense knowledge often is). It does so by formalising the observation that humans often reason by picturing different contingencies of what the world could be like, and then choose to believe what is thought to be most plausible. Relevant commonsense background information furthermore is obtained from the ConceptNet semantic network and translated into BO, and processed by the Limbo reasoner.
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In this seminar we will study concepts of forgetting and relevance developed in the field of Knowledge Representation. Humans not only forget unintentionally but also intentionally, for example, when they obtain new information and delete previous contradicting and apparently false information. In fact, humans usually do not forget irrevocably; we can bring back memories and reconstruct forgotten knowledge. In formal languages as studied in KR, however, even supposedly simple forgetting often turns out very difficult. In this seminar, we will study techniques for forgetting in KR languages such as predicate logic, description logics, or answer set programs. Related to forgetting is the concept of relevance among different facts. For example, when some fact is to be forgotten, this may also affect other facts relevant to the forgotten fact. Relevance is useful to develop tractable reasoning and handling inconsistencies in a knowledge base.
In this seminar we will study papers from various subfields of Knowledge Representation. This includes belief revision; argumentation theory; planning; causality in general and the situation calculus in particular. All of these subfields of AI are related to each other. For example, one usually acts depending on what one believes, and actions in turn affect one's beliefs. In this seminar, we will study recent papers on these topics (particularly from the IJCAI-15 conference).
Humanity has crossed the line from being a rural to urban species since 2007. For the first time in history, more people live in cities and urban areas than in the countryside. Starting in the developed ations, where the urbanisation process has been significantly decelerated in the meantime, urbanisation has especially increased in Asia and South America as well as in Africa to a substantial extent in the second half of the last century. Processes of urbanisation have a negative influence on the availability and quality of water resources. Especially in developing and emerging countries, the hydrological and hydrogeological setting of each region is deteriorated through the growing urbanization processes. Often the hydrological and hydrogeological basis of an area is strongly affected by rocesses of urbanisation in these countries. Changes of the structure of urban development going along with the urbanisation will not be without consequences for the environment and water resources.
In frame of this background the Department of Engineering Geology and Hydrogeology of the RWTH Aachen University analysis the interaction between high speed urbanisation/mega-urbanisation and water resources in China and India. In context to this we want to develop a knowledge-based planning and simulation framework.
The goal of this thesis was to integrate Answer Set Programming (ASP) into a Golog system in order to obtain an agent framework that is capable of efficient non-monotonic reasoning with introspection.
In this seminar we will study several modeling and reasoning techniques for knowledge and belief in dynamic systems. Knowledge is an important aspect of intelligent programs: while most of today's systems assume a closed world, i.e., everything they don't know to be true is assumed to be false, an intelligent system needs to consider possible that there are truths not known to the system. In a dynamic environment, i.e., an environment where one or multiple agents (inter)act, the system will usually have to acquire new knowledge through sensing. Potentially it may even revise its beliefs when it realizes some beliefs were wrong. In this seminar we will study various aspects of action, knowledge, and belief.
We plan to implement a peer review process for this seminar. That is, every student will read some other students' term paper and provide feedback in form of a written review. This shall not only deepen your understanding of the other topics, but it also introduces you to the academic review process.