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Planning with Markov Decision Processes
Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on...
Economics of Agricultural and Wildlife Smuggling
The U.S. bans imports of certain agricultural (ag) and wildlife goods that can carry pathogens or diseases or whose harvest can threaten wildlife stocks or endanger species. Despite these bans, contraband is regularly uncovered in inspections of cargo containers and in domestic markets. This study characterizes the economic factors affecting ag and wildlife smuggling by drawing on inspection and interdiction data from the U.S. Dept. of Ag. and the Fish and Wildlife Service and existing economic literature. Ag and wildlife smuggling includes luxury goods, ethnic foods, and specialty goods, such as traditional medicines. Incidents of detected smuggling are disproportionately higher for ag goods originating in China and for wildlife goods originating in Mexico.
Recent Advances in Reinforcement Learning
This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.
Encyclopedia of Machine Learning
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Learning and Reasoning in Hybrid Structured Spaces
Artificial intelligence often has to deal with uncertain scenarios, such as a partially observed environment or noisy observations. Traditional probabilistic models, while being very principled approaches in these contexts, are incapable of dealing with both algebraic and logical constraints. Existing hybrid continuous/discrete models are typically limited in expressivity, or do not offer any guarantee on the approximation errors. This book, Learning and Reasoning in Hybrid Structured Spaces, discusses a recent and general formalism called Weighted Model Integration (WMI), which enables probabilistic modeling and inference in hybrid structured domains. WMI-based inference algorithms differ w...
An Introduction to Lifted Probabilistic Inference
Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.
The Sanner Family in the United States
John Sanner was born ca. 1635 in Europe and died 1698 in St. Mary's County, Maryland. He had a brother, Thomas, who did not come to America, but he had a son, John, born ca. 1658 who came to St. Mary's County ca. 1676 and died in 1753. Also includes the family of George Ludwig Sanner who immigrated to Philadelphia in 1749. He had two sons, Ludwig (ca. 1755-1827) and Michael (1757-1835). Also includes the family of Isaac Sannar, Sr. (born ca. 1754) who was born in Virginia and married Mary Elizabeth (1755-1864). They had two sons, Isaac and Josiah, born in Cleveland, Ohio.
Handbook of Philosophical Logic
This eighteenth volume of the acclaimed Handbook of Philosophical Logic includes many contributors who are among the most famous leading figures of applied philosophical logic of our time. Coverage includes deontic logic, practical reasoning, homogeneous and heterogeneous logical proportion, and talmudic logic. Overall, it will appeal to students, practitioners, and researchers looking for an authoritative resource in these areas. The contributors first explore models in terms of dynamic logics for information-driven agency. The paradigm they use is dynamic-epistemic logics for knowledge and belief and their current extensions to the statics and dynamics of agents’ preferences. Next, in th...
