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Actions and Specificity
Actions and Specificity

... to hold after executing the action. McCarthy and Hayes [25] solved this problem by adding additional frame axioms; one for each action and each fact. The obvious problem with this solution is that the number of frame axioms rapidly increases when many actions and many facts occur. Robert Kowalski r ...
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... procedure for identifying existing commitments to be removed from the constraint graph. The termination criterion is a list of user-defined conditions for ending the search. There may be many criteria: a definition of a solution (e.g., all the activities have a start time and all the constraints are ...
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Dynamic Potential-Based Reward Shaping
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... can be extended to cover joint action learners. Unlike single-agent reinforcement learning where the goal is to maximise the individual’s reward, when multiple self motivated agents are deployed not all agents can always receive their maximum reward. Instead some compromise must be made, typically t ...
Super Logic Programs - Institut für Informatik
Super Logic Programs - Institut für Informatik

... In this paper we propose a specific class of such extended logic programs which will be (immodestly) called super logic programs or just super programs. We will argue that the class of super programs satisfies all of the above conditions, and, in addition, is sufficiently flexible to allow various a ...
A Brief History of Decision Support Systems
A Brief History of Decision Support Systems

... Dynamics Group at the Massachusetts Institute of Technology Sloan School. His work on corporate modeling led to programming DYNAMO, a general simulation compiler. In 1960, J.C.R. Licklider published his ideas about the future role of multiaccess interactive computing in a paper titled “Man-Computer ...
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... Changing the organisation may imply changes within the system at different levels and at different extents. These changes strongly depend on the chosen view (ACPV or OCVP) and on the organisational capabilities of agents (being or not aware of the organisation). Considering the emergence-based MAS, ...
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... John D.C. Little, also at Massachusetts Institute of Technology, was studying DSS for marketing. Little and Lodish (1969) reported research on MEDIAC, a media planning support system. Also, Little (1970) identified criteria for designing models and systems to support management decision-making. His ...
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... The remainder of this article is organized as follows. We first describe related work in Section 2. We then define search problems in Section 3 and describe the general BFS algorithm in Section 4. In Section 5, we extend this algorithm to expand sets of states and study a number of example applicati ...
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A survey of dynamic scheduling in manufacturing systems

... with machine breakdown. The criteria include the minimisation of the makespan (schedule efficiency) and the impact of schedule change (schedule stability). For the stability, they investigated two measures: the deviation from the original job starting time, and the deviation from the original sequen ...
Belief Base Change Operations for Answer Set Programming
Belief Base Change Operations for Answer Set Programming

... the AGM theory was achieved by the use of monotonic SEModels first presented in (Delgrande et al. 2008) and later on extended to merge operations (Delgrande et al. 2009; Hué, Papini, and Würbel 2009) and to update operations in the style of Katsuno and Mendelson by Slota and Leite (Slota and Leite ...
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CptS 440 / 540 Artificial Intelligence
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A Argumentation Mining: State of the Art and Emerging Trends
A Argumentation Mining: State of the Art and Emerging Trends

... [Bench-Capon and Dunne 2007], due to its ability to conjugate representational needs with user-related cognitive models and computational models for automated reasoning. In particular, the study of argumentation in artificial intelligence gave rise to a new discipline called computational argumentat ...
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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