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1	End-to-End Neuro-Symbolic Approaches for Event Recognition Apriceno, Gianluca 30 October 2023 (has links) Event detection is a critical challenge in many fields like video surveillance, social graph analysis, and multimedia processing. Furthermore, events are “structured” objects involv ing multiple components like the event type, the participants with their roles, and the atomic events in which it decomposes. Therefore, the recognition of an event is not only limited to recognize the type of the event and when it happened, but it involves solving a set of simple tasks. Exploiting background knowledge about events and their relations could then be beneficial for event detection. In the last years, neuro-symbolic integration has been proposed to merge the strengths and overcome the drawbacks of both symbolic and neural worlds. As a consequence, different neuro-symbolic frameworks, which com bine low-level perception of neural networks with a symbolic layer, encoding prior domain knowledge (usually defined in terms of logical rules), have been applied to solve different atemporal tasks. In this thesis, we want to investigate the application of the neuro-symbolic paradigm for event detection. This would also provide a better insight into the strengths and limitations of neuro-symbolic towards tasks involving time.
2	Evaluation Functions in General Game Playing Michulke, Daniel 24 July 2012 (has links) (PDF) While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it. Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own. One approach to obtain such a strategy is to analyze the game rules and create a state evaluation function that can be subsequently used to direct the agent to promising states in the match. In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function. Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent. Agent Spiele Allgemeine Spiele Künstliche Intelligenz Bewertungsfunktionen Zustandsbewertung Heuristik Merkmale Neuronale Netze Neuro-Symbolische Integration Agent Games General Game Playing GGP Artificial Intelligence evaluation function state evaluation heuristic features neural networks neuro-symbolic integration ddc:003 ddc:004 ddc:006 rvk:ST 324 Heuristik Merkmal Agent <Künstliche Intelligenz> Spieltheorie
3	Evaluation Functions in General Game Playing Michulke, Daniel 22 June 2012 (has links) While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it. Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own. One approach to obtain such a strategy is to analyze the game rules and create a state evaluation function that can be subsequently used to direct the agent to promising states in the match. In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function. Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent.:Introduction Game Playing Evaluation Functions I - Aggregation Evaluation Functions II - Features General Evaluation Related Work Discussion info:eu-repo/classification/ddc/003 ddc:003 info:eu-repo/classification/ddc/004 ddc:004 info:eu-repo/classification/ddc/006 ddc:006

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