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In this work, we provide a treatment of the relationship between two models that have been widely used in the implementation of autonomous agents: the Belief DesireIntention (BDI) model and Markov Decision Processes (MDPs). We start with an informal description of the relationship, identifying the common features of the two approaches and the differences between them. Then we hone our understanding of these differences through an empirical analysis of the performance of both models on the TileWorld testbed. This allows us to show that even though the MDP model displays consistently better behavior than the BDI model for small worlds, this is not the case when the world becomes large and the MDP model cannot be solved exactly. Finally we present a theoretical analysis of the relationship between the two approaches, identifying mappings that allow us to extract a set of intentions from a policy (a solution to an MDP), and to extract a policy from a set of intentions.

Decision making. --- Intelligent agents (Computer software). --- Markov processes. --- Statistical decision. --- Markov processes --- Statistical decision --- Intelligent agents (Computer software) --- Decision making --- Mechanical Engineering --- Engineering & Applied Sciences --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Computer Science --- Mechanical Engineering - General --- Information Technology --- Artificial Intelligence --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Agents, Autonomous (Computer software) --- Agents, Cognitive (Computer software) --- Agents, Intelligent (Computer software) --- Assistants, Cognitive (Computer software) --- Assistants, Intelligent software --- Autonomous agents (Computer software) --- Cognitive agents (Computer software) --- Cognitive assistants (Computer software) --- IAs (Computer software) --- Intelligent agent software --- Intelligent software agents --- Intelligent software assistants --- Software agents (Computer software) --- Special agents (Computer software) --- Decision problems --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Computer science. --- Artificial intelligence. --- Computer simulation. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Simulation and Modeling. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Choice (Psychology) --- Problem solving --- Artificial intelligence --- Game theory --- Operations research --- Statistics --- Management science --- Stochastic processes --- Computer programs --- Artificial Intelligence.

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This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.

Computer networks. --- Computer science -- Congresses. --- Computer science. --- Computer algorithms --- Artificial intelligence --- Engineering & Applied Sciences --- Mechanical Engineering --- Computer Science --- Mechanical Engineering - General --- Database management. --- Computer algorithms. --- Artificial intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Mathematical statistics. --- Data mining. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Data Mining and Knowledge Discovery. --- Database Management. --- Probability and Statistics in Computer Science. --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorithms --- Artificial Intelligence. --- Informatics --- Science --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods

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In this work, we provide a treatment of the relationship between two models that have been widely used in the implementation of autonomous agents: the Belief DesireIntention (BDI) model and Markov Decision Processes (MDPs). We start with an informal description of the relationship, identifying the common features of the two approaches and the differences between them. Then we hone our understanding of these differences through an empirical analysis of the performance of both models on the TileWorld testbed. This allows us to show that even though the MDP model displays consistently better behavior than the BDI model for small worlds, this is not the case when the world becomes large and the MDP model cannot be solved exactly. Finally we present a theoretical analysis of the relationship between the two approaches, identifying mappings that allow us to extract a set of intentions from a policy (a solution to an MDP), and to extract a policy from a set of intentions.

Artificial intelligence. Robotics. Simulation. Graphics --- vormgeving --- simulaties --- robots

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This SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the multiple facets of the cyber attribution problem that make it difficult for “out-of-the-box” artificial intelligence and machine learning techniques to handle.  Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. This SpringerBrief not only lays out the theoretical foundations for how to handle the unique aspects of cyber attribution – and how to update models used for this purpose – but it also describes a series of empirical results, as well as compares results of specially-designed frameworks for cyber attribution to standard machine learning approaches.  Cyber attribution is not only a challenging problem, but there are also problems in performing such research, particularly in obtaining relevant data. This SpringerBrief describes how to use capture-the-flag for such research, and describes issues from organizing such data to running your own capture-the-flag specifically designed for cyber attribution. Datasets and software are also available on the companion website.

Production management --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- veiligheid (mensen) --- machine learning --- computerbeveiliging --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)

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This book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.

Artificial intelligence. --- Machine learning. --- Artificial Intelligence. --- Machine Learning. --- Logic, Symbolic and mathematical.