Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Preface. 1 Introduction. 1.1 Autonomous Agents. 1.2 Characteristics of Agents. 1.3 Multi-Agent Systems. 1.4 Hello World! 2 The BDI Agent Model. 2.1 Agent-Oriented Programming. 2.2 Practical Reasoning. 2.3 A Computational Model of BDI Practical Reasoning. 2.4 The Procedural Reasoning System. 2.5 Agent Communication. 3 The Jason Agent Programming Language. 3.1 Beliefs. 3.2 Goals. 3.3 Plans. 3.4 Example: A Complete Agent Program. 3.5 Exercises. 4 Jason Interpreter. 4.1 The Reasoning Cycle. 4.2 Plan Failure. 4.3 Interpreter Configuration and Execution Modes. 4.4 Pre-Defined Plan Annotations. 4.5 Exercises. 5 Environments. 5.1 Support for Defining Simulated Environments. 5.2 Example: Running a System of Multiple Situated Agents. 5.3 Exercises. 6 Communication and Interaction. 6.1 Available Performatives. 6.2 Informal Semantics of Receiving Messages. 6.3 Example: Contract Net Protocol. 6.4 Exercises. 7 User-Defined Components. 7.1 Defining New Internal Actions. 7.2 Customising the Agent Class. 7.3 Customising the Overall Architecture. 7.4 Customising the Belief Base. 7.5 Pre-Processing Directives. 7.6 Exercises. 8 Advanced Goal-Based Programming. 8.1 BDI Programming. 8.2 Declarative (Achievement) Goal Patterns. 8.3 Commitment Strategy Patterns. 8.4 Other Useful Patterns. 8.5 Pre-Processing Directives for Plan Patterns. 9 Case Studies. 9.1 Case Study I: Gold Miners. 9.2 Case Study II: Electronic Bookstore. 10 Formal Semantics. 10.1 Semantic Rules. 10.2 Semantics of Message Exchange in a Multi-Agent System. 10.3 Semantic Rules for Receiving Messages. 10.4 Semantics of the BDI Modalities for AgentSpeak. 11 Conclusions. 11.1 Jason and Agent-Oriented Programming. 11.2 Ongoing Work and Related Research. 11.3 General Advice on Programming Style and Practice. A Reference Guide. A.1 EBNF for the Agent Language. A.2 EBNF for the Multi-Agent Systems Language. A.3 Standard Internal Actions. A.4 Pre-Defined Annotations. A.5 Pre-Processing Directives. A.6 Interpreter Configuration. Bibliography.

Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology)

Thank you very much for downloading logistic regression a self learning text. As you may know, people have search hundreds times for their favorite books like this logistic regression a self learning text, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their desktop computer. logistic regression a self learning text is available in our book collection an online access to it is set as public so you can get it instantly. Our digital library spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the logistic regression a self learning text is universally compatible with any devices to read.

Logistic Regression A Self Learning Text

A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

Automated Planning: Theory and Practice.

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

/pdf/multiagent-systems-a-modern-approach-to-distributed-5fon0donq1.pdf

Multiagent systems: a modern approach to distributed artificial intelligence

Remaining useful lifetime prediction via deep domain adaptation

We provide a new formulation for the problem of learning the optimal classification tree of a given depth as a binary linear program. A limitation of previously proposed Mathematical Optimization formulations is that they create constraints and variables for every row in the training data. As a result, the running time of the existing Integer Linear programming (ILP) formulations increases dramatically with the size of data. In our new binary formulation, we aim to circumvent this problem by making the formulation size largely independent from the training data size. We show experimentally that our formulation achieves better performance than existing formulations on both small and large problem instances within shorter running time.

Learning optimal classification trees using a binary linear program formulation (extended abstract)

We study the problem of allocating a set of indivisible goods to a set of agents having additive preferences. We introduce two new important complexity results concerning efficiency and fairness in resource allocation problems: we prove that the problem of deciding whether a given allocation is Pareto-optimal is coNP-complete, and that the problem of deciding whether there is a Pareto-efficient and envy-free allocation is $\Sigma_2^p$-complete.

/pdf/on-the-complexity-of-efficiency-and-envy-freeness-in-fair-yvxvq3vsq5.pdf

On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences

This paper proposes a new variant of the task allocation problem, where the agents are connected in a social network and tasks arrive at the agents distributed over the network. We show that the complexity of this problem remains NP-complete. Moreover, it is not approximable within some factor. In contrast to this, we develop an efficient greedy algorithm for this problem. Our algorithm is completely distributed, and it assumes that agents have only local knowledge about tasks and resources. We conduct a broad set of experiments to evaluate the performance and scalability of the proposed algorithm in terms of solution quality and computation time. Three different types of networks, namely small-world, random and scale-free networks, are used to represent various social relationships among agents in realistic applications. The results demonstrate that our algorithm works well and also that it scales well to large-scale applications. In addition we consider the same problem in a setting where the agents holding the resources are self-interested. For this, we show how the optimal algorithm can be used to incentivize these agents to be truthful. However, the efficient greedy algorithm cannot be used in a truthful mechanism, therefore an alternative, cluster-based algorithm is proposed and evaluated.

/pdf/multiagent-task-allocation-in-social-networks-1z57gb954r.pdf

Multiagent task allocation in social networks

This paper proposes a new variant of the task allocation problem, where the agents are connected in a social network and tasks arrive at the agents distributed over the network. We show that the complexity of this problem remains NP-hard. Moreover, it is not approximable within some factor. We develop an algorithm based on the contract-net protocol. Our algorithm is completely distributed, and it assumes that agents have only local knowledge about tasks and resources. We conduct a set of experiments to evaluate the performance and scalability of the proposed algorithm in terms of solution quality and computation time. Three different types of networks, namely small-world, random and scale-free networks, are used to represent various social relationships among agents in realistic applications. The results demonstrate that our algorithm works well and that it scales well to large-scale applications.

/pdf/distributed-task-allocation-in-social-networks-3hkw0wlxl2.pdf

Yingqian Zhang

Papers

Remaining useful lifetime prediction via deep domain adaptation

Learning optimal classification trees using a binary linear program formulation (extended abstract)

On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences

Multiagent task allocation in social networks

Distributed task allocation in social networks