Introduction to artificial intelligence pdf


 

All up-to-date info is on the course web page: terney.info edu/academic/class/s07/www/. • Instructors: Martial Hebert. Artificial intelligence (AI), deep learning, machine learning and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that. The ultimate aim of artificial intelligence (A.I.) is to understand intelligence and to build intelligent Introduction to Artificial Intelligence Download book PDF.

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Introduction To Artificial Intelligence Pdf

This class is a broad introduction to artificial intelligence (AI) o AI is a very broad field with many subareas. • We will cover many of the primary concepts/ideas. A. Overview of Applications-oriented Al Research. B. TEIRESIAS Those of us involved in the creation of theHandbook of Artificial Intelligence, both writers and . CSC Intro to Artificial Intelligence. Winter Instructor: Prof. Sheila McIlraith. Lectures/Tutorials: ▫ Monday. pm GB ▫ Wednesday pm. GB

The classic artificial intelligence teaching material Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that responds in a manner similar to human intelligence. Research in this area includes robotics, speech recognition, image recognition, Natural language processing and expert systems. Since the birth of artificial intelligence, the theory and technology have become more and more mature, and the application fields have been expanding. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can be like human thinking, and it may exceed human intelligence. Russell is a professor of computer science at the University of California at Berkeley and has published more than papers on general-purpose artificial intelligence; Norvig is the director of Google Research, AAAI fellow, ACM fellow. This book provides the most comprehensive and cutting-edge introduction to the theory and practice of artificial intelligence in modern technology. It introduces the most advanced artificial intelligence technology through intelligent decision-making, search algorithms, logical reasoning, neural networks and reinforcement learning. Intelligent professional researchers interested.

Besides, it shows how you can build applications that let computers see and make decisions based on that data. Frankly speaking, this book is a real treasure for two categories of readers. The book consists of three sections. Section I is dedicated to applied math and machine learning basics.

The next section II focuses on deep networks and modern practices. The third part — Section III is all about deep learning research. With recent advances in content personalization, natural language processing, image recognition , and behavior prediction ML is no longer the tool only for data scientists. Knowledge of ML technologies can help designers find ways to better engage with and understand their users.

Patrick Hebron explains how ML applications can affect the way you design websites, mobile applications, and other software. The best thing is that he uses real-world examples to show this impact in practice.

Intelligent professional researchers interested. OpenCV provides an easy-to-use computer vision infrastructure along with a comprehensive library containing more than functions that can run vision code in real time.

Written by the creators of OpenCV, the widely used free open-source library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on the data.

With this book, any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications. View at site Classic textbook for the foundation of deep learning Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.

Synthesis Lectures on Artificial Intelligence and Machine Learning

Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

Deep Learning The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. View at site Textbook for the application of Bayesian decision theory The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques.

The rate of inactive agents is remarkably high in comparison with a high and a balanced job concentration regardless of a coalition. It describes that a low job concentration causes employers and workers to be a spectator by the low possibility of occupation. However, nice agents decrease when a coalition is allowed in the same manner of a high and a balanced job concen- 10 S.

It results in the utility of employers and workers decreased when a coalition is allowed. Figure 4 depicts the variation of average payoffs in a low job concentration along generations. Employers get near the mutual cooperation payoff payoff value 1. It means that the labor market is stable and most of agents i. Workers get fewer payoffs relatively than employers in every job capacity, which describes that they are exploited by aggressive employers due to an unfavorable market structure.

Needless to say, if a coalition is allowed the payoffs become less due to non-cooperative behaviors of the coalition as well as other job concentrations. Variation of average payoffs in a low job concentration. Employers earn more payoffs rather than workers in every job capacity. Notice the values of Y axis in the figures.

Most of payoff lines are lower when a coalition is allowed regardless of employers and workers 4. Particularly, Figure 2 a , b , c Agent-Based Evolutionary Labor Market Model with Strategic Coalition 11 describe the variation of the number of coalitions when a job concentration is high Notice that the maximum number of coalitions is one third of a population. It is caused by that the total number of workers is more than that of employers in each generation.

Figure 2 d , e , f describe the number of coalitions when a job concentration is balanced.

In the figure, the number of coalitions is varied almost equivalently between an employer and a worker. The reason is that the balance of the number of employers and workers permits the equivalent possibility of coalition formation. Figure 2 g , h , i depict the number of coalitions when a job concentration is low. The number of coalitions in each job concentration and a job capacity when a strategic coalition is allowed. Solid lines are for workers and dashed lines are for employers 5 Conclusions A real-world labor market has complex worksite interactions among its constituents like workers and employers.

Therefore, modeling the labor market and predicting the 12 S. Cho future market structure are an important study to help proper policies established and the policies adaptive to a changing environment. In this paper, we propose a strategic coalition to model complex interactions in an agent-based computational labor market. We also investigate how a strategic coalition affects the labor market structure and the behavior of workers and employers.

Experimental results describe that a strategic coalition makes workers and employers more aggressive to their worksite partners.

Specifically, employers and workers act cooperatively when a job capacity is balanced and a coalition is not allowed. However, they become non-cooperative players when a coalition is allowed. The number of coalitions varies according to a labor market structure which consists of the ratio of employers and workers.

That is, employers form a coalition more actively when a job concentration is high. Conversely, workers form more coalitions when a job concentration is low.

CS Introduction to Artificial Intelligence

The utility level of employers and workers becomes less when a coalition is allowed. This appears remarkably high when a labor market structure is in a tight and an excess job capacity. References 1.

Tesfatsion, L. Axelrod, R. Colman, A. Darwen, P.

J, Yao, X. Springer-Verlag, Heidelberg Germany 7. Francisco, A. Shehory, O. Garland, A. Tate, A. Sandholm, T. Privacy preservation is crucial in the ubiquitous computing because a lot of privacy-sensitive information can be collected and distributed in the ubiquitous computing environment. The anonymity-based approach is one of well-known ones for privacy preservation in ubiquitous computing.

It allows users to use pseudonyms when they join in a service area of the ubiquitous computing environment. This paper proposes a multiagent architecture to make it possible to provide ID-based services in the anonymity-based privacy awareness systems.

The proposed architecture employs so-called the white page agent which maintains the current pseudonyms of users and allows users to get the pseudonyms of other users from the agent. Even though the white page agent contains the pseudonyms of users, it is enforced not to disclose the association of real user IDs with pseudonyms by adopting encryption techniques.

This paper presents in detail the proposed architecture, its constituent components and their roles and how they provide ID-based services in the anonymity-based system. As an example, it also presents how buddy service can take place in the proposed architecture.

To provide ubiquitous computing services, they need to use contextual information e. Lots of contextual information about users are collected through sensors embedded in the environment and stored somewhere in the environment. Among the contextual information, the identity ID and location are most sensitive.

If it is possible to completely hide real IDs of users, we are nearly free from privacy concerns. In practice users frequently confront with the situations their IDs are asked when they use some services. In the meanwhile, the locations of users may imply some aspects of privacy-sensitive information. In the policy-based approach, a designated server takes charge of handling access control to privacy-sensitive information based on the privacy policies.

Users register at the server their privacy preferences about who would be allowed to access their information. In this scheme, the users should put their trust in the server. In the circumstances where there will be multiple servers, users would hesitate to trust those servers. On the contrary, the anonymity-based approach does not demand users to trust any server.

To protect their own privacy, users use pseudonyms when they join in a service area. Thanks to pseudonyms, attackers come to have difficulty in associating pseudonyms with real IDs. Even though the number of users in an service area affects the degree of privacy preservation and data mining techniques could reveal some association among pseudonyms, the anonymity-based approach has the advantage in that users do not have to unconditionally believe some server.

However, this approach also has some restrictions in providing ID-based services like buddy service, safety alert service. The buddy service is one of popular services in cellular phone community, which informs a user of the presence of her buddy around her.

The safety alert service is to help a user not to get into a dangerous place by tracking her location. In this paper, we propose a multiagent architecture to enable ID-based services in the anonymity-based privacy awareness systems. The white page agent is assumed to be not so much as secure and trustworthy as the users expect. To provide secure white page service, the proposed method employs encryption techniques and several interaction protocols.

In the proposed architecture, the users register their pseudonyms to the white page agent each time they change 16 K. Lee and S. Lee their pseudonym. The users and applications having the friendship with a user can access the current pseudonym of the user stored in the white page agent. Even though the white agent stores the pseudonym data for users, it cannot figure out their contents because the pseudonyms are encrypted with the keys not known to the agent.

By enabling to locate users by pseudonyms, the proposed method provides the ID-based services in the anonymity-based ubiquitous computing environment. In addition, by introducing a hierarchical naming scheme considering proximity among physical areas, the proposed architecture enables to easily provide location-based services. This paper is organized as follows: Section 2 presents some related works to privacy preservation in ubiquitous computing. Section 3 introduces the proposed multiagent architecture for ID-based services in anonymity-based ubiquitous computing environment.

Section 4 shows how to implement buddy service on the proposed architecture as an example. Finally, Section 5 draws conclusions. Users register to the server their privacy preferences about who can use which data of them.

CS430: Introduction to Artificial Intelligence

When an application requests data from the server, it also sends its privacy policy for the data along with the request.

The privacy policy-based control method enables flexible access control based on various criteria such as time of the request, location, speed, and identities of the located objects. Despite this advantage, it is burdensome for average users to specify such complex policies.

Privacy policies play the role of establishing a trust in the server. But, they cannot guarantee that the server adequately protects the collected data from various attacks. The users are also enforced to trust the server who controls the access to their privacy-sensitive data. The anonymity-based privacy preservation method is an alternative of the policy-based method. In this scheme, when a user enters into a service area of an application, she uses a pseudonym instead of her real ID.

The use of pseudonyms makes it difficult for malicious applications to identify and track individuals. There remain yet some vulnerabilities under the situations in which the only limited number of users move in and out the service areas, or the sensor network is owned by an untrusted party which can keep track of device-level IDs like MAC addresses.

Due to the anonymity-based nature, it is not easy to incorporate detailed access control like privacy policies and to provide ID-based services like buddy services, safety alert service, callback service, etc. This method does not ask users to trust any application or server. In their architecture, the sensors can keep track of the number of users in an area and monitor changes in real-time, and a location server collects the sensor data and publishes it to applications.

To enable this, the approach takes the special naming scheme for locations in which names are encoded into a hierarchically organized bit stream. It reports only some upper part of the bit stream when it wants to increase the level of anonymity.

This approach uses pseudonyms for locations instead of using pseudonyms for users. As the matter of fact, the pseudonyms for locations are the blurred IDs of the real location IDs. In the Cricket location-support system[13], there are a set of beacons embedded in the environment and receiving devices that determine their location by listening to the radio and ultrasound beacons. The location information is initially only known to the devices, and then the owners of the devices decide to whom this information will be disclosed.

Therefore, the users do not have to trust any embedded sensors or servers. To employ this approach, the sensor networks should be comprised of only the homogeneous types of sensors. The users should carry a device that is compatible with the beacons and powerful enough to process the tasks of location identification and communication. They do not disclose their real IDs to the sensor networks to preserve their privacy.

While they communicate with each other, they use their pseudonyms. Their devices are assumed not to have unique IDs which the sensor networks could use to associate them with specific users. Therefore, it is assumed that there are no ways to directly bind user IDs with device IDs. There are special servers called zone agents, each of which takes care of a spatial service area, collects data from the sensors in their own area, communicates with user devices i.

This section describes the proposed ID-based service multiagent architecture in detail. Lee Fig. Overview of the ID-based service multiagent architecture 3.

The architecture comprises of a white page agent, a set of zone agents, a set of user agents, a set of user directory agents, and a set of application agents. Each agent plays the following roles: White Page Agent. It also provides the white page service that enables users with proper keys to retrieve the current pseudonyms of their friends.

User Agents. Each user has her own agent which runs in the device carried with her. On behalf of users, the user agents communicate with other agents and applications in a privacy-preserving way. Zone Agents. Each zone agent takes care of a designated physical zone. It monitors which user agents come in and go out its zone and assigns new pseudonyms to user agents i. It relays the communication messages between user agents in its zone and user agents or applications off the zone.

User Directory Agents. In the considered architecture, there are multiple user directory agents, each of which can work for a set of users. A user makes a contract with a user directory agent and delegates to the user directory agent the task of maintaining her pseudonym. Application Agents. An application agent acts as an interface for an application. It receives requests from outside, transfers them to its corresponding application, and then sends back the results produced by the application.

It also serves as the communication front-end of an application. Thus it can play the role of establishing initial set-up like acquiring the current pseudonym of a user when an application wants to communicate with the user.

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