Artificial Intelligence Books

I think this should be the first book that college students considering a career in AI research should read. It is far FAR more readable than "Artificial Intelligence: A Modern Approach" for the layperson ... or any person for that matter. I really hope that Mr. Hogan releases updated versions of this book every five years ... which means we're past due for the next edition right now. ;-)

And I hope Mr. Hogan infuses more of the book with his sense of humor ... especially at the end. He needs to keep this VERY dry topic as light as he can.

I feel a computer helps humans to consider more complex ideas and that it is an adjunct to the human mind. I don't believe in AI. This book and Mr Hogan's comments support me in that. It is thoroughly enjoyable and written in an interested reader's level and not too Hi-tech.

This isn't the first "history of AI" book in my collection, and won't be the last. I picked this one up because of Hogan's name, and am happy with that choice. I like his style. He knows where to put in the jokes (although he could use a few more during the weaker parts toward the end). The beginning, which starts wayyyyy back with the philosophers, is a little dry, and the end (with lots of time spent on what computers still can't do) didn't interest me. I like knowing about the trials and tribulations of what worked and didn't, and why. I'm hoping that one of these days somebody will do an entire book on the Cyc project.

I started to say I "enjoyed" this book, but it seems to be this is not the type of book that one "enjoys." I have read everything of James P. Hogan's work I could get my hands on, and he is one of my top two or three favorite writers. This is non-fiction, but it reflects the same clear and interesting style of his fiction. I would say that this book is not for a casual reader, nor was it intended as a textbook, though at some points it may seem that way. Artificial intelligence is a very interesting and widely misunderstood subject, and perhaps the greatest value of this book is in exploding some of the myths about AI. This is not a quick, easy read, but for the layman who wants to learn more about AI, it is well worth the time and effort involved in reading it.

The Psychic is a fairly interesting read, though it is a little cliched and seems to be have been written with one eye on the movie industry. Give it to your kid brother for xmas, if you cant afford what he really wants.

This book captures your imagination and opens your mind into the infinite possibilities of life and adventures with psychic powers. I was unable to put this book down and feel extremely fortunate I came upon this hidden novel. I am looking forward to the author's next book.

This book was an exceptional look into the haunting possiblities of the future spiced with sci-fi adventure that made this a great book. You are drawn into the characters and wish that you could share in Eddies powers in your own life. The possibilities of psychic power are opened up to you and you share in the voyeurism that it allows. I highly recommend this book to all.

Jay Sweeny, a young upstart author, sets this fast-paced sci-fi, romantic thriller on the West Coast of the US at the dawn of the introduction of the next and critical step in information technology--artificial intelligence. Eddie, the main character of the book is bombarded with unfortunate events that leave him with fresh powers that allow him to advance from a the greasy floor of a Fresno garage to the apex of the entire tech industry. He is almost driven insane in the process. Sweeney lets the reader in and out of Eddie's troubled brow as if he were psychic himself--omniscient. The antagonist, Victo Paine is sinister to the bone and as dark as any villian could hope to be. Sharing Eddie's gift, his revelations come as a colossal surprise to the unexpecting reader and his genius and cruelty have rarely been matched in literature. Verne, the mainframe with A.I. practically evolves into a human character in the story as he surprises even Winston, his creator, with a sense of judgement and morality--before unheard of in a machine. Sweeney poses huge questions regarding the future of the human race and our origins as well. At the same time he delivers a damn good story. Get ready to peer into the thoughts of peers and strangers alike--you may not like what you find. You may not be alone out there.

Publishers Weekly didn't like this book and thought it would only appeal to younger readers. Not so. Although intimidating in length, this book is extremely well written, and it explores some very thought-provoking issues about artificial intelligence, social change, and families. I thoroughly enjoyed it.

I have had this book for a while but was saving it as a reward for finishing a major project at work. On Friday I finished the project, and Friday evening I started reading Shelter. I finished it at 2:00 Saturday morning, having paused only to eat dinner. It's a deeply engaging read that is moving without indulging in pathos and that makes thought-provoking points without being heavy handed.

Palwick creates characters who are vivid enough that I find myself wanting to meet some (and hoping never to meet others), and does a masterful job of creating sympathy for even the least sympathetic people in the book. She also does a fantastic job of creating suspense through an interesting structure that moves back and forth between the past and present, showing effects long before exploring the causes.

This book isn't as tightly constructed as her previous novel, The Necessary Beggar, but it's also much larger in scope and ambition. It exceeded both my expectations and my hopes, both of which were very high.

In the future, colossal storms have devastated climates and the pandemic CV virus has killed off much of the human populace. MacroCorp CEO Preston Walford is a victim of the disease, but his intelligence is transferred into a virtual cyber-being. His "machine" has been declared legally a person as has some other transformations. This angers those opposed to artificial intelligence even if it originated in a God given natural state. However Preston could not care less about protestors who are like gnats in his mind; his plan is to change society into his image.

However, he keeps track of his estranged daughter Meredith, who cannot conceive so has adopted an African infant who survived the ravage of a virus. However, her Nicholas suffers from horrible nightmares of monsters that he must kill or die. Meredith tries to help her beloved son with the aid of excessive altruistic Roberta Danton and Fred the AI as mom realizes her child has been brainwiped probably by her father. Soon all those who offered solace will learn that the worst crime a person can commit is caring for another human.

This multifaceted science fiction contains two major complicated subplots that tie together through the Meredith's relationships with her dad and her son. One theme focuses on the war as big business AI supporters (dad) vs. the anti-AI radicals. However, that subplot though well written lacks the heart of the other major subplot, caring for Nicholas in a world where kindness and compassion for others can prove deadly. Science fiction fans will appreciate Susan Palwick's horrifying look at a near future San Francisco.

Harriet Klausner

I am a huge fan of Susan Palwick. My personal favorite Palwick book is The Necessary Beggar, but the others are also great.

Shelter is about a mentally ill child, an AI rights movement, a number of people bound together by complex family ties and social relations, and a homeless guy who takes care of cats. It is very gripping and you want to read it right through to the end.

Meredith wants to give shelter to her mentally ill child so he won't be brainwiped. Her child wants shelter from the monsters. Henry the homeless guy is looking for shelter to sleep in and shelter for his cats. Roberta wants the shelter of family after all of her original family die. The AIs and uploaded personalities are looking for the shelter of legal rights. Everyone is looking for physical shelter from a huge storm with flooding.

The amazon editorial review says this book is slow and recommends it for younger readers (since when do young people like slow things?). I would recommend it for sophisticated readers, whether older or younger, who can pick up on all the characters' complex emotions, and I read the book all the way through in a day and a half. It's not slow. It's just the size of a large dictionary. That means there's *more to read.*

I am learning by myself the topic of Genetic Algorithms (GA) for my PhD dissertation. Even though this book is written for John H. Holland considered the father of Genetics Algorithms, this is not a basic or easy reading book. The book does not contain any source code and even though it contains some kind of pseudocode, it will not give you a clear idea about how to implement a GA. If you want an introduction book maybe you should look for the Mitchell Melanie's book "An Introduction to Genetic Algorithms" , Fogel's book "Evolutionary Computation vol. 1" or Chamber's book "The Practical Handbook of Genetic Algorithms".
The way the author approaches the development of the framework is sometimes overwhelming because the author does not concentrate in one specific case or concept but he mentions all the different possibilities almost at the same time. I think it is worthwhile to buy the book to have it for advanced understanding of the concepts involved in the study of Complex Adaptive System. My approach to learn GA will be reading the above mentioned books and then study this book in a very detailed and slowly way to digest the huge amount of concepts and information provided by it.

This is a wonderful time. We can read about information theory in Shannon's own words, fuzzy logic in Zadeh's, relativity in Einstein's, and genetic programming in Holland's. He created evolutionary algorithms, and shares his thoughts in this brief work.

1975, when he first published this work, was a long time ago. Since then, computing has advanced, computing demands have advanced, and biology has advanced. Biology, because it functions at all the levels from atoms to worlds, has bottomless potential for insight. Because the atoms, the worlds, and everything between are all unfriendly, biology has many problems to solve. It doesn't matter whether you are an oak tree, a virus, or a whale, the solution (at the species level) is the same: evolve. Holland was the first to harness that incredible problem-solving power to computational use.

A huge literature has built up from Holland's founding thoughts. Those thoughts are here, in their original and purest form. It is hardly surprising that Holland anticipated so many elaborations of his work. One, in particular, struck me: the idea of 'hot spots' for genetic crossover. Or rather the opposite: 'cold spots' where crossover is inhibited. As a computer scientist, Holland's first thoughts were written in binary. When you allow points where crossover can not occur, you allow coherent multibit values - maybe even floating point. It's easy to laugh at Holland's initial naivete now, but he was talking about the foundations, not the structure built up from it.

If you have ever programmed genetic algorithms, you have been stunned by their effectiveness in creating good solutions. 'Good' doesn't mean precisely optimal, but pretty damm good anyway.

If you were a hard core creationist to start with, you still are. But now you know that evolutionary problem solving is powerful, broad, subtle, and effective - so much, that it's hard to believe it could ever have arisen by chance.

//wiredweird

This book presents an inspirational synthesis from mathematics, computer science and systems theory addressing genetic algorithms and their role in intelligent engineering/business systems.

Topics include: background, a formal framework, illustrations (genetics, economics, game playing, searches, pattern recognition and statistical inference, control and function optimization, and central-nervous system), schemata, the optimal allocation of trials, reproductive plans and genetic operators, the robustness of genetic plans, adaptation of coding and representations, and overview, interim and prospectus.

Inclusion of a disk of spreadsheet-based examples would have increased user-friendliness to the sometimes moderately-complex mathematics. Otherwise, this book is a well presented, and useful classic for researchers and software vendors seeking to develop more innovative intelligent products.

Has Artificial Intelligence just become a catchall phrase for any advanced algorithmic programming? Because if you peruse this book, you may consider some methods it describes as just being sophisticated algorithms, and not necessarily anything to do with AI per se.

Ok, let us not try to get bogged down into semantics. The book does have many nice, state of the art (2004) methods for handling diverse problems arising in game coding. For example, the section on speech recognition is quite well done. The main thing to take away from that section is that you need to restrict the problem so that the range of possible user responses is limited. Which may not necessarily be what that author intended. But from your point of view of easier and more robust coding, this is the pragmatic way to use speech recognition.

Of course, an AI purist might argue that what I have just suggested is not really AI. I agree. I use the speech recognition as just one example. You may derive the greatest benefit from this book if you deprecate the AI aspect and just see what methods you can usefully cull. Always remember that true AI is really hard. You are writing games. NOT research. Leave that to others.

Honestly, I just had to submit a review to comment on this line from a reviewer of this book. "Ok, let us not try to get bogged down into semantics." What the heck does this mean? I think he/she meant, "Okay, let us not get bogged down in semantics."
What else is wrong with this reviewer's reading/writing skills that contributed to their negative review of this book? If you can't write clearly...

All of the books in the Game Programming Gems and AI Wisdom series have been quite useful to me as a game programmer. AI Game Programming Wisdom 2 is a continuation of the Charles River Media series devoted to gaming issues. As with the rest of the series the articles are of varying lengths and detail. Many of the articles simply reference other articles in the series rather than basic or classic works in AI. So, this series is less useful as a bibliographic source. The topics covered in the articles are usually short and written at a high-level, but nonetheless very interesting and worth further research. And therein lies some of the frustration. It is often difficult to find more information about a particular subject since most of the references simply refer to articles in other books in the series, which are themselves are rather vague.

AI Game Programming Wisdom 2 is structured very much the same as the first book in the series. The articles are broken into similar sections, with the addition of a new section on finite-state-machines and splitting the section on learning into two. Having already covered the A* algorithm, its various optimizations, and navigation meshes in the previous books those topics are virtually absent here (though they may be buried as a secondary topic in some of the path-finding articles). I thought that the series had exhausted discussion of finite-state machines, but the editors managed to include a couple of interesting articles. The demand for more realistic AI behavior in character-based games in growing. And while a full simulation of emotions and human characteristics is unteneable, at the moment, there are some interesting ideas in the Learning section of the book e.g. "Motivational Graphs: A New Architecture for Complex Behavior Simulation.".

I can't say that this book is indispensible from an implementation perspective. Most of the articles are simply too vague - though there are notable exceptions throughout the entire series of books. What I found most useful in these articles was that they stimulated thought and helped to generate discussion among the programmers and designers at work.

The Defense Satellite Program (DSP)-truly one of the most innocuous code names ever devised for a critical military program-arose in the earliest years of the space age as a means of detecting the launch of a rocket from anywhere in the world. With the development of ballistic missiles in the 1950s, for the first time in the history of the United States our two great oceans could not protect us from sustained attack and destruction. To warn against a Soviet ballistic missile attack, and thereby to allow time for the launch of a counterattack, the Department of Defense sponsored the development of satellites ringing the globe that would use infrared photographic technology to detect missile launches. The theory behind the system was that the heat signature from the rocket blast would be detected by satellites in space and then show up on infrared scopes at military monitoring posts. Through this process the time and place of launch, as well as the missile trajectory, could be ascertained within seconds of launch.

It was a brilliant concept but it took years for it to come to fruition. The first effort, Project MIDAS, experienced numerous technical problems, but finally reached a turning point in 1963 when MIDAS 7 detected the first missile launch from space. MIDAS confirmed the concept, and the DSP program, with first launch in 1970, has provided early warning of missile launches ever since. Through 1997 eighteen DSP satellites had been placed in orbit, not all of them operational of course at the same time.

Jeffrey T. Richelson's history of this program, "America's Space Sentinels," is an especially important and welcome addition to the literature of the military space program. It provides as comprehensive an understanding of this effort as is possible in the current environment, using a wealth of declassified documents to piece together this program's evolution from idea to implementation and operational life. It is, of course, not the final word on this subject because of still-classified materials that should one day be made available about DSP, but it represents a benchmark in the historiography.

Especially welcome is Richelson's discussion of DSP's employment in the post-cold war era. He provides an excellent overview of its use to detect Scud missile launches in the Gulf War of 1991. He also describes how it detected the test firing of a new Iranian missile in 1998 and its use in piecing together the details of airplane accidents, such as the September 1997 collision of American and German military aircraft off the Atlantic coast of Africa. He ends with a discussion of the follow-on missile launch detection program, the Space-Based Infrared System (SBIRS), which is due to come on line at the end of the century.

From the time when DSP served as the backbone of the nation's strategic early warning system during the cold war through its continued use in the still very threatening climate of the 1990s to its replacement by a presumably more capable system, this book is an important contribution to the public's understanding of space-based military systems. It should be required reading for all who are interested in the strategic defense of the United States in the nuclear era.

This book gives an excellent overview of America's Missile Warning satellites, beginning with MIDAS in the 1960s and ending with SBIRS for the 21st Century. The information included in the text is more in-depth that the fact sheets given out by the United States Air Force.

Dr. Jeff Richelson has provided some of the greatest reads dealing with United States military satellite systems - "America's Secret Eyes in Space", "The US Intelligence Community", and now "America's Space Sentinels." This book covers the genesis and evolution of the US' space-based missile warning system, commonly known as DSP, or defense support program. Dr. Richelson takes us through the teething of MIDAS, Program 646, and DSP's early predecessors, in addition to its successors of ALERT and SBIRS.

One high point in the text is the information on SCUD missile launches during Operation Desert Storm. While the news media reported bits and pieces on the launches, Dr. Richelson gives us a "bird's eye" view of what crews in Colorado saw half-a-world away, and what their contribution did to the defense of the US troops in the desert.

The only downside to this book is its currency - This is a volume in definite need of updating due to the bringing online of the SBIRS constellation, the battles fought during the Clinton era about space systems and emerging technologies that have DSP/SBIRS ready for the scrap-heap. If you're a military space-nut like me, this needs to be in your library.

Mathematically sophisticated and quite original. The Finnish simulation, which accurately models language change, is particularly impresssive.

As a student who actually studied the theory of Analogical Modeling of Language under the author, Skousen, I found this book to be a fascinating and brilliant insight into linguistic analysis. Obviously, some study of language or linguistics is a helpful background for this book, but it is a straightforward explanation of Skousen's theories with ample evidence to back up his claims.

What is fascinating about Skousen's work is that it addresses the fundamental questions that Noam Chomsky often avoided in his theory of transformational grammar--how is it that we are able to use so many rules in language without ever having to go to school for them? Skousen's model basically addresses how the human brain is able to process language through "analogy"--in other words, Skousen's major supposition is that people learn to create language by creating datasets in their heads. As they get older, the datasets get larger. This may explain why certain children use tenses wrong (I thunk, I knowed it). Skousen's book also demonstrates exactly how to build a dataset and empirically research certain linguistic phenomenon. He uses one example, the a-an frequency in English. He demonstrates that using the article "a", as often as we do, shows an amazing amount of "leakage" toward making mistakes away from an. So a child with a smaller data set would end up making mistakes like "a apple", simply because the child's mind is so overcome with "a" examples that movement toward the "a" becomes possible. This textbook is a must for those interested in artificial intelligence, real language use, and linguistics. Shane Dixon (sydixon@hotmail.com)

This book is so confused about what it wants to say, it's like being trapped in a car with a neurotic babbler on a long roadtrip to nowhere. Furthermore, it's overly impressed with itself. Frankly, this book accomplished something that is rare in my readings: it left me less impressed with the subject than when I started out. After reading this, and rooting around for other literature, and projects, CBR, though it is based on a solid premise (from Philosophy, not science ;) ), is just a rhizome waiting for some soil and water.

This book is easily the best of its type, there is no other introductory book on CBR available! I bought this at AAAI-97 and found it extremely easy to read and sensibly layed out. It provides a good comparison of CBR with other AI and IS techniques and clearly explains what CBR's unique strengths (and to be fair) weaknesses are. A sensible range of application case-studies both academic and commercial are described and a wide range of CBR tools are described and compared. The book concludes with some methodological guidelines for building case-based systems. I would have no hesitation in recommending this book to undergraduate and postgraduate students who want an approachable introduction to CBR. The book will also be invaluable to commercial developers who are thinking of deploying a case-based system. Finally, the book's remarkably extensive bibliography, which is categorised to make finding references on specific subjects easy to find, will mean that ALL researchers in CBR should buy this book - it will save you days in the library. To conclude, an excellent book that will satisfy a wide range of readers

The author gives a comprehensible introduction to the field of Case Base Reasoning (CBR). He starts with an overview to the various techniques and algorithms to be used with CBR and formulates practical criteria when to use them and when not (e.g. adaptation). Very usefull are the differences to other AI approaches and the criteria to check the applicability of these methods. A chapter on CBR software tools reviews a series of available tools and development shells. Practical examples, especially on helpdesk applications show how CBR has been used successfully in projects. All in all the book demystifies CBR and encourages the use of it for building up decision support systems.

This is the very best book on classical AI. However, there's a catch, as classical AI has many pitfalls, such as the frame problem or the symbol grounding problem. But there are ways to overcome these pitfalls, and if you want to see what's really hot in AI today you should check out Douglas Hofstadter's Fluid Concepts and Creative Analogies.

Don't judge this book by its cover-or at least by its title. Haugeland's Artificial Intelligence: The Very Idea does not adequately serve as a general introduction to the conceptual underpinnings and philosophical background of the quest to create an artificial mind. Rather, it focuses on one specific approach to how natural and man-made thought works: "thinking...essentially is rational manipulation of mental symbols." (p. 4) Haugeland plows forward with this as his core assumption, barely noting that some AI researchers see thought from a very different perspective (for example, the connectionists) and others find the whole enterprise fraught with theoretical difficulty (such as Dreyfus).

So Haugeland's story is that of a particular theory of mind that held predominance for several decades (what the author himself dubs "good, old-fashioned artificial intelligence" or "GOFAI", p. 112) but is now gradually being superceded. His introduction to this story concludes with a description of the Turing test and a justification for its use, and a brief statement of the efficacy of describing a system in different-even contradictory-ways through different "organizational levels". (p. 9) Of all the ideas presented in the book, this last one has the greatest promise for applicability beyond GOFAI.

Chapter 1, "The Saga of the Modern Mind", is a condensed bit of intellectual history. Haugeland introduces the philosophical children of the Copernican revolution-Hobbes, Descartes, and Hume-and the ways they grappled with understanding the world of the mental with the ideas that had proven so effective in the physical sciences. We soon encounter the "paradox of mechanical reason": if reason is the meaningful manipulation of symbols, and meanings are not physical entities, then how can machines manipulate them? (p. 39)

Chapter 2 serves as an extended definition of "Automatic Formal Systems", that is, computers. This material is the most challenging in the text, but the important concepts (formal games, digital systems, medium independence, etc.), are well-described, except for finite playability. The students I tutored through this work found it impossible to determine just what point was being made, and so did I.

How does one assign meanings-connections to the "real", outside world-to the symbols that a computer manipulates? This question is taken up in Chapter 3, "Semantics"-and answered, it seems, by sleight-of-hand. Haugeland gives to this the name "the formalist's motto": "if you take care of the syntax, the semantics will take care of itself". (p. 106) Neither I nor my students found this simple resolution at all satisfying. In every example of a formal game that the author presents, whatever semantic interpretation it has is provided from outside the system.

Chapter 4, "Computer Architecture", charts the milestones of computing. It begins with the analytical engine, and lauds Babbage's single-handed invention of programming without noting, however, that a human mind does not resemble the tabula rasa of a computer's memory bank. Moving quickly to the twentieth century, we get insightful descriptions of Turing machines, von Neumann machines (which turn out to be the kind of computer we are accustomed to), the mind-bending tree-structured LISP machines, and Newell's pragmatic production machines.

Chapter 5, "Real Machines", might be better titled "Real Problems". Haugeland presents some of the brick walls that AI research has run into. These can be grouped into the phenomenon of the combinatorial explosion: in order to interact with the real world in a manner that demonstrates "common sense", an AI must have access to an impossibly large store of information (while accessing what it needs in due time), and be able to consider an equally impossibly large set of potential courses of action. (p. 178) Methods to restrict what the AI has to consider, such as the focus on "micro-worlds", result in a system with no sense. Haugeland acknowledges these problems, and offers nothing but hope in scientific and technological progress to answer them.

Chapter 6, "Real People", develops means by which the sense that humans exhibit, and machines are far from realizing. Dennett's intentional stances and Grice's conversational implicatures are intelligent-if partial-characterizations of perspicuous reasoning. They are, however, frustratingly slippery for computer programmers, so it's not surprising that Haugeland, with some exasperation, groups them together under the "nonasininity canon": "An enduring system makes sense to the extent that, as understood, it isn't making [a rear] of itself." (p. 219) I feel that, if a reader has followed the author this far, then he or she deserves better than this.

Yet Haugeland and his colleagues are bound to feel frustration. Computers are electromechanical in nature, while humans are neurochemical. Computers can engage in numerical calculation with speed and precision, while most people find mathematics to be their most difficult school subject. Computers are tools that we devised to assist us. Human behavior was forged in the four-billion cauldron of evolution, and psychologists have barely begun to sort out the seething stew of vestigial loves, hates, and motivations that shape our behavior. And honest cognitive science will admit that humans and supercomputers are each masters of two separate, very different worlds. At the end, Haugeland finally admits this possibility-without contemplating the alternatives to the computation theory of might that this possibility demands.

This is a great exposition of the fundamental notions involved in the philosophy of AI. While at first look may appear like a good undergraduate read, it is, in fact, quite subtle and deep in most of the material it touches. Great scholarship.

The heart of this book is the step-by-step instructions to construct seven different Lego robot chassis. This is just like any Lego instructions you will find, but it's not in color, and there is English text to add more material about the reasons behind the design.

In addition there is Mindstorms control code to accompany each chassis design. The programming language is Not Quite C (NQC). Installation instructions for NQC are included as this is a step you will have to take in addition to the standard Mindstorms installation.

The bots range from just a small construction around the RCX control block to designs that are 'gargantuan'. They are as sturdy and well designed as those that you find in the original Mindstorms kit.

I was disappointed that there was not more emphasis placed on describing the program logic. I would have appreciated flow charts as most of the control logic is simple state machines.

Overall I think this would be a fine book for anyone serious about Mindstorms sumo.

Competitive Mindstorms: A Complete Guide To Robotic Sumo Using Lego Mindstorms is a hobby guide to using the Lego Mindstorms Robotic Invention System to design, build, program, and unleash autonomous "sumo-bot" creations for use in a robotic sumo arena. Written by dedicated Lego Mindstorms enthusiast David Perdue, Competitive Mindstorms introduces the reader to the sumo-bot, provides seven projects designed to give hands-on experience in three different approaches to robotic sumo (small-and-fast class, medium class, and big-sumo strategies), presents extensive discussions of Lego pieces and building methodology, programming techniques, and much more. Black-and-white photographs illustrate the building instructions in this go-to guide especially friendly to beginners but packed with valuable tips, tricks, and techniques for the seasoned Lego Mindstorms hobbyist as well.

The Lego company has a Mindstorms product line that lets you build Lego models with simple programmable electronics. Easy but fascinating robots. Perdue offers you third party support, in the form of advice that is independent of the company. This book is an amalgam of neat assembly instructions and diagrams of robots to build, with accompanying source code to control them.

The Lego library is easy enough to understand. You code in C, and link to that library. So the outlook is procedural, not object oriented. But for the code examples shown, and for any code that you are likely to write, their sizes are small enough that a procedural approach is perfectly adequate. And with less overhead than an object oriented outlook.

A nice aspect is that you can tinker with both the hardware and software, in tight feedback design loops of changing something and testing it. In other projects, often it might be purely software. Which may not appeal to you, if you're the sort who is attracted to robotics.