Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Human–computer interaction wikipedia , lookup
Perceptual control theory wikipedia , lookup
Incomplete Nature wikipedia , lookup
Ethics of artificial intelligence wikipedia , lookup
Semantic Web wikipedia , lookup
Existential risk from artificial general intelligence wikipedia , lookup
Philosophy of artificial intelligence wikipedia , lookup
Embodied cognitive science wikipedia , lookup
Works Cited “Artificial intelligence.” World of Computer Science. N.p.: n.p., 2007. N. pag. Gale Science in Context. Web. 2 Oct. 2013. The article starts off with the definition of artificial intelligence (AI) and a brief history of AI development, which includes an explanation of the Turing Test. It then describes modern branches of AI. Some branches are concerned with reasoning and study rule-based/expert systems and data structures to develop intelligence. Others attempt to mimic nature, i.e. neural networks and genetic algorithms. AI draws on many disciplines, and knowledge from them is essential to advancing the sporadic progress in the field. This wasn’t very helpful. Sure, it gave plenty of background information I never knew, but it was more suited to those who wanted a broad overview of the field. Although each small topic had a decent amount of specific info on it, it didn’t mention distributed AI (swarming). It might come in handy if I need a reference on different AI techniques. Berdahl, Andrew. “Science paper: Emergent sensing of complex environments by mobile animal groups.” Collective Animal Behavior: CouzingLab@PrincetonUniversity. Couzin Lab, 2013. Web. 29 Oct. 2013. <http://icouzin.princeton.edu/newer-science-paper-emergentsensing-of-complex-environments-by-mobile animal-groups/>. Aside from the brief text on the page, there was a lovely video of emergent behavior in fish schools. It gave a nice visual and change of pace from all of the stuff I’d been reading. The fish follow two simple rules, slow down in dark areas, and swim toward other fish. In a large school, it enables the fish to seek out dark patches among light fairly quickly. It was mostly review for me; but I hadn’t heard of a study on fish before. The two simple rules were classic and a nice addition to my mental catalog. Nothing new though. Bell, Donald. “UML basics: An introduction to the Unified Modeling Language.” developerWorks. IBM developerWorks, n.d. Web. 23 Mar. 2014. - - -. “UML basics: The class diagram.” developerWorks. IBM developerWorks, n.d. Web. 23 Mar. 2014. - - -. “UML basics: The sequence diagram.” developerWorks. IBM developerWorks, n.d. Web. 23 Mar. 2014. Bonabeau, Eric, et al. “Swarm intelligence theory: A snapshot of the state of the art.” Theoretical Computer Science 411 (2010): 2081-83. Print. A quick overview of what’s going on in that particular journal, it told me what some of the cooler new things were, including advancements in the studies in PSO, ACO, and flocking of birds. One of them even had a new way to make an algorithm more efficient by modifying the parameters as it runs. Not useful for me in particular, but it does provide a fascinating guide of what’s going on. If I need to look up a particular article in this issue of the journal, it will be great, but otherwise, not really. “Chapter 1: Propositional Logic.” Department of Mathematics. California Institute of Technology, 2010. Web. 29, Oct. 2013. http://www.math.caltech.edu/~201011/3term/ma006c/10ma6cnotes1.pdf>. Defines propositional logic terms and situations involving it (examples). Includes: and, or, not, implies, and causal logic operations, as well as instructions on how to write syntactically correct statements (well formed formulas). This is great. A lot of the papers I read deal with set theory, math, and logic – i.e. propositional logic. One needs to understand these first before understanding anything else in these papers. “Chapter Two: Set Theory.” St. Lawrence University. St. Lawrence University. n.d. Web. 23 Mar. 2014. <http://myslu.stlawu.edu/~svanderv/chaptwo.pdf>. “Civil Space.” John Hopkins Applied Physics Laboratory. John Hopkins University Applied Physics Laboratory, 2013. Web. 1 Dec. 2013. <http://www.jhuapl.edu/ourwork/ civilspace/default.asp>. It tells me that we essentially work for NASA, and that we focus on “space physics and planetary science.” It also tells me that we work on end-to-end solutions and practical applications of new knowledge. Not very useful. Although it got the part about applying knowledge correct, the project I’m working on pertains more to the navy than to space or even the space department. Even then, I knew most of its message already, albeit not in such precise language. Collective Animal Behavior: CouzinLab@PrincetonUniversity. Couzin Lab, 2013. Web. 1 Dec. 2013. <http://icouzin.princeton.edu/>. The website is a place where the lab constantly updates the exciting, cool new things that they do – their research into swarming in animals – in plain (science) English. It’s essentially swarming in biology – from cells to animals to humans. It’s pretty cool and interesting – but it has absolutely nothing to do with diagnosing a navy ship. It’s just very interesting and fascinating light read. Collins, Kristine. Personal interview. N.d. Ms. Collins has more recently come out of college than her other colleagues, so she knows more accurately what’s needed in computer science, as well as which programming languages are the ones to be studying now. In addition, she knows what the work experience is like, especially what it’s like for a person just coming in from college, i.e. the level of independence needed. Ms. Collins is a great resource, especially if one wants to know what more recent trends in the job are like. She also very helpful if one needs help programming, as she programs a lot in the languages close to C and Python more often. Currie, Justin. MIT Godel Escher Bach Lecture 1. YouTube. N.p., n.d. Web. 2 Oct. 2013. <http://www.youtube.com/watch?v=IWZ2Bz0tS-s>. My first impression about this what that the teacher was a student; my second impression was that the “math” in it was much more philosophical than hard logic. It was certainly logical, but it was all about very abstract logic. It went over mainly four things, isomorphism, paradoxes, recursion, and formal systems, with an emphasis on the latter three. He went over four types of paradoxes, veridical, falsidical, zenos, and antinomies. Recursion was briefly discussed through fractals, and how Sierpinski’s triangle is actually exists in an irrational number of dimensions... The formal systems section questioned the nature of mathematical syntax, especially logic rules and number representation. During the entire lecture, Mr. Currie discussed the nature of when simple logic rules, formal systems, become something beyond that, such as the nature of the universe, intelligent life, and even cultural customs… Helpful if you’re trying to understand the fundamental question of the field of AI. That is to say, it’s not very helpful if you’re putting the finishing touches on an algorithm, but certainly very helpful if you’re starting to make an AI and need to see the big picture before building it. For my purposes, this isn’t very helpful because I often think about philosophy and am familiar with most of these concepts. - - -. MIT Godel Escher Bach Lecture 2. YouTube. N.p., n.d. Web. 2 Oct. 2013. <http://www.youtube.com/watch?v=HqmUuHnvJ98>. This did nothing but go over examples of fractals and its occurrences in nature and applications today. The explainer spent too much time drawing the fractals and expounding on interesting facts about them, such as fractal ferns. He wasn’t very good at explaining them either (he was substitute teacher). This wasn’t useful at all. Sure, I now know that coastlines and mountains are nothing more than randomized Koch curves in two and three dimensions respectively, and I am familiar with the Mandelbrot set, but what does that have to do with making AI? It doesn’t. - - -. MIT Godel Escher Bach Lecture 3. YouTube. N.p., n.d. Web. 2 Oct. 2013. <http://www.youtube.com/watch?v=86AHslduncM>. Describes formal systems more in detail, and explores what happens when we have problems with formal systems, such as un-provable truths and paradoxes. For example, when we question the un-provable truth, “parallel lines never intersect”, we get non-Euclidean geometry, which is extremely useful. Paradoxes are useful if we are to make an intelligent AI. It has to do with how paradoxes are created when a propositional statement can reference itself, such as, “This statement is false.” Compare this with humans, who can talk in reference to themselves all the time… It also briefly discusses different types of infinities. If one is trying to make any sort of logical system or even build an AI which can talk to you, this is great in the sense that gives you several caveats about building them. In a rule-based system, you can’t have objects/statements reference themselves, and if you’re building a program that can talk, (i.e. to pass the Turing test) you must take care that the program can handle referencing itself. Feldman, Alexander, Gregory Provan, and Arjan van Gemund. “A Model-Based Active Testing Approach to Sequential Diagnosis.” Journal of Artificial Intelligence Research 39 (2010): 301-34. Print. Exactly what is says on the cover. It details sequential diagnosis in excruciating detail and very big words. It shows a way to reduce the cardinality of possible test cases (hypotheses), and explores different methods to do so. This is extremely useful. Mr. Scheidt explicitly states that we will eventually go back to this later on in the year and that it is essential to have at least a basic understanding of this. John Hopkins Applied Physics Laboratory. John Hopkins U Applied Physics Laboratory, 2013. Web. 29 Oct. 2013. <http://www.jhuapl.edu/>. The JHUAPL website gives general info on the history, the people, and the topics they research. It often has quick biographies of the employees and the various programs they have, such as cybersecurity. Not particularly useful for me. The level of info on here is too general and not technical enough to be of any use in my project. Johnson, Steven. Emergence: The Connected Lives of Ants, Brains, Cities, and Software. New York: Simon, 2002. Print. The book gives an in depth explanation of what it means to have emergent intelligence (a complex adaptive system with emergent behavior), with plenty of concrete examples from biology, sociology, and computer science. “Here Comes Everybody!” (Introduction) poses the problem: how difficult it is to think in terms of the collective. Take slime mold. When put in a maze with food, this simple organism will plot the most efficient (shortest) way to all the food without a brain or any nervous system. Even more interesting is how it (they?) knows to aggregate. Normally, slime mold exists as individual protists, but when food is scarce, they will aggregate into a visible blob, a swarm. The question is how? Scientists had known that they secrete acrasin (cyclic AMP) and it was somehow involved in aggregation. For decades they had thought that pacemakers, “elite” cells, began production of AMP and then other started producing it in response to the first. AMP would wash over the protists in pulses; then they aggregated. With some applied math, it becomes apparent that there are no pacemakers. The protists secrete a trail of AMP of varying length based on how scarce food is. The scarcer the food, the longer the trail, and the more likely other protists are to find it. If a protist encounters enough trails, it will begin to cluster with others, and soon slime mold forms. However, the biologists didn’t understand the math behind it; they kept on looking for a pacemaker until a set of experiments convincingly showed the absence of one. “The Myth of the Ant Queen” (Chapter 1) gives an overview of what and how emergent behavior emerges. It begins with ants. People may assume that the ant queen directs all activity in the colony, when in fact she does nothing more than eat and reproduce for the colony. Each ant is exceedingly stupid and only acts as instinct dictates, yet somehow as a whole, they are able to do complex tasks. For example, they are able to pick out a site for a cemetery and a midden (garbage dump), as well as maximize the distance of the midden from the cemetery and the colony. Somehow they are able to solve a geometry puzzle some humans may not be able to solve without anyone directing them to; they each do their own thing. There is no need for a centralized intelligence to do an intelligent task; a swarm can do it without anything directing it to. The same happens with humans and computers. In one of the earliest human cities, Manchester, people built houses wherever they pleased. Yet somehow, they organized themselves into so rigidly defined districts that a rich person could walk the whole of his area without encountering any poor. In computers, this takes the form of genetic algorithms. “Street Level” (Chapter 2) is about exactly that – the “street level”, or more accurately, local interaction. The behavior of ants is determined by what each individual ant experiences on a local level, but it creates global behavior. For example, an ant stops foraging if she encounters too many foragers, thus creating a rough upper limit on the number of foragers. In addition, over the course of the colony’s 15 year life span, the colony learns behaviors, although the ants only live a year at most. Younger colonies are more fickle (respond to famine differently every time) and territorial, while older ones are more stable (respond to famine the same, as well as mutually agree to boundary lines). There are five rules to keep in mind to make a swarm work. 1) More is different. The behavior (and how we perceive it) is different if there are more ants. Ten ants can’t determine how many foragers are needed, but 2,000 can. Similarly, we can’t figure out what an ant is doing if we studied one individually – you must study it as part of the whole colony. 2) Ignorance is useful. It keeps them doing their jobs; the stupidity of the ants is a feature. You wouldn’t want an ant suddenly sprouting sentience in the same way you don’t want one of your neurons to sprout it. 3) Encourage random encounters. Decentralization relies on lots of random encounters. There are so many encounters; it allows an individual to learn about the macrostate of the system (statistics makes sure of this). 4) Look for patterns in the signs. Ants don’t need extensive vocabulary, but they do need to be aware of gradients in pheromones – the pheromone for, “food this way!” gets stronger in what direction. 5) Pay attention to your neighbors, or “Local information leads to global wisdom.” Without the ants paying attention to what pheromones are being secreted by other ants, the ability of the swarm to regulate itself is severely hampered. Human cells work this way too. They know what parts of DNA to read based on what the cells around them are reading. “The Pattern Match” (Chapter 3) is all about emergent behavior characteristics. They are patterns in time and space, and are not always contingent on consciousness. For example, a city is a form of emergent behavior. People organize themselves into districts (silk weavers in Florentine have stayed in a spot for centuries), and as a group, they can function as information storage and retrieval (a city quickly makes use of powerful technologies, effectively storing information about it). Emergent behavior emerges when the energy flowing through a system increases – i.e. increase in temp. = field of buttercups. In the same way, cities spontaneously emerge when energy (food supply) increases. Currently, the Internet isn’t exactly a pattern – a connection between two places. Consumers can go to websites, but there’s no way for the websites to know which individuals came. However, there’s a way to connect them. By tracking a consumer’s history, one can make associations between the websites and the consumer, effectively tracking his interests. This mutual feedback is essential to emergent systems. “Listening to Feedback” (Chapter 4) is about positive and negative feedback. Positive feedback tends to be inimical to emergent systems, giving too much power to a particular group instead of the people. For example, when the media begins talking about Gennifer Flowers, soon all of it is talking about her. A single media story is popular enough that other stations want to broadcast it, and soon, you will lose viewers if you don’t broadcast the story. A single story propagates itself again and again, multiplying its power, which in turn multiplies its power more. Negative feedback, in contrast, allows things to reach a balance - homeostasis. A shift in one direction results in a force in the opposite direction. For example, if a website contributor decides to spam, he may be voted out of his position, thereby resulting in less spam. The key to positive and negative feedback is that both sides have to be able to give it, and there’s the right amount of it. For example, both the media and public must be able to directly influence each other – i.e. the public votes on which media is worth watching, thereby boosting “good” media and relegating “bad” media. There can’t be too much or too little feedback either. Small communities are diverse and dynamic; any larger than 5,000 or so people and its growth becomes like cancer – unwanted and out of control. On the net, it keeps boards lively and meaningful. However, if there’s too little feedback from the masses (lurkers), then this gives power to cranks, critics of the posting majority – they assume the lurkers are on their side. If there’s too much feedback, other things may happen – for example, the clearly defined leaders, pranksters, respectful minority in an online community disappear into the giant flood of people. “Control Artist” (Chapter 5) describes the nature of trying to control emergent systems. It’s not a creationist, i.e. a programmer who builds his program for a specific function, and knows it will not do anything more. Controlling emergent systems is more like Darwinism – you set the initial conditions, and see what rises out of the primordial code. It’s very hard to predict what will happen. For example, just by looking at a simulation’s code (StarLogo) of an individual slime mold cell, you wouldn’t be able to tell that they will more likely aggregate into clusters if the population density increases. It just happens. Trying to optimize solutions from this also takes another Darwinistic approach: predators. Once a relatively optimal solution is reach, the program has no incentive to search for a (possibly) better solution, so you must introduce predator programs that lowers the success rate (directly) if it reaches a relative optimal solution. It’s also important that you don’t make your system too open-ended – otherwise it becomes too hard to program it. In gaming, it’s important to make sure your system doesn’t find the optimal solution – i.e. chess – so that a supercomputer like Deep Blue doesn’t take the fun out of it by playing for you. “The Mind Readers” (Chapter 6) explains self-awareness. It argues that self-awareness isn’t the result of increased intelligence; rather, it is the result of social awareness of others. In complex social hierarchies (human or animal), one must be able to predict what another of one’s kind is thinking and why. It puts evolutionary pressure on building theories of other minds. Such pressure eventually leads to the ability to figure what others but also what oneself is thinking about. The chapter also presents a possible future – one where programs figure out what TV you like via group associations, such as the envisioned Internet in chapter 3. The ads in this future will also present themselves to you based on your likes, making them much more useful to the individual and the seller. “See What Happens” (Chapter 7) speculates more about the future and states that there are multiple levels of organization within self-organized complexity. Atoms form chemicals which form cells which form humans which form societies which form the world… A fascinating read, Emergence is absolutely brilliant. It’s almost like a textbook for emergence, without any of the droll reading. Although a bit long, and almost philosophical (the examples balance this out), it teaches a basic understanding of emergent behavior without any of the technicalities of making one. It’s useful in so many fields; it’s highly useful for any mediumlevel beginner in A.I. “Knowledge representation.” World of Computer Science. N.p.: n.p., 2007. N. pag. Gale Science in Context. Web. 2 Oct. 2013. Knowledge representation (KR) is usually considered to be a problem in AI, but its methods can also be used in philosophy, linguistics, and psychology, and math. Many AI today use very specific techniques for KR, such as offering possible diagnoses of patient symptoms. On the other hand, humans can generalize their understanding from many sources of knowledge and apply it to many different areas. This is the core problem of KR – to replicate the human way to store knowledge. Solutions to the problem are partly inspired by the computational theory of the mind – that the human brain does nothing more than manipulate symbols in a logical and syntactic way. The question is how. Short of modeling the brain, other attempted solutions have included using propositional logic, predicate logic, ideas from fuzzy logic or Bayesian inference, and concepts from object oriented programming. Another problem in the field is the Frame Problem. The question is essentially, how does the computer recognize cause and effect? Humans recognize flipping a switch turns on a light, but a computer doesn’t. More useful than other things, it still covers mostly background info. It articulates the problem in KR very well, but it offers no truly insightful insights on how to solve the issue. The issue is much more tailored to AI, and beginner AI creators will find the problems and concepts posed in here useful but not always necessary to know. "Metrics and Test Plan for Experimentation of Control Algorithms on the NSWC Tabletop and JHU/APL Tabletop Simulator (Short Version)." 15 June 2009. TS. Lists and describes metrics – measurable quantities – of the operability of each of the simulated Tabletop’s components. For example, for the water cooled radars to be on, the radars must be less than 125 degree Fahrenheit and must have a power supply between 49 and 52 watts. It also uses many equations involving summations and integrals to create a metric for things that are not easily quantifiable, such as total mission operability and survivability. Useful reference for when I need info on how to determine the theoretical operability of the ship. It has equations and examples of ship operability and load models, which would help me greatly when setting up paper diagrams of how the program is to run. Pekala, M., N. Rolander, and D. Scheidt. "Tabletop Simulation Interface Specification." 2 July 2009. TS. It’s an overview of the Tabletop GUI – which program receives what kind of data from where. For example, the simulation will output arguments of type int32 and double. It also gives brief examples of what each argument means, i.e. the simulation time is represented with a double. At the end, it gives the mission objective using a propositional statement. It’s good for when I’m past the theoretical stage of algorithm development and I’m physically writing code. I’ll need to know the inputs, outputs, failure parameters, etc. Scheidt, David. Personal interview. 21 Oct. 2013. Mr. Scheidt offered a lot of insights into AI and the field of test planning. (For example, there’s a very large journal dedicated to the field.) He also offered a lot of illumination into topdown vs. bottom-up A.I., and the strengths/weaknesses associated with each. Top-down AI are easier to understand and create, but they can’t solve the same problems that bottom-up AI can. However, bottom-up AI are much harder to create, and sometimes it’s impossible to understand why they work. Testing them is even harder. Mr. Scheidt has a lot of experience in the field of AI; he’s great person to ask if you need info on specifics of the field. However, he’s a busy man, so keep the question few and interesting. “Scientific Papers.” Scitable. Nature Education, n.d. Web. 23 Mar. 2014.