In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive. The situated approach gives a much lower priority to abstract reasoning or problem-solving skills. The approach was originally proposed as an alternative to traditional approaches (that is, approaches popular before 1985 or so). After several decades, classical AI technologies started to face intractable issues (e.g. combinatorial explosion) when confronted with real-world modeling problems. All approaches to address these issues focus on modeling intelligences situated in an environment. They have become known as the situated approach to AI. == Emergence of a concept == === From traditional AI to Nouvelle AI === During the late 1980s, the approach now known as Nouvelle AI (Nouvelle means new in French) was pioneered at the MIT Artificial Intelligence Laboratory by Rodney Brooks. As opposed to classical or traditional artificial intelligence, Nouvelle AI purposely avoided the traditional goal of modeling human-level performance, but rather tries to create systems with intelligence at the level of insects, closer to real-world robots. But eventually, at least at MIT new AI did lead to an attempt for humanoid AI in the Cog Project. === From Nouvelle AI to behavior-based and situated AI === The conceptual shift introduced by nouvelle AI flourished in the robotics area, given way to behavior-based robotics (BBR), a methodology for developing AI based on a modular decomposition of intelligence. It was made famous by Rodney Brooks: his subsumption architecture was one of the earliest attempts to describe a mechanism for developing BBAI. It is extremely popular in robotics and to a lesser extent to implement intelligent virtual agents because it allows the successful creation of real-time dynamic systems that can run in complex environments. For example, it underlies the intelligence of the Sony Aibo and many RoboCup robot teams. Realizing that in fact all these approaches were aiming at building not an abstract intelligence, but rather an intelligence situated in a given environment, they have come to be known as the situated approach. In fact, this approach stems out from early insights of Alan Turing, describing the need to build machines equipped with sense organs to learn directly from the real-world instead of focusing on abstract activities, such as playing chess. == Definitions == Classically, a software entity is defined as a simulated element, able to act on itself and on its environment, and which has an internal representation of itself and of the outside world. An entity can communicate with other entities, and its behavior is the consequence of its perceptions, its representations, and its interactions with the other entities. === AI loop === Simulating entities in a virtual environment requires simulating the entire process that goes from a perception of the environment, or more generally from a stimulus, to an action on the environment. This process is called the AI loop and technology used to simulate it can be subdivided in two categories. Sensorimotor or low-level AI deals with either the perception problem (what is perceived?) or the animation problem (how are actions executed?). Decisional or high-level AI deals with the action selection problem (what is the most appropriate action in response to a given perception, i.e. what is the most appropriate behavior?). === Traditional or symbolic AI === There are two main approaches in decisional AI. The vast majority of the technologies available on the market, such as planning algorithms, finite-state machines (FSA), or expert systems, are based on the traditional or symbolic AI approach. Its main characteristics are: It is top-down: it subdivides, in a recursive manner, a given problem into a series of sub-problems that are supposedly easier to solve. It is knowledge-based: it relies on a symbolic description of the world, such as a set of rules. However, the limits of traditional AI, which goal is to build systems that mimic human intelligence, are well-known: inevitably, a combinatorial explosion of the number of rules occurs due to the complexity of the environment. In fact, it is impossible to predict all the situations that will be encountered by an autonomous entity. === Situated or behavioral AI === In order to address these issues, another approach to decisional AI, also known as situated or behavioral AI, has been proposed. It does not attempt to model systems that produce deductive reasoning processes, but rather systems that behave realistically in their environment. The main characteristics of this approach are the following: It is bottom-up: it relies on elementary behaviors, which can be combined to implement more complex behaviors. It is behavior-based: it does not rely on a symbolic description of the environment, but rather on a model of the interactions of the entities with their environment. The goal of situated AI is to model entities that are autonomous in their environment. This is achieved thanks to both the intrinsic robustness of the control architecture, and its adaptation capabilities to unforeseen situations. === Situated agents === In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if: they exist in a dynamic (rapidly changing) environment, which they can manipulate or change through their actions, and which they can sense or perceive. Examples might include web-based agents, which can alter data or trigger processes (such as purchases) over the Internet, or virtual-reality bots which inhabit and change virtual worlds, such as Second Life. Being situated is generally considered to be part of being embodied, but it is useful to consider each perspective individually. The situated perspective emphasizes that intelligent behavior derives from the environment and the agent's interactions with it. The nature of these interactions are defined by an agent's embodiment. == Implementation principles == === Modular decomposition === The most important attribute of a system driven by situated AI is that the intelligence is controlled by a set of independent semi-autonomous modules. In the original systems, each module was actually a separate device or was at least conceived of as running on its own processing thread. Generally, though, the modules are just abstractions. In this respect, situated AI may be seen as a software engineering approach to AI, perhaps akin to object oriented design. Situated AI is often associated with reactive planning, but the two are not synonymous. Brooks advocated an extreme version of cognitive minimalism which required initially that the behavior modules were finite-state machines and thus contained no conventional memory or learning. This is associated with reactive AI because reactive AI requires reacting to the current state of the world, not to an agent's memory or preconception of that world. However, learning is obviously key to realistic strong AI, so this constraint has been relaxed, though not entirely abandoned. === Action selection mechanism === The situated AI community has presented several solutions to modeling decision-making processes, also known as action selection mechanisms. The first attempt to solve this problem goes back to subsumption architectures, which were in fact more an implementation technique than an algorithm. However, this attempt paved the way to several others, in particular the free-flow hierarchies and activation networks. A comparison of the structure and performances of these two mechanisms demonstrated the advantage of using free-flow hierarchies in solving the action selection problem. However, motor schemas and process description languages are two other approaches that have been used with success for autonomous robots. == Notes and references == Arsenio, Artur M. (2004) Towards an embodied and situated AI, In: Proceedings of the International FLAIRS conference, 2004. (online) The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents, Luc Steels and Rodney Brooks Eds., Lawrence Erlbaum Publishing, 1995. (ISBN 978-0805815184) Rodney A. Brooks Cambrian Intelligence (MIT Press, 1999) ISBN 0-262-52263-2; collection of early papers including "Intelligence without representation" and "Intelligence without reason", from 1986 & 1991 respectively. Ronald C. Arkin Behavior-Based Robotics (MIT Press, 1998) ISBN 0-262-01165-4 Hendriks-Jansen, Horst (1996) Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought. Cambridge, Mass.: MIT Press.
Cuboid (computer vision)
In computer vision, the term cuboid is used to describe a small spatiotemporal volume extracted for purposes of behavior recognition. The cuboid is regarded as a basic geometric primitive type and is used to depict three-dimensional objects within a three dimensional representation of a flat, two dimensional image. == Production == Cuboids can be produced from both two-dimensional and three-dimensional images. One method used to produce cuboids utilizes scene understanding (SUN) primitive databases, which are collections of pictures that already contain cuboids. By sorting through SUN primitive databases with machine learning tools, computers observe the conditions in which cuboids are produced in images from SUN primitive databases and can learn to produce cuboids from other images. RGB-D images, which are RGB images that also record the depth of each pixel, are occasionally used to produce cuboids because computers no longer need to determine the depth of an object, as they typically do because depth is already recorded. Cuboid production is sensitive to changes in color and illumination, blockage, and background clutter. This means that it is difficult for computers to produce cuboids of objects that are multicolored, irregularly illuminated, or partially covered, or if there are many objects in the background. This is partially due to the fact that algorithms for producing cuboids are still relatively simple. == Usage == Cuboids are created for point cloud-based three-dimensional maps and can be utilized in various situations such as augmented reality, the automated control of cars, drones, and robots, and object detection. Cuboids allow for software to identify a scene through geometric descriptions in an “object-agnostic” fashion. Interest points, locations within images that are identified by a computer as essential to identifying the image, created from two-dimensional images can be used with cuboids for image matching, identifying a room or scene, and instance recognition. Interest points created from three dimensional images can be used with cuboids to recognize activities. This is possible because interest points aid software to focus on only the most important aspects of the images. RGB-D images and SLAM systems are used together in RGB-D SLAM systems, which are employed by Computer-aided design systems to generate point cloud-based three-dimensional maps. Most industrial multi-axis machining tools use computer-aided manufacturing and subsequently work in cuboid work spaces.
Malleability (cryptography)
Malleability is a property of some cryptographic algorithms. An encryption algorithm is said to be malleable if it is possible to transform a ciphertext into another ciphertext which decrypts to a related plaintext. That is, given an encryption of a plaintext m {\displaystyle m} , it is possible to generate another ciphertext which decrypts to f ( m ) {\displaystyle f(m)} , for a known function f {\displaystyle f} , without necessarily knowing or learning m {\displaystyle m} . Malleability is often an undesirable property in a general-purpose cryptosystem, since it allows an attacker to modify the contents of a message. For example, suppose that a bank uses a stream cipher to hide its financial information, and a user sends an encrypted message containing, say, "TRANSFER $0000100.00 TO ACCOUNT #199." If an attacker can modify the message on the wire, and can guess the format of the unencrypted message, the attacker could change the amount of the transaction, or the recipient of the funds, e.g. "TRANSFER $0100000.00 TO ACCOUNT #227". Malleability does not refer to the attacker's ability to read the encrypted message. Both before and after tampering, the attacker cannot read the encrypted message. On the other hand, some cryptosystems are malleable by design. In other words, in some circumstances it may be viewed as a feature that anyone can transform an encryption of m {\displaystyle m} into a valid encryption of f ( m ) {\displaystyle f(m)} (for some restricted class of functions f {\displaystyle f} ) without necessarily learning m {\displaystyle m} . Such schemes are known as homomorphic encryption schemes. A cryptosystem may be semantically secure against chosen-plaintext attacks or even non-adaptive chosen-ciphertext attacks (CCA1) while still being malleable. However, security against adaptive chosen-ciphertext attacks (CCA2) is equivalent to non-malleability. == Example malleable cryptosystems == In a stream cipher, the ciphertext is produced by taking the exclusive or of the plaintext and a pseudorandom stream based on a secret key k {\displaystyle k} , as E ( m ) = m ⊕ S ( k ) {\displaystyle E(m)=m\oplus S(k)} . An adversary can construct an encryption of m ⊕ t {\displaystyle m\oplus t} for any t {\displaystyle t} , as E ( m ) ⊕ t = m ⊕ t ⊕ S ( k ) = E ( m ⊕ t ) {\displaystyle E(m)\oplus t=m\oplus t\oplus S(k)=E(m\oplus t)} . In the RSA cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = m e mod n {\displaystyle E(m)=m^{e}{\bmod {n}}} , where ( e , n ) {\displaystyle (e,n)} is the public key. Given such a ciphertext, an adversary can construct an encryption of m t {\displaystyle mt} for any t {\displaystyle t} , as E ( m ) ⋅ t e mod n = ( m t ) e mod n = E ( m t ) {\textstyle E(m)\cdot t^{e}{\bmod {n}}=(mt)^{e}{\bmod {n}}=E(mt)} . For this reason, RSA is commonly used together with padding methods such as OAEP or PKCS1. In the ElGamal cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = ( g b , m A b ) {\displaystyle E(m)=(g^{b},mA^{b})} , where ( g , A ) {\displaystyle (g,A)} is the public key. Given such a ciphertext ( c 1 , c 2 ) {\displaystyle (c_{1},c_{2})} , an adversary can compute ( c 1 , t ⋅ c 2 ) {\displaystyle (c_{1},t\cdot c_{2})} , which is a valid encryption of t m {\displaystyle tm} , for any t {\displaystyle t} . In contrast, the Cramer-Shoup system (which is based on ElGamal) is not malleable. In the Paillier, ElGamal, and RSA cryptosystems, it is also possible to combine several ciphertexts together in a useful way to produce a related ciphertext. In Paillier, given only the public key and an encryption of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} , one can compute a valid encryption of their sum m 1 + m 2 {\displaystyle m_{1}+m_{2}} . In ElGamal and in RSA, one can combine encryptions of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} to obtain a valid encryption of their product m 1 m 2 {\displaystyle m_{1}m_{2}} . Block ciphers in the cipher block chaining mode of operation, for example, are partly malleable: flipping a bit in a ciphertext block will completely mangle the plaintext it decrypts to, but will result in the same bit being flipped in the plaintext of the next block. This allows an attacker to 'sacrifice' one block of plaintext in order to change some data in the next one, possibly managing to maliciously alter the message. This is essentially the core idea of the padding oracle attack on CBC, which allows the attacker to decrypt almost an entire ciphertext without knowing the key. For this and many other reasons, a message authentication code is required to guard against any method of tampering. == Complete non-malleability == Fischlin, in 2005, defined the notion of complete non-malleability as the ability of the system to remain non-malleable while giving the adversary additional power to choose a new public key which could be a function of the original public key. In other words, the adversary shouldn't be able to come up with a ciphertext whose underlying plaintext is related to the original message through a relation that also takes public keys into account.
Virtual collective consciousness
Virtual collective consciousness (VCC) is a term rebooted and promoted by two behavioral scientists, Yousri Marzouki and Olivier Oullier in their 2012 Huffington Post article titled: "Revolutionizing Revolutions: Virtual Collective Consciousness and the Arab Spring", after its first appearance in 1999-2000. VCC is now defined as an internal knowledge catalyzed by social media platforms and shared by a plurality of individuals driven by the spontaneity, the homogeneity, and the synchronicity of their online actions. VCC occurs when a large group of persons, brought together by a social media platform think and act with one mind and share collective emotions. Thus, they are able to coordinate their efforts efficiently, and could rapidly spread their word to a worldwide audience. When interviewed about the concept of VCC that appeared in the book - Hyperconnectivity and the Future of Internet Communication - he edited, Professor of Pervasive Computing, Adrian David Cheok mentioned the following: "The idea of a global (collective) virtual consciousness is a bottom-up process and a rather emergent property resulting from a momentum of complex interactions taking place in social networks. This kind of collective behaviour (or intelligence) results from a collision between a physical world and a virtual world and can have a real impact in our life by driving collective action." == Etymology == In 1999-2000, Richard Glen Boire provided a cursory mention and the only occurrence of the term "Virtual collective consciousness" in his text as follows: The trend of technology is to overcome the limitations of the human body. And, the Web has been characterized as a virtual collective consciousness and unconsciousness The recent definition of VCC evolved from the first empirical study that provided a cyberpsychological insight into the contribution of Facebook to the 2011 Tunisian revolution. In this study, the concept was originally called "collective cyberconsciousness". The latter is an extension of the idea of "collective consciousness" coupled with "citizen media" usage. The authors of this study also made a parallel between this original definition of VCC and other comparable concepts such as Durkheim's collective representation, Žižek's "collective mind" or Boguta's "new collective consciousness" that he used to describe the computational history of the Internet shutdown during the Egyptian revolution. Since VCC is the byproduct of the network's successful actions, then these actions must be timely, acute, rapid, domain-specific, and purpose-oriented to successfully achieve their goal. Before reaching a momentum of complexity, each collective behavior starts by a spark that triggers a chain of events leading to a crystallized stance of a tremendous amount of interactions. Thus, VCC is an emergent global pattern from these individual actions. In 2012, the term virtual collective consciousness resurfaced and was brought to light after extending its applications to the Egyptian case and the whole social networking major impact on the success of the so-called Arab Spring. Moreover, the acronym VCC was suggested to identify the theoretical framework covering on-line behaviors leading to a virtual collective consciousness. Hence, online social networks have provided a new and faster way of establishing or modifying "collective consciousness" that was paramount to the 2011 uprisings in the Arab world. == Theoretical underpinnings of VCC == Various theoretical references in fields ranging from sociology to computer science were mentioned in order to account for the key features that render the framework for a virtual collective consciousness. The following list is not exhaustive, but the references it contains are often highlighted: Émile Durkheim's collective representations are at the heart of VCC since collectivity taken decisions according to Durkheim's assumptions will approve or disapprove individuals' actions and help them eventually reach their final goal. Marshall McLuhan's global village: The shrinking of our big world to a small place called cyberspace is made possible by technological extensions of human consciousness. Carl Jung's collective unconscious: When a society witnesses significant changes, the anchoring of archetypal images (e.g., political leaders) seems to be deeply rooted in individuals' collective unconscious that is likely to bias their political choices. Individual memories of public events were also supposed to convey a "collective awareness" that can be subconsciously altered by the instantaneous spread of information through social networking around the world. Daniel Wegner's transactive memory (TM): social-networking platforms such as Facebook during the Tunisian revolution or Twitter during the Egyptian revolution served as placeholders of a VCC where information can be harnessed and steered to the highly specific revolutionary purpose. Although research on TM was originally limited to couples, small groups, and organizations, recent studies strongly suggest that an effective TM can operate on a very large scale too. James Surowiecki's wisdom of crowds Collective influence algorithm: The CI (Collective influence) algorithm is effective in finding influential nodes in a variety of networks, including social networks, communication networks, and biological networks. It has been used to identify influencers on social-media platforms, to identify key nodes in transportation networks, and to identify potential drug-targets in biological networks. == Some illustrations of VCC == Besides the studied effect of social networking on the Tunisian and Egyptian revolutions, the former via Facebook and the latter via Twitter other applications were studied under the prism of VCC framework: The Whitacre's virtual choir: A compelling example of the degree of autonomy and self-identity members of a spontaneously created network through a VCC is Eric Whitacre's unique musical project that involved a collection of singers performing remotely to create a virtual Choir. The effect of all the voices illustrated a genuine virtual collective empathy merging the artist's mind with all the singers through his silent conducting gestures. The Harlem Shake dance: The Bitcoin protocol: It was questioned whether or not the Bitcoin protocol can morph into virtual collective consciousness. The Byzantine generals problem was used as an analogy to understand the behavioral complexity of the community of Bitcoin's users. Artificial Social Networking Intelligence (ASNI): refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize users' interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping our digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact.
Cryptographic multilinear map
A cryptographic n {\displaystyle n} -multilinear map is a kind of multilinear map, that is, a function e : G 1 × ⋯ × G n → G T {\displaystyle e:G_{1}\times \cdots \times G_{n}\rightarrow G_{T}} such that for any integers a 1 , … , a n {\displaystyle a_{1},\ldots ,a_{n}} and elements g i ∈ G i {\displaystyle g_{i}\in G_{i}} , e ( g 1 a 1 , … , g n a n ) = e ( g 1 , … , g n ) ∏ i = 1 n a i {\displaystyle e(g_{1}^{a_{1}},\ldots ,g_{n}^{a_{n}})=e(g_{1},\ldots ,g_{n})^{\prod _{i=1}^{n}a_{i}}} , and which in addition is efficiently computable and satisfies some security properties. It has several applications on cryptography, as key exchange protocols, identity-based encryption, and broadcast encryption. There exist constructions of cryptographic 2-multilinear maps, known as bilinear maps, however, the problem of constructing such multilinear maps for n > 2 {\displaystyle n>2} seems much more difficult and the security of the proposed candidates is still unclear. == Definition == === For n = 2 === In this case, multilinear maps are mostly known as bilinear maps or pairings, and they are usually defined as follows: Let G 1 , G 2 {\displaystyle G_{1},G_{2}} be two additive cyclic groups of prime order q {\displaystyle q} , and G T {\displaystyle G_{T}} another cyclic group of order q {\displaystyle q} written multiplicatively. A pairing is a map: e : G 1 × G 2 → G T {\displaystyle e:G_{1}\times G_{2}\rightarrow G_{T}} , which satisfies the following properties: Bilinearity ∀ a , b ∈ F q ∗ , ∀ P ∈ G 1 , Q ∈ G 2 : e ( a P , b Q ) = e ( P , Q ) a b {\displaystyle \forall a,b\in F_{q}^{},\ \forall P\in G_{1},Q\in G_{2}:\ e(aP,bQ)=e(P,Q)^{ab}} Non-degeneracy If g 1 {\displaystyle g_{1}} and g 2 {\displaystyle g_{2}} are generators of G 1 {\displaystyle G_{1}} and G 2 {\displaystyle G_{2}} , respectively, then e ( g 1 , g 2 ) {\displaystyle e(g_{1},g_{2})} is a generator of G T {\displaystyle G_{T}} . Computability There exists an efficient algorithm to compute e {\displaystyle e} . In addition, for security purposes, the discrete logarithm problem is required to be hard in both G 1 {\displaystyle G_{1}} and G 2 {\displaystyle G_{2}} . === General case (for any n) === We say that a map e : G 1 × ⋯ × G n → G T {\displaystyle e:G_{1}\times \cdots \times G_{n}\rightarrow G_{T}} is an n {\displaystyle n} -multilinear map if it satisfies the following properties: All G i {\displaystyle G_{i}} (for 1 ≤ i ≤ n {\displaystyle 1\leq i\leq n} ) and G T {\displaystyle G_{T}} are groups of same order; if a 1 , … , a n ∈ Z {\displaystyle a_{1},\ldots ,a_{n}\in \mathbb {Z} } and ( g 1 , … , g n ) ∈ G 1 × ⋯ × G n {\displaystyle (g_{1},\ldots ,g_{n})\in G_{1}\times \cdots \times G_{n}} , then e ( g 1 a 1 , … , g n a n ) = e ( g 1 , … , g n ) ∏ i = 1 n a i {\displaystyle e(g_{1}^{a_{1}},\ldots ,g_{n}^{a_{n}})=e(g_{1},\ldots ,g_{n})^{\prod _{i=1}^{n}a_{i}}} ; the map is non-degenerate in the sense that if g 1 , … , g n {\displaystyle g_{1},\ldots ,g_{n}} are generators of G 1 , … , G n {\displaystyle G_{1},\ldots ,G_{n}} , respectively, then e ( g 1 , … , g n ) {\displaystyle e(g_{1},\ldots ,g_{n})} is a generator of G T {\displaystyle G_{T}} There exists an efficient algorithm to compute e {\displaystyle e} . In addition, for security purposes, the discrete logarithm problem is required to be hard in G 1 , … , G n {\displaystyle G_{1},\ldots ,G_{n}} . === Candidates === All the candidates multilinear maps are actually slightly generalizations of multilinear maps known as graded-encoding systems, since they allow the map e {\displaystyle e} to be applied partially: instead of being applied in all the n {\displaystyle n} values at once, which would produce a value in the target set G T {\displaystyle G_{T}} , it is possible to apply e {\displaystyle e} to some values, which generates values in intermediate target sets. For example, for n = 3 {\displaystyle n=3} , it is possible to do y = e ( g 2 , g 3 ) ∈ G T 2 {\displaystyle y=e(g_{2},g_{3})\in G_{T_{2}}} then e ( g 1 , y ) ∈ G T {\displaystyle e(g_{1},y)\in G_{T}} . The three main candidates are GGH13, which is based on ideals of polynomial rings; CLT13, which is based approximate GCD problem and works over integers, hence, it is supposed to be easier to understand than GGH13 multilinear map; and GGH15, which is based on graphs.
CHAOS (chess)
CHAOS (Chess Heuristics and Other Stuff) is a chess playing program that was developed by programmers working at the RCA Systems Programming division in the late 1960s. It played competitively in computer chess competitions in the 1970s and 1980s. It differed from other programs of that era in its look-ahead philosophy, choosing to use chess knowledge to evaluate fewer positions and continuations as opposed to simple evaluations that relied on deep look-ahead to avoid bad moves. == Introduction == CHAOS was originally developed by Ira Ruben, Fred Swartz, Victor Berman, Joe Winograd and William Toikka while working at RCA in Cinnaminson, NJ. Its name is an acronym for 'Chess Heuristics and Other Stuff.' Program development moved to the Computing Center of the University of Michigan when Swartz changed jobs, and Mike Alexander joined the development group. Swartz, Alexander and Berman were continuously group members from that point onward in CHAOS' evolution, as others of the original authors left and new members contributed episodically. Chess Senior Master Jack O'Keefe contributed to CHAOS' development from about 1980 onwards. CHAOS was written in Fortran, except for low-level board representation manipulations written in assembly language or C. Due to this portability, it ran on RCA, Univac and IBM-compatible mainframes in its lifetime. CHAOS heralds from the mainframe computing era when only machines of that capacity were able to play at a high level. Consequently, development and testing could only take place at off-peak times for production use of the machine. In a competition, CHAOS had to run on a dedicated mainframe with a telephone link to the match venue. In its later years, CHAOS ran on computers on the machine assembly floor of Amdahl Corporation on MTS. == Background == === Chess and artificial intelligence === Mathematicians Claude Shannon and Alan Turing, working separately, were the first to view playing chess as a challenge to machines. Working for AT&T / Bell Labs with its access to telephone switching equipment, Shannon built a relay-based machine that learned how to work its way through a two-dimensional, 5x5 cell maze in 1949. Shannon viewed this as an analogue of the way that organisms learn things about their natural environment. There is a random element to searching it, a memory element to benefit from the search outcome, and a reward element that reinforces learning when the global outcome is favorable to the organism. Soon afterward, Shannon wrote a mathematical analysis of the game of chess, published in 1950. Like with the maze, he broke down game play into the necessary elements for reinforcement learning. Associated with each board configuration a move will be made from, there is a numerical score. To decide what move to make, a player wants to maximize their own position's score after the move and to minimize their opponent's score (a minimax view). Since there are about 32 possible moves at each of the early stages of the game, and about 40 moves and responses in each game, then there are about 32 80 {\displaystyle 32^{80}} or about 10 120 {\displaystyle 10^{120}} possible games - an impossibly large set to evaluate completely. Therefore, there must be a way to limit the number of moves to look ahead for to find the best one. Reducing the game to these few key elements provided a way to think about human intelligence in general. Shannon became part of a wider group using computing machines to mimic aspects of human intelligence that grew into the general idea of artificial intelligence. (Other members of this group were John McCarthy, Herbert Simon, Allen Newell, Alan Kotok, Alex Bernstein and Richard Greenblatt.) The paradigm that evolved was that there was a quantification of the position on the board into a score, an evaluation method to find favorable outcomes (minimax, later alpha-beta pruning), and a strategy to manage the combinatorial explosion of the look-ahead possibilities. By the early 1960s, there were computer programs that played chess at a rudimentary level. They used very simple evaluation functions for each position and tried to search as far forward as was practical given the time constraints and available compute power. Naturally, programmers optimized their code to use the available computing resources. This led to a major philosophical divide among chess programs: those that tried to evaluate as many positions as possible, and those that tried to evaluate the most promising move sequences as deeply as possible. CHAOS was firmly in the camp believing only the most promising moves should be evaluated in depth. Said Swartz, "The 'brute force people' ... look at every (possible move) no matter what garbage it is. Most moves are just terrible, terrible moves, and most computing time is being spent on pure garbage." The program spent more time evaluating each board position in the expectation that it would find the most promising lines of play to explore in depth. In 1983, the then-fastest chess program (Belle) evaluated 110,000 positions per second, and typical programs 1000–50,000 per second, whereas CHAOS evaluated about 50-100 per second. === Machine learning and strategies to manage search === From about 1949 onward, Arthur Samuel began work for IBM on machine learning, culminating in a checkers-playing program in 1952 and publications on the topic. Concurrently, Christopher Strachey created Checkers, a program to play the board game of checkers in 1951, but it had no capacity to learn from its play. Checkers was chosen by both authors because it was simpler than chess yet contained the basic characteristics of an intellectual activity, and, in Samuel's view, was a test-bed in which heuristic procedures and learning processes could be evaluated quickly. Checker playing programs introduced the notion of the game tree and evaluating play to various depths to choose the best move. The complexity of chess, however, promoted it to the status of an analogue for human intelligence, and it attracted computer scientists' attention, who referred to it as research into artificial intelligence (AI). Like checkers, it required a numerical assessment of each arrangement of chess pieces on a board. It also required looking ahead to future moves to decide how to play the present position. Due to the enormous number of possible moves, there had to be a way to confine the look-ahead search to the most promising lines of play. From these factors, the notion of minimax score evaluation developed and, later, alpha-beta tree pruning to abandon looking at positions worse than any that have already been examined. === Chess search strategies === The AI community viewed artificial intelligence as comprising two parts: a way to symbolically quantify the knowledge in hand (a chess board position), and a set of heuristics to limit look-ahead to the consequences of a move. The early chess playing programs attempted to look forward as far as possible, perhaps to 3 moves ahead by each player, and to choose the best outcome. This led to the horizon effect, whereby a key move 4 or more moves ahead would be unexamined and therefore missed. Consequently, the programs were quite weak and heuristics to manage the search became important in their development. CHAOS used a selective search strategy with iterative widening. As chess programs evolved, they incorporated books of opening lines of play from historic sources. Nowadays, book moves are catalogued in machine-readable form, but originally programmers had to type them in. CHAOS had an extensive book for its time of around 10,000 moves that O'Keefe helped to develop. A problem with play from an opening book is the behavior of the program when the play leaves the book: the positional advantage may be so subtle that the evaluation scheme may be unable to understand it, leading to very wide and shallow searches to establish a line of play. The horizon effect again plagues move selection after leaving the book. CHAOS mitigated these problems by only using book lines that it could understand, and by relying on cached analyses of continuations out of the book made while the opponent's clock was running. == Game Play History == CHAOS played in twelve ACM computer chess tournaments and four World Computer Chess Championships (WCCC). Its debut was the ACM computer chess tournament in 1973, taking 2nd place. In 1974, it again won 2nd place in the WCCC, defeating the tournament favorite Chess 4.0 but losing to Kaissa. CHAOS was close to winning the 1980 WCCC, but lost to Belle in a playoff. The 1985 ACM computer chess tournament was CHAOS' last competition. One of CHAOS' notable victories was over Chess 4.0 at the 1974 WCCC tournament. Chess 4.0 was unbeaten by any other program up until then. Playing as white, CHAOS made a knight sacrifice (16 Nd4-e6!!) that traded material for open lines of attack and eventually won the game. CHAOS’ authors thought the move was due to a
Open Data-Link Interface
The Open Data-Link Interface (ODI) is an application programming interface (API) for network interface controllers (NICs) developed by Apple and Novell. The API serves the same function as Microsoft and 3COM's Network Driver Interface Specification (NDIS). Originally, ODI was written for NetWare and Macintosh environments. Like NDIS, ODI provides rules that establish a vendor-neutral interface between the protocol stack and the adapter driver. It resides in Layer 2, the Data Link layer, of the OSI model. This interface also enables one or more network drivers to support one or more protocol stacks.