Question 1

In Lisp programming, express the mathematical function f(x) = 2x + 1 as a proper Lisp function literal using lambda notation and prefix operators.

Accepted Answer

Introduction / Context: Lisp uses prefix notation and functional forms to represent expressions. Writing a mathematical function such as f(x) = 2x + 1 requires understanding of lambda expressions, function application, and the use of arithmetic operators in prefix form. Given Data / Assumptions: Target function: f(x) = 2x + 1. We want an anonymous function (lambda) rather than a named top-level function. Standard Lisp prefix arithmetic operators are available. Concept / Approach: In Lisp, multiplication is written as (* a b) and addition as (+ a b). A lambda expression is written (lambda (args) body). Thus, to compute 2x + 1, we multiply x by 2 and then add 1 to the result inside the lambda body. Step-by-Step Solution: Represent 2x as (* 2 x).Add 1 to that product as (+ (* 2 x) 1).Wrap with a lambda taking x: (lambda (x) (+ (* 2 x) 1)).Optional: To name the function, use (defun f (x) (+ (* 2 x) 1)). Verification / Alternative check: Apply the lambda to a test value: ((lambda (x) (+ (* 2 x) 1)) 3) evaluates to 7, which matches 23+1. Why Other Options Are Wrong: Option b has equivalent arithmetic but extra spacing; still correct mathematically, but option a is the canonical minimal form we seek. Option c computes 2(x+1), which is 2x+2, not 2x+1. Option d defines a named function; the prompt asks for a lambda expression literal. Common Pitfalls: Accidentally using infix notation or omitting parentheses leads to syntax errors. Ensure operators are in prefix form and expressions are fully parenthesized. Final Answer: (lambda (x) (+ (* 2 x) 1))

Question 2

Industrial robotics teaching methods: Programming a robot by physically guiding the end effector along the desired trajectory so it records and repeats the path is known as which control method?

Accepted Answer

Introduction / Context: Robots can be programmed by various methods. One practical technique involves physically moving the robot arm through the intended path while the controller samples and stores positions. This method is associated with applications requiring smooth trajectories such as painting or welding. Given Data / Assumptions: Operator physically moves the robot along the desired path. Controller records many intermediate points or a continuous stream. Robot later replays the trajectory automatically. Concept / Approach: This approach corresponds to continuous-path control (often taught via lead-through or teach pendant). The system interpolates between closely spaced recorded points to create smooth motion, unlike discrete point-to-point programming used in pick-and-place tasks. Step-by-Step Solution: Identify the teaching style: physical guidance with trajectory capture.Map to control category: continuous-path (CP) rather than point-to-point (PTP).Recognize typical CP applications: spray painting, arc welding, polishing.Select the corresponding option: Continuous-path control. Verification / Alternative check: Documentation for industrial robots distinguishes CP (trajectory defined by many points) from PTP (move between a few taught points). The described physical training aligns with CP. Why Other Options Are Wrong: Contact sensing and robot vision are sensing modalities, not trajectory teaching methods. Pick-and-place refers to PTP moves between discrete locations. Common Pitfalls: Confusing lead-through teaching (a way to capture paths) with PTP programming; CP emphasizes continuous trajectories, not just endpoints. Final Answer: Continuous-path control

Question 3

Interdisciplinary link: A bidirectional feedback loop that tightly connects computer modeling with the study of mind and behavior is most closely associated with which field?

Accepted Answer

Introduction / Context: Many disciplines use computational models to understand complex phenomena. When those models inform—and are informed by—research on human perception, reasoning, learning, and language, the interplay forms a hallmark of a particular interdisciplinary field. Given Data / Assumptions: Emphasis on a bidirectional loop: modeling informs theory; experimental results refine models. Focus is on mind, behavior, and intelligent processes. Computational approaches stand alongside psychology, neuroscience, linguistics, and philosophy. Concept / Approach: Cognitive science integrates computer science/AI with psychology and neuroscience to build and test models of cognition. Simulations and algorithms are compared against human data; discrepancies drive new experiments, creating the described feedback loop. Step-by-Step Solution: Identify the two linked elements: computer modeling and the science of mind.Recognize the iterative refinement process based on experiments.Map to the established interdisciplinary domain: cognitive science.Select ‘‘Cognitive science’’ as the correct field. Verification / Alternative check: Academic programs and journals in cognitive science explicitly promote computational modeling validated by behavioral/neuronal evidence. Why Other Options Are Wrong: ‘‘Heuristic processing’’ describes a technique, not a discipline. ‘‘Artificial science’’ and ‘‘human intelligence’’ are vague/ill-defined in this context. Common Pitfalls: Equating AI alone with cognitive science; cognitive science requires empirical grounding and cross-disciplinary integration. Final Answer: Cognitive science

Question 4

Common Lisp assignment semantics: Which function evaluates both the variable argument and the value expression when assigning, thereby allowing indirect (symbol-valued) assignment?

Accepted Answer

Introduction / Context: In Common Lisp, variable assignment can be done with multiple forms. Understanding when the variable name is evaluated versus treated as a literal symbol is crucial, especially for indirect assignments and metaprogramming tasks. Given Data / Assumptions: We compare set and setq semantics. We care about whether the first argument (the variable) is evaluated. Goal: identify the form that evaluates both its arguments. Concept / Approach: setq assigns to a named variable without evaluating the variable name; only the value expression is evaluated. By contrast, set evaluates its first argument to obtain a symbol and evaluates its second argument for the new value. Thus, set supports indirect assignment when the symbol to assign is itself computed. Step-by-Step Solution: Consider (setq x 10): ‘‘x’’ is not evaluated; value 10 is stored in variable x.Consider (set (read-from-string "x") 10): first argument evaluates to the symbol X; second evaluates to 10; X is then assigned 10.Therefore, the function that evaluates both variable and value is set.Select ‘‘set’’ as the correct choice. Verification / Alternative check: Test in a REPL: (let ((s 'x')) (set (symbol-value 's') 5)) demonstrates evaluated first arg; (setq s 5) would instead set variable s, not X. Why Other Options Are Wrong: setq does not evaluate the variable name. add and ‘‘eva’’ are not standard Common Lisp assignment primitives. Common Pitfalls: Passing a raw symbol to set without quoting or ensuring evaluation yields a symbol leads to errors; ensure the first argument evaluates to a symbol object. Final Answer: set

Question 5

Common Lisp literal symbols: Which form correctly assigns the symbol x (not the value of x) to the variable y?

Accepted Answer

Introduction / Context: In Lisp, symbols can be manipulated as data. To store the literal symbol x in a variable y, you must prevent evaluation of x. The quote operator (' or (quote …)) serves this purpose by yielding the symbol itself instead of its bound value. Given Data / Assumptions: We want y to hold the symbol x, not x's current value. setq assigns to a variable without evaluating the variable name. We must ensure the right-hand side is the symbol object. Concept / Approach: Use (setq y 'x'). Here, 'x evaluates to the symbol X. setq stores that symbol in y. Any forms that use ‘‘=’’ syntax are not Lisp and are therefore incorrect. Step-by-Step Solution: Prevent evaluation of x using quote: 'x.Assign it with setq: (setq y 'x').Confirm: evaluating y should return the symbol X.If needed, compare to (setq y x), which would copy x's current value instead. Verification / Alternative check: In a REPL, after (setq y 'x'), (symbolp y) returns T and (eq y 'x') returns T. Why Other Options Are Wrong: (setq y x) assigns the value of x, not the symbol. Forms using ‘‘=’’ are not valid Lisp syntax. Common Pitfalls: Forgetting to quote the symbol or confusing set and setq semantics can result in unintended variable dereferencing. Final Answer: (setq y 'x')

Question 6

Problem-solving terminology in AI: What is the term for the judgmental, experience-based, or commonsense component of problem solving that guides search and decision making?

Accepted Answer

Introduction / Context: Artificial intelligence and cognitive psychology distinguish between algorithmic procedures that guarantee optimal solutions and heuristic methods that trade optimality for speed and practicality, drawing on rules of thumb and domain intuition. Given Data / Assumptions: We focus on the ‘‘commonsense’’ or ‘‘judgmental’’ aspect of problem solving. Emphasis is on guidance, shortcuts, and plausible strategies rather than exhaustive search. Term is widely used in AI, operations research, and decision science. Concept / Approach: A heuristic is a strategy or rule of thumb that helps find satisfactory solutions efficiently. Heuristics guide search (e.g., A*'s admissible heuristics), prune options, and prioritize actions when computation or information is limited. Step-by-Step Solution: Identify the descriptive cues: commonsense, judgmental, experience-based.Associate these with guidance functions in search and decision tasks.Recall canonical examples: Manhattan distance in grid pathfinding; medical triage rules.Select ‘‘Heuristic’’ as the established term. Verification / Alternative check: Standard AI textbooks define heuristics exactly in this manner—as informed, non-guaranteed strategies that reduce search effort. Why Other Options Are Wrong: ‘‘Analytical’’ and ‘‘critical’’ describe reasoning styles, not specific guidance rules. ‘‘Value based’’ refers to preference modeling, not commonsense shortcuts. Common Pitfalls: Assuming heuristics always produce optimal answers; they aim for ‘‘good enough’’ quickly, balancing accuracy and cost. Final Answer: Heuristic

Question 7

In the history of artificial intelligence (AI), who is widely regarded as the 'father of AI' for coining the term, organizing the 1956 Dartmouth Conference, and advancing foundational research?

Accepted Answer

Introduction / Context: When students learn about the origins of artificial intelligence, one name consistently stands out because of his decisive role in naming the field, convening the seminal conference that launched it, and contributing core ideas for decades. Identifying the 'father of AI' tests knowledge of AI's early history, key personalities, and the institutional milestones that shaped the discipline we study today. Given Data / Assumptions: The question asks for the individual most commonly credited as the 'father of AI.' We consider contributions such as coining the term 'artificial intelligence,' organizing foundational meetings, and sustained research impact. Famous early researchers include John McCarthy, Allen Newell, and others often mentioned in introductory AI histories. Concept / Approach: Historical consensus in academic texts attributes the title 'father of AI' primarily to John McCarthy. He coined the term 'artificial intelligence' and, with colleagues, organized the 1956 Dartmouth Conference, generally cited as AI's birth event. McCarthy later contributed to Lisp, time-sharing, and formal reasoning—pillars that influenced AI programming and theory for decades. Step-by-Step Solution: List prominent early AI figures and their hallmark contributions.Match each candidate with the criteria: naming the field, convening a founding conference, and long-term research output.Observe that John McCarthy uniquely satisfies all three in widely used AI textbooks and historical surveys.Select the option that exactly names John McCarthy. Verification / Alternative check: Introductory AI courses and standard references repeatedly identify McCarthy as the person who coined 'AI' and led the Dartmouth proposal. This external corroboration aligns with the discipline's collective memory and published histories. Why Other Options Are Wrong: Fisher Ada: Not an AI pioneer; likely a confusion with Ada Lovelace from early computing history. Allen Newell: Major pioneer who, with Simon, developed logic theorist/GPs, but did not coin the term 'AI' nor organize Dartmouth. Alan Turning: Misspelling of Alan Turing; crucial to computing and machine intelligence concepts but not the specific 'father of AI' designation. None of the above: Incorrect because John McCarthy is the accepted answer. Common Pitfalls: Confusing foundational computing figures (Turing, Lovelace) with those who formally established AI as a field; overlooking Dartmouth 1956's centrality. Final Answer: John McCarthy

Question 8

In robotics and control systems, which type of actuator typically delivers high power output but is often considered 'messy' due to fluid leaks and maintenance concerns?

Accepted Answer

Introduction / Context: Selecting actuators is a core engineering decision in robotics, heavy machinery, and industrial automation. Power density, control responsiveness, cost, noise, and maintainability all matter. This question probes practical engineering knowledge about the trade-offs between electric, hydraulic, and pneumatic actuation, with an emphasis on real-world maintenance and cleanliness. Given Data / Assumptions: We compare common actuator families: electric motors, hydraulic cylinders/motors, and pneumatic cylinders. Criterion: 'Generates a good deal of power' and is 'messy' in typical industrial practice. Context includes factories, mobile equipment, and robotic arms where environmental cleanliness may be an issue. Concept / Approach: Hydraulic systems use pressurized incompressible fluids, enabling very high force and power density for a given actuator size. However, hydraulic fluid can leak at seals, fittings, and hoses, leading to oil spills, contamination, and frequent cleanup. Pneumatic systems use compressed air—cleaner but with lower achievable forces for similar package sizes—and electric motors are clean and increasingly powerful but may require complex gearing for very high torques. Step-by-Step Solution: Evaluate power output: hydraulics typically exceed pneumatics and many electric solutions in raw force/torque per size.Evaluate 'messiness': hydraulic oil leaks and maintenance produce the classic 'messy' reputation.Check alternatives: pneumatics are generally cleaner (air exhaust), although moisture/oil carryover is manageable; electrics are cleanest.Conclude that hydraulics best match both criteria (high power, messy). Verification / Alternative check: Industry practice confirms that heavy excavators, presses, and high-force robotics rely on hydraulics; maintenance logs commonly include seal replacements and cleanup procedures due to oil leakage risks. Why Other Options Are Wrong: Electric: Clean operation, growing power capabilities, but not characteristically 'messy.' Pneumatic: Usually cleaner than hydraulics; power density is lower for equivalent sizes. Hydraulic and Pneumatic: Pneumatics are not generally 'messy' in the same sense. None: Incorrect because hydraulics fit. Common Pitfalls: Assuming 'messy' refers to noise (pneumatics can be noisy, but not oily) or ignoring the power-density contrast. Final Answer: Hydraulic

Question 9

Japan's Fifth Generation Computer Systems initiative: what was the name of the organization created to implement the project's goals?

Accepted Answer

Introduction / Context: The Fifth Generation Computer Systems (FGCS) project was a landmark national research program in Japan during the 1980s, aiming to push logic programming, knowledge processing, and massively parallel architectures. Knowing the organization behind FGCS helps students place AI history within broader policy and institutional frameworks that influence research trajectories and technology transfer. Given Data / Assumptions: The question asks for the implementing body of FGCS. MITI is often associated as the sponsoring ministry, but not the executing research institute. Other acronyms listed relate to US initiatives and consortia (MCC, SCP). Concept / Approach: ICOT—Institute for New Generation Computer Technology—was formed specifically to carry out FGCS research and development. While MITI helped fund and coordinate national industrial policy, ICOT executed the technical program, collaborating with universities and companies to prototype systems aligned with FGCS goals such as logic programming languages (e.g., Prolog variants) and parallel inference machines. Step-by-Step Solution: Identify which option represents a research institute rather than a ministry or a foreign program.Match that institute to the FGCS implementation role: ICOT.Discard distractors: MCC is a US consortium; SCP is a US DARPA initiative; MITI is a ministry, not the implementing lab.Select ICOT as the correct answer. Verification / Alternative check: Historical reports and retrospectives of FGCS consistently state that ICOT ran the program under MITI's sponsorship, distinguishing policy oversight from research execution. Why Other Options Are Wrong: MITI: Sponsor/overseer, not the implementing research institute. MCC: US-based consortium unrelated to Japan's FGCS. SCP: US Strategic Computing Program (DARPA), not Japanese. None: Incorrect because ICOT is the correct implementing body. Common Pitfalls: Conflating sponsor ministries with executing institutes; mixing up US and Japanese initiatives due to similar timeframes. Final Answer: ICOT (Institute for New Generation Computer Technology)

Question 10

In computational linguistics, which structure consists of named nodes and labeled arcs and is commonly used to represent natural language grammars for parsing (e.g., Augmented Transition Networks)?

Accepted Answer

Introduction / Context: Natural language understanding hinges on representing grammar so that a parser can recognize valid sentences. One influential approach models grammar as a set of states connected by labeled transitions that correspond to syntactic conditions. Knowing this representation and its variants (such as Augmented Transition Networks, ATNs) is foundational for students of NLP and AI. Given Data / Assumptions: We need a network model with named nodes (states) and labeled arcs (transitions). The model should support parsing and grammar representation, not just data visualization. Examples include finite-state machines with augmentations for recursion and features. Concept / Approach: A Transition Network is a directed graph in which nodes represent parsing states and arcs are labeled with tests or grammar symbols. An Augmented Transition Network extends this with registers and procedures to handle nested structures and long-distance dependencies, making it more powerful than simple finite-state machines while remaining graph-based and operationally intuitive for parsing. Step-by-Step Solution: Map the description (named nodes, labeled arcs) to known grammar formalisms.Eliminate generic topologies (star/complete) that do not encode grammatical transitions.Identify Transition Network (and ATN) as directly matching grammar-based parsing mechanisms.Select 'Transition Network' as the correct structure. Verification / Alternative check: Classic NLP literature describes ATNs exactly in these terms, with examples of arcs labeled by categories like NP, VP, or feature tests. Why Other Options Are Wrong: Tree Network: Trees are parse outputs, not the control network used to drive the parser. Star/Complete Network: Graph topologies with no inherent linguistic semantics. None: Incorrect because Transition Network fits precisely. Common Pitfalls: Confusing parse trees (results) with the network representation of the parsing procedure itself. Final Answer: Transition Network

Question 11

AI and computer chess history: in 1985, International Master David Levy defeated a reigning world-champion chess program in four straight games by adopting orthodox play that confused the engine. What was the name of that program?

Accepted Answer

Introduction / Context: Computer chess has long served as a benchmark for artificial intelligence. In the mid-1980s, programs achieved master-level performance but still showed weaknesses that strong human players could exploit. Remembering landmark matches helps situate the evolution from early rule-based and alpha–beta programs to modern engines powered by advanced evaluation and, later, machine learning. Given Data / Assumptions: The match occurred in 1985 and featured David Levy, a noted computer-chess critic and International Master. The program was a world-champion engine of its time. The human strategy relied on orthodox, non-speculative play to induce programmatic misjudgments. Concept / Approach: During this period, CRAY BLITZ was among the strongest programs, winning the World Computer Chess Championship (WCCC) and running on Cray supercomputers. Levy's strategy highlighted evaluation and horizon effect limitations common to engines of the era. While Kaissa was an earlier Soviet champion (1974), and other names listed are not world-champion chess programs, CRAY BLITZ aligns with the timeline and status indicated in the stem. Step-by-Step Solution: List candidate programs that held WCCC titles around 1985.Compare dates: CRAY BLITZ was a champion in the early–mid 1980s.Match the described result (Levy's 4–0 win via orthodox play) with historical reports.Choose CRAY BLITZ as the best-fitting answer among the options. Verification / Alternative check: Contemporary articles and retrospectives discuss Levy's victories over top programs of that epoch, including CRAY BLITZ, prior to the dominance of later systems like Deep Thought and Deep Blue. Why Other Options Are Wrong: Kaissa: Earlier champion, not matched to the 1985 event described. Golf/DIGDUG: Not computer-chess world champions. None: Incorrect because CRAY BLITZ fits the facts. Common Pitfalls: Confusing different championship years or mixing later engines with earlier-era matches. Final Answer: CRAY BLITZ

Question 12

In knowledge-based systems, what is the term for a computer program that emulates the problem-solving and decision-making processes of human experts within a narrow domain?

Accepted Answer

Introduction / Context: Before modern data-driven AI, expert systems dominated practical AI deployment in medicine, finance, engineering, and troubleshooting. These systems attempted to encode expert knowledge in a formal representation and apply it through an inference engine, providing explanations and recommendations similar to those of human specialists. Understanding this term anchors the history and methodology of symbolic AI. Given Data / Assumptions: We seek the recognized name for a program that simulates expert-level reasoning in a limited domain. The system leverages knowledge bases, rules, and inference rather than general human-like cognition. The answer choices include distractors that are not standard technical terms. Concept / Approach: An expert system couples a knowledge base (facts and production rules) with an inference engine (forward or backward chaining) and may include an explanation facility. Its strength lies in transparency and domain specificity, trading broad generalization for structured expert guidance and justifications for conclusions. Step-by-Step Solution: Identify the canonical term used across AI textbooks for domain-specific human-like reasoning.Confirm that 'expert system' matches the features described—knowledge base, rules, inference engine.Eliminate nonstandard terms such as 'human logic' or 'expert reason.'Select 'expert system' as the correct option. Verification / Alternative check: Classic systems like MYCIN, XCON, and DENDRAL are consistently labeled expert systems, reinforcing the definition and usage. Why Other Options Are Wrong: Human logic / Expert reason / Personal information: Not formal AI categories. None: Incorrect because a precise term exists. Common Pitfalls: Assuming all AI that mimics people is an 'expert system'; modern ML systems differ in architecture and learning methodology. Final Answer: Expert system

Question 13

In the LISP programming language used for AI, which built-in function returns the first element of a list structure (i.e., the head of the list)?

Accepted Answer

Introduction / Context: LISP is a classic AI programming language built around symbolic expressions (s-expressions) and list processing. Two primitive selectors, historically named car and cdr, retrieve parts of a list. Recognizing what each returns is essential for reading and writing idiomatic LISP code and for understanding many AI examples from classic literature. Given Data / Assumptions: The question focuses on the function that returns the first element of a list. LISP lists are linked cells (cons pairs) where the head (car) holds the first element, and the tail (cdr) holds the rest. Some dialects provide synonyms (e.g., first) but the canonical primitive is still car. Concept / Approach: In most LISP dialects, car(list) yields the head element. cdr(list) produces the tail (a list of remaining elements). While some dialects support first as a more readable alias, the primitive historically and portably recognized is car—originating from the hardware architecture of early Lisp machines ('contents of the address register'). Step-by-Step Solution: Recall the pair of primitives: car → head, cdr → tail.Map the requested operation (first element) to car.Note that set is unrelated; second is a convenience function (or composition).Select 'car' as the correct answer. Verification / Alternative check: Running a simple example in any LISP REPL—e.g., (car '(a b c)) → a—confirms the behavior unambiguously. Why Other Options Are Wrong: set: Assignment or symbol binding in some dialects; not list selection. first: Often a synonym, but traditionally the primitive asked in such questions is car. second: Returns the second element or is defined via (car (cdr ...)). None: Incorrect because car is standard. Common Pitfalls: Confusing dialect conveniences with core primitives; mixing up car and cdr semantics. Final Answer: car

Question 14

Early mobile robotics at SRI International: under Nils Nilsson’s leadership, what was the name of the pioneering mobile robot platform that integrated perception, planning, and navigation?

Accepted Answer

Introduction / Context: Shakey, developed at SRI International (formerly Stanford Research Institute) in the late 1960s and early 1970s, is a landmark in mobile robotics. The project, guided by prominent researchers including Nils Nilsson, integrated computer vision, planning, and navigation, demonstrating that a robot could reason about its actions in a constrained environment. Recognizing Shakey situates modern robotics in its historical lineage. Given Data / Assumptions: We seek the SRI robot associated with Nilsson's team. The robot is historically known for combining AI planning (STRIPS), perception, and movement. Names listed include distractors unrelated to SRI's mobile robot. Concept / Approach: Shakey was the first mobile robot to reason about its own actions via symbolic planning, using camera and range sensors to build a simplified world model and execute plans. Its software introduced techniques—such as the STRIPS planner—that shaped AI planning research for decades and inspired subsequent autonomous systems. Step-by-Step Solution: Identify which name matches the SRI robot: Shakey.Recall that Dedalus and Vax do not refer to SRI's mobile platform; 'Robitics' is not a known robot name.Confirm via the association with STRIPS and Nilsson's publications.Select 'Shakey' as correct. Verification / Alternative check: Museum exhibits and academic retrospectives consistently feature Shakey as the pioneering SRI mobile robot, reinforcing the connection with Nilsson's leadership. Why Other Options Are Wrong: Robitics: Not a recognized robot; appears to be a misspelling or distractor. Dedalus/Vax: Not SRI's mobile robot; unrelated names in this context. None: Incorrect because Shakey is the canonical answer. Common Pitfalls: Confusing Shakey with later robots or conflating planning systems (STRIPS) with the robot's name. Final Answer: Shakey

Question 15

In natural language processing (NLP), the field is commonly divided into two major subareas. Which pair best captures this split and reflects core research and engineering tasks?

Accepted Answer

Introduction / Context: NLP involves both interpreting human language (what does this text or speech mean?) and producing human-like language (how can a system speak or write?). While many taxonomies exist, a standard and intuitive dichotomy divides NLP into understanding and generation. This framing appears in classic AI textbooks and continues to inform system architectures today. Given Data / Assumptions: The split should reflect high-level functional goals, not implementation styles or unrelated dimensions. Understanding includes parsing, semantic interpretation, and discourse modeling. Generation includes content selection, surface realization, and speech/text production. Concept / Approach: Understanding addresses mapping from text or speech to meaning representations, tasks like part-of-speech tagging, syntactic parsing, named entity recognition, and question answering. Generation addresses mapping from meaning or intent to linguistic output, including natural language generation (NLG), dialogue response generation, and summarization phrasing. This separation helps modularize systems and focus evaluation metrics on comprehension versus production quality. Step-by-Step Solution: Evaluate each option's relevance to core NLP objectives.Eliminate categories that classify methods (symbolic vs numeric) rather than goals.Select 'understanding and generation' as directly capturing the fundamental split.Confirm with examples: speech recognition + semantic parsing (understanding) vs text generation + TTS (generation). Verification / Alternative check: Course syllabi and research surveys commonly organize content around comprehension and production, corroborating this answer. Why Other Options Are Wrong: Symbolic and numeric: Methodological distinction, not a task split. Time and motion: Irrelevant to NLP. Algorithmic and heuristic: Both are approaches, not subfields. None: Incorrect because a well-accepted pair exists. Common Pitfalls: Confusing implementation paradigms (rules vs ML) with the functional decomposition of NLP systems. Final Answer: Understanding and generation

Question 16

In a rule-based expert system, which artifact encodes procedural domain knowledge as condition–action statements that the inference engine fires when conditions are satisfied?

Accepted Answer

Introduction / Context: Rule-based systems are a cornerstone of symbolic AI. They separate knowledge from control: domain knowledge is written as rules, while an inference engine manages which rules fire and in what order. Understanding what counts as 'procedural domain knowledge' clarifies how these systems capture expert behavior and make decisions transparently. Given Data / Assumptions: We focus on how domain expertise is represented for execution, not on the engine itself. Rules typically follow an IF–THEN schema with optional certainty factors. The inference engine performs forward or backward chaining over a working memory (facts). Concept / Approach: Production rules are condition–action statements that operationalize knowledge: IF conditions over facts are true, THEN perform actions such as asserting new facts, recommending a decision, or invoking procedures. These rules collectively encode procedural knowledge because they prescribe what to do when certain patterns are recognized in the data. The inference engine (sometimes called a rule interpreter) selects and fires applicable rules according to a conflict-resolution strategy. Step-by-Step Solution: Identify the representation that directly contains domain-specific logic in executable form.Distinguish it from components that execute or manage rules (engines/interpreters).Recognize 'production rules' as the standard term for IF–THEN knowledge units.Choose 'Production rules' as the correct answer. Verification / Alternative check: Classic expert systems (e.g., MYCIN) are documented as sets of production rules coupled with an inference engine; textbooks consistently define procedural domain knowledge in terms of these rules. Why Other Options Are Wrong: Rule interpreters: Part of the control mechanism, not the knowledge itself. Meta-rules: Rules about rules, often guiding control strategies, not domain procedures. Control rules: Oversee inference control; again not the domain facts/procedures. None: Incorrect because production rules precisely fit. Common Pitfalls: Mixing up the engine (how reasoning happens) with the knowledge base (what is known and how to act). Final Answer: Production rules

Question 17

In the history of artificial intelligence and computing, which concept was originally named the ‘‘imitation game’’ by its creator and later became a foundational test for machine intelligence?

Accepted Answer

Introduction / Context: The question asks about a pivotal concept in artificial intelligence that began under the name ‘‘imitation game.’’ This concept evaluates whether a machine's responses are indistinguishable from those of a human in natural language conversation, and it has influenced decades of AI research and public discourse. Given Data / Assumptions: The term ‘‘imitation game’’ was used by the concept's original creator. The concept later became widely known under a different, now-standard name. Options include major AI milestones such as LISP and The Logic Theorist, as well as the field of Cybernetics. Concept / Approach: Alan Turing introduced the ‘‘imitation game’’ in his 1950 paper, proposing an operational way to discuss machine intelligence without debating definitions. If a human judge conversing via text cannot reliably tell the machine from a human, the machine is said to pass the test. The modern name for this proposal is the Turing Test. Step-by-Step Solution: Identify which option corresponds to the ‘‘imitation game’’ as coined by Alan Turing.Map that historical label to the modern term used in AI literature.Confirm that this is the Turing Test, not a language, program, or field.Select ‘‘The Turing Test.’’ Verification / Alternative check: Introductory AI textbooks and historical essays consistently state that Turing's ‘‘imitation game’’ is the original label for what we now call the Turing Test, validating the choice. Why Other Options Are Wrong: LISP: A programming language used in AI, not a test of intelligence. The Logic Theorist: An early theorem-proving program by Newell and Simon; unrelated to the imitation game label. Cybernetics: A broad field on control and communication in animals and machines; not a single test. None of the above: Incorrect because the Turing Test matches precisely. Common Pitfalls: Confusing general AI milestones with Turing's specific proposal; assuming the imitation game refers to a particular program rather than a test protocol. Final Answer: The Turing Test

Question 18

In knowledge-based computing, what do we call a computer program that encapsulates domain-specific expertise to solve problems at a near-human specialist level?

Accepted Answer

Introduction / Context: Before contemporary data-driven machine learning dominated practice, symbolic AI delivered real-world value via systems that modeled human expert reasoning in narrow domains such as medical diagnosis, configuration, and financial advising. These systems remain an essential part of AI history and are still useful where transparent, rule-based reasoning is required. Given Data / Assumptions: The program stores and applies specialized domain knowledge. It aims to make decisions or recommendations similar to a human expert. It typically includes a knowledge base and an inference engine. Concept / Approach: The standard term is ‘‘expert system.’’ Such a system represents knowledge in production rules or other structured forms and uses forward or backward chaining to infer conclusions. An explanation facility may justify outputs by tracing rule firings, making the system's reasoning auditable and trustworthy in regulated settings. Step-by-Step Solution: Identify the key qualities: narrow domain focus and codified expertise.Match those qualities to the canonical AI artifact name.Confirm that ‘‘expert system’’ fits while the distractors are not standard AI terms.Choose ‘‘expert system.’’ Verification / Alternative check: Classic systems like MYCIN (medicine) and XCON (configuration) are universally labeled expert systems, confirming the terminology. Why Other Options Are Wrong: Intelligent planner: Focuses on plan generation, not general domain expertise. Automatic processor / operational symbolizer: Not standard AI nomenclature. None of the above: Incorrect because a well-accepted term exists. Common Pitfalls: Equating any AI with an expert system; modern ML systems differ in representation and learning methods. Final Answer: expert system

Question 19

In computer vision, which visual cues are typically the most informative for scene understanding and object perception in practical systems?

Accepted Answer

Introduction / Context: Computer vision systems infer structure and semantics from images or video by exploiting visual cues. Understanding which cues are reliably informative helps in designing feature extractors, sensors, and algorithms for tasks like tracking, segmentation, recognition, and 3D reconstruction. Given Data / Assumptions: Candidates include colour, motion, depth, and texture—well-known cues in vision. ‘‘Height and weight’’ are not direct visual cues derived from pixels. We are looking for the combination that captures multiple reliable signal sources. Concept / Approach: Colour aids segmentation and recognition under consistent illumination; motion provides temporal coherence and can separate foreground from background; depth describes scene geometry; texture conveys surface properties and aids in stereo and shading. Together, these cues complement each other across varied lighting, occlusion, and viewpoint changes. Hence, both (a) and (b) are correct collectively. Step-by-Step Solution: Evaluate each pair: colour+motion and depth+texture are classic, useful cues.Note that height+weight are not pixel-derived or universally measurable from images.Select the option that includes both sets of valid cues.Choose ‘‘a and b above.’’ Verification / Alternative check: Standard textbooks and benchmarks (e.g., optical flow for motion, stereo for depth, SIFT/LBP for texture, colour histograms for segmentation) validate the importance of these cues. Why Other Options Are Wrong: Only (a) or only (b): Too narrow; both sets are widely used. Height and weight: Not visual cues readily extracted from raw images. None: Incorrect because multiple valid cues are listed. Common Pitfalls: Assuming colour is always reliable; in practice, illumination changes can degrade it, which is why combining cues is beneficial. Final Answer: a and b above

Question 20

Educational programming at MIT AI Lab: which language environment did Seymour Papert design to help children learn through exploratory computing and turtle graphics?

Accepted Answer

Introduction / Context: Seymour Papert, a pioneer in constructionist learning, believed children learn best by making and exploring. He co-developed a programming environment centered on simple commands and visual feedback via ‘‘turtle graphics,’’ enabling learners to internalize mathematical and computational ideas by creating artifacts. Given Data / Assumptions: The environment originated at MIT's AI Lab. It was explicitly designed for children and education. It featured turtle graphics for immediate visual reinforcement. Concept / Approach: The language is LOGO. Its command set (forward, right, left, penup, pendown) and immediate-mode execution make abstract geometry and procedure concepts tangible. LOGO embodied Papert's constructionism by empowering learners to build and reflect rather than passively consume information. Step-by-Step Solution: Identify the educational focus and turtle graphics hallmark.Match to the language designed with these goals—LOGO.Rule out BASIC/FORTRAN (general-purpose, not specifically for child-centered pedagogy) and MYCIN (an expert system, not a language).Select ‘‘LOGO.’’ Verification / Alternative check: Histories of educational computing consistently cite LOGO as Papert's signature contribution, implemented on early microcomputers and school labs. Why Other Options Are Wrong: BASIC/FORTRAN: Popular languages but not designed around Papert's constructionist principles. MYCIN: A medical expert system, unrelated to programming education. None: Incorrect because LOGO precisely fits. Common Pitfalls: Confusing LOGO with general introductory languages; LOGO's pedagogy and graphics set it apart. Final Answer: LOGO

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