Question 1

In robotics and control, if a robot modifies its trajectory in real time based on sensory feedback and changing external conditions, it is considered:

Accepted Answer

Introduction / Context: Robot classifications often hinge on feedback and autonomy. A key distinction is whether the system can sense its environment and alter actions accordingly, versus executing a predetermined path with no adjustments. The ability to adapt trajectory speaks to intelligence and closed-loop behavior. Given Data / Assumptions: The robot alters its path in response to external conditions. Sensing and feedback are present during motion. We are choosing the best descriptor among common robotics terms. Concept / Approach: Altering trajectory based on sensory input implies an intelligent system employing perception, planning, or control algorithms that adjust commands. This contrasts with open-loop or non-servo systems, which lack feedback-based correction and cannot compensate for disturbances. While mobility refers to the capability to move, it does not imply adaptivity. Step-by-Step Solution: Identify the hallmark capability: adaptive trajectory adjustment via feedback.Map that capability to the term most aligned with autonomy and decision-making: ‘‘intelligent.’’Exclude ‘‘open loop’’ and ‘‘non-servo’’ because they explicitly lack feedback.Exclude ‘‘mobile’’ because mere motion does not imply adaptation. Verification / Alternative check: Standard robotics texts associate intelligent behavior with sensing, planning, and feedback control that change motion online, e.g., obstacle avoidance and trajectory replanning. Why Other Options Are Wrong: Mobile: Can move, but may still be preprogrammed and non-adaptive. Open loop / non-servo: No feedback; cannot adjust to external changes. None: Incorrect because ‘‘intelligent’’ captures the described behavior. Common Pitfalls: Equating servo control alone with intelligence; while servoing is a mechanism, the question emphasizes adaptive, condition-driven changes indicating intelligent control. Final Answer: intelligent

Question 2

In rule-based expert systems, what can the explanation facility be used for in practice when supporting users and developers?

Accepted Answer

Introduction / Context: One distinguishing feature of symbolic expert systems is their transparency. The explanation facility traces which rules fired and why, allowing users to understand recommendations and enabling knowledge engineers to refine rule sets. This capability is central to trust, adoption, and maintenance of expert systems in domains like healthcare and finance. Given Data / Assumptions: The facility can show how conclusions were reached. It can assist developers in finding errors or gaps in rules. ‘‘Construct a diagnostic model’’ is broader than explanation and typically precedes rule authoring. Concept / Approach: Explanation facilities generally support two key activities: they help end users by articulating the reasoning chain behind a conclusion (why and how), and they aid developers in debugging by exposing the rule firings and data dependencies. Building a diagnostic model, however, is a knowledge engineering task outside the explanation feature's remit; it is performed during system design, not as a function of explanation output. Step-by-Step Solution: List the common uses of explanation: user justification and developer debugging.Compare with options: (b) debugging and (c) explaining reasoning both match standard uses.Recognize that constructing a diagnostic model is not typically provided by the explanation mechanism.Select ‘‘both (b) and (c).’’ Verification / Alternative check: Documentation of classic systems (e.g., MYCIN) describes ‘‘why’’ and ‘‘how’’ explanations and their use during rule refinement, confirming (b) and (c) as core functions. Why Other Options Are Wrong: Construct a diagnostic model: That is a design-time activity, not what the explanation facility does. All of the above: Overbroad because it includes (a), which is not a typical explanation feature. None: Incorrect because (b) and (c) are valid uses. Common Pitfalls: Assuming explanation automatically creates new diagnostic structures; it clarifies reasoning but does not author knowledge. Final Answer: both (b) and (c)

Question 3

In a standard problem-solving methodology for AI systems, during which stage is the problem first connected to a proposed solution approach or model?

Accepted Answer

Introduction / Context: AI projects move through recognizable stages: discovering and framing the problem, choosing an approach, representing knowledge mathematically or symbolically, building the system, and validating it. Knowing which phase links the problem to a candidate solution informs good project planning and communication with stakeholders. Given Data / Assumptions: ‘‘Identification’’ surfaces and defines the problem. ‘‘Conceptualization’’ selects a plausible solution paradigm. ‘‘Formalization’’ encodes the chosen concept in precise representations. ‘‘Implementation’’ and ‘‘testing’’ follow after design decisions are made. Concept / Approach: The first point where the problem is tied to a particular way of solving it is conceptualization. Here, teams decide whether to use rules, constraints, probabilistic models, or search/planning; they outline data needs and systems boundaries. Only after that is the approach formalized into specific structures (e.g., production rules, Bayesian networks) and algorithms. Step-by-Step Solution: Recognize that identification states ‘‘what’’ the problem is.Determine where ‘‘how’’ we might solve it is first articulated: conceptualization.Note that formalization comes later to make concepts precise for coding.Choose ‘‘conceptualization’’ as the earliest linkage stage. Verification / Alternative check: Project lifecycles in AI texts and software engineering show this ordering consistently: identify → conceptualize → formalize → implement → test. Why Other Options Are Wrong: Identification: Problem discovery, not solution modeling. Formalization: Precise representation after choosing an approach. Implementation / testing: Construction and validation occur after design. Common Pitfalls: Jumping to implementation before conceptual clarity; skipping formalization leads to brittle systems. Final Answer: conceptualization

Question 4

LISP machines and symbolic processors: which company historically offered a LISP machine widely regarded as among the most powerful symbolic processors of its time?

Accepted Answer

Introduction / Context: During the 1980s, specialized hardware platforms called LISP machines were built to accelerate symbolic AI workloads. Evaluating the companies behind these systems provides historical context for AI's evolution and the hardware–software co-design ideas that reappear today in accelerators. Given Data / Assumptions: Multiple vendors shipped LISP machines: Symbolics, LMI, Texas Instruments, and others. ‘‘Most powerful’’ refers to broad contemporary consensus on performance and capability for symbolic processing. We need to identify the company most associated with top-end LISP machine performance. Concept / Approach: Symbolics produced the 3600 series and successors, often cited for leading performance and sophisticated development environments. While LMI, TI, and Xerox produced notable systems, Symbolics machines were frequently benchmark leaders and widely used in research labs for intensive symbolic AI tasks. Step-by-Step Solution: List major LISP machine vendors.Identify which brand was most associated with top performance in the era.Select ‘‘Symbolics’’ as the best historical match.Confirm against historical accounts of the 3600/36xx series reputations. Verification / Alternative check: Academic and industry retrospectives commonly highlight Symbolics for cutting-edge LISP hardware and environments, supporting this selection. Why Other Options Are Wrong: LMI/TI/Xerox: Important contributors but less frequently cited as the ‘‘most powerful’’ of the time. None: Incorrect because Symbolics fits the description. Common Pitfalls: Assuming modern general-purpose CPUs make historic distinctions irrelevant; the question asks about contemporary views of that era. Final Answer: Symbolics

Question 5

In U.S. AI research history, the Strategic Computing Program (SCP) was a major government initiative. Which organization sponsored and managed this program?

Accepted Answer

Introduction / Context: The Strategic Computing Program (SCP) was a significant U.S. government effort in the 1980s aimed at advancing computing and AI technologies for strategic applications. Identifying its sponsoring body is essential for understanding how public policy and defense priorities catalyzed AI research and infrastructure. Given Data / Assumptions: SCP was a federal research initiative. Candidate organizations include DARPA, NSF, and JPL. We must choose the primary sponsor/manager of the program. Concept / Approach: DARPA—the Defense Advanced Research Projects Agency—within the U.S. Department of Defense, initiated and oversaw SCP. DARPA has a long track record of funding foundational computing research, including ARPANET, VLSI, and AI-related projects, aligning with the strategic aims of SCP. Step-by-Step Solution: Identify which listed body typically manages defense-oriented research portfolios.Align historical programs like SCP with DARPA's mission and prior initiatives.Select ‘‘Defense Advanced Research Projects Agency.’’Rule out NSF and JPL as primary managers for SCP. Verification / Alternative check: Official histories and academic reviews attribute SCP to DARPA management, confirming the answer. Why Other Options Are Wrong: National Science Foundation: Civilian basic research focus; not the SCP sponsor. Jet Propulsion Laboratory: NASA center; not the SCP managing agency. All of the above / None: Both inconsistent with the historical record. Common Pitfalls: Conflating multiple U.S. research agencies; SCP specifically traces to DARPA's strategic computing agenda. Final Answer: Defense Advanced Research Projects Agency

Question 6

Foundations of digital logic: which mathematical system did Claude Shannon apply to describe and analyze the behavior of electronic switching circuits, forming the basis of modern digital design?

Accepted Answer

Introduction / Context: Claude Shannon's master's thesis revolutionized electrical engineering by linking logic and circuit design. He showed that binary relay and switching circuits could implement logical propositions, enabling systematic analysis and synthesis of digital systems—the conceptual foundation of modern computing hardware. Given Data / Assumptions: We are seeking the mathematical logic system Shannon used. The context is electronic switching circuits (relays, gates). Options include programming languages and AI fields that are unrelated to circuit logic. Concept / Approach: Boolean algebra, with variables taking values 0 and 1 and operations AND, OR, NOT, provides a perfect abstraction for circuit states and gate behavior. Shannon mapped logical expressions to relay circuits, enabling algebraic minimization and reliable design practices still taught today (K-maps, algebraic simplification, logic synthesis). Step-by-Step Solution: Recognize the need for a binary logical framework matching on/off circuit states.Identify Boolean algebra as the canonical system with binary variables and logical operators.Exclude programming languages (LISP/XLISP) and fields like neural networks as not matching Shannon's thesis.Select ‘‘Boolean algebra.’’ Verification / Alternative check: Electronics and computer engineering curricula universally credit Boolean algebra as the theoretical basis for digital logic design per Shannon's pioneering work. Why Other Options Are Wrong: LISP/XLISP: Programming languages; unrelated to relay logic foundations. Neural networking: A modeling paradigm; not the algebra Shannon applied. None: Incorrect because the correct system is well known. Common Pitfalls: Assuming a programming language was involved; Shannon's contribution was mathematical abstraction directly tied to circuits. Final Answer: Boolean algebra

Question 7

Applications of Intelligent Computer-Assisted Instruction (ICAI): In which environments can ICAI programs be effectively employed to deliver training and decision-support learning?

Accepted Answer

Introduction / Context: Intelligent Computer-Assisted Instruction (ICAI) integrates artificial intelligence with educational software to adapt content, provide individualized feedback, and assess learner performance. The question tests your understanding of where ICAI can be deployed and why it is broadly applicable across sectors. Given Data / Assumptions: ICAI systems personalize learning pathways and automate assessment. They support simulation-based practice, tutoring, and remediation. Different industries require scalable, data-driven training solutions. Concept / Approach: Because ICAI focuses on adaptive instruction and real-time feedback, any environment that needs consistent training, skills validation, or scenario-based learning can benefit. Evaluate each setting—academia, corporate learning, and defense—against these capabilities. Step-by-Step Solution: Identify ICAI core features: adaptivity, diagnostics, feedback, analytics.Map features to education: personalized remediation, mastery learning, formative assessment.Map features to corporations: onboarding, compliance training, product knowledge, role-play simulations.Map features to defense: mission rehearsal, standard operating procedures, after-action diagnostics.Conclude that all listed environments benefit; select ‘‘All of the above’’. Verification / Alternative check: Case studies show improved retention and reduced training time when adaptive tutors are deployed in universities, enterprises, and defense simulations alike. Why Other Options Are Wrong: Educational institutions alone: too narrow; corporate and defense needs are substantial. Corporations alone: ignores academia and mission-critical training. Departments of Defence alone: excludes broader civilian applications. None of the above: contradicted by extensive real-world deployments. Common Pitfalls: Confusing ICAI with static e-learning; ICAI requires adaptivity and diagnostic feedback, not just slideware. Final Answer: All of the above

Question 8

Neuro-inspired hardware: What term is used for computers whose circuits are patterned after the interconnected neurons of the human brain?

Accepted Answer

Introduction / Context: Researchers model computation after biological neural systems to achieve robust pattern recognition, fault tolerance, and learning from data. The question targets recognition of the standard term for brain-inspired computing architectures. Given Data / Assumptions: Circuitry mimics neuron–synapse interconnections. Systems can learn from examples and generalize. Applications include vision, speech, and control. Concept / Approach: Computers designed around artificial neurons and weighted connections are known as neural network computers. The phrase precisely captures both the structure (networks of units) and the learning capability (weight adaptation). Step-by-Step Solution: Identify keyword ‘‘interconnections like human brain’s nerve cells’’.Recall that artificial neural networks emulate this topology.Select the standard nomenclature: ‘‘Neural network computers’’. Verification / Alternative check: Neural hardware accelerators (e.g., neuromorphic chips) are explicitly marketed as neural network systems; the terminology is consistent across literature. Why Other Options Are Wrong: Intelligent/Smart computers: vague marketing terms, not specific to neuronal design. Supercomputers: describe scale/performance, not architecture. None of the above: contradicted by established terminology. Common Pitfalls: Equating any high-performance system with neural networks; the defining feature is neuron-like computation and adaptivity, not raw speed. Final Answer: Neural network computers

Question 9

Decision Support Systems (DSS): These programs are primarily designed to help managers make which type of choices?

Accepted Answer

Introduction / Context: Decision Support Systems (DSS) integrate data, models, and user interfaces to assist managers with complex choices. This question checks whether you recognize the core mission of DSS: improving the quality and speed of managerial decision-making. Given Data / Assumptions: DSS combines databases, analytics, and what-if modeling. It targets structured and semi-structured decisions under uncertainty. Users are typically managers and analysts. Concept / Approach: While DSS can produce budgets or visuals as outputs, those are means to the end. The principal intent is to support business decisions—pricing, capacity, risk, supply chain, and portfolio choices—through data-driven insights and scenario analysis. Step-by-Step Solution: Identify the main beneficiaries: managers and analysts.Determine the objective: better business decisions, not clerical tasks.Select the option explicitly mentioning business decisions. Verification / Alternative check: Classic DSS components (data warehouse, model base, UI) serve to evaluate alternatives, not just to format reports. Why Other Options Are Wrong: Budget projections and visual presentations are outputs/tools, not the core purpose. Vacation schedules are routine HR tasks, not DSS’s principal domain. None of the above: incorrect because DSS explicitly supports decision making. Common Pitfalls: Confusing DSS with MIS (routine reporting). DSS emphasizes analysis and modeling of choices under uncertainty. Final Answer: Business decisions (structured and semi-structured)

Question 10

Knowledge encapsulation: What is the name of a computer program that contains the distilled expertise of a human specialist for problem solving?

Accepted Answer

Introduction / Context: Expert systems encode the rules, heuristics, and domain facts used by specialists to make decisions. The question probes whether you can identify the proper term for software that delivers expert-level advice in a narrow domain. Given Data / Assumptions: Program captures domain knowledge (rules and facts). Provides recommendations, diagnoses, or classifications. Often includes an inference engine and knowledge base. Concept / Approach: An expert system is a classic AI application that emulates decision-making of human experts. It differs from general AI, from databases (which store but do not infer), and from MIS (which summarizes operations). Step-by-Step Solution: Recognize key phrase ‘‘distilled knowledge of an expert.’’Map to architecture: knowledge base + inference engine.Select ‘‘Expert system’’ as the precise label. Verification / Alternative check: Examples like MYCIN (medical) and XCON (configuration) confirm the definition and scope. Why Other Options Are Wrong: DBMS stores data; it doesn’t reason. MIS produces periodic reports; not a reasoning engine. Artificial intelligence is an umbrella term; too broad for the described artifact. Common Pitfalls: Assuming any smart program is an expert system; the distinguishing feature is explicit domain knowledge and rule-based inference. Final Answer: Expert system

Question 11

High-resolution bit-mapped displays: These displays are especially useful for which output purposes?

Accepted Answer

Introduction / Context: Display quality is driven by pixel density and bit-mapped rendering. This question checks whether you understand how higher resolution affects both text clarity and graphical fidelity, and how it can increase on-screen information density. Given Data / Assumptions: Bit-mapped displays render text and graphics as pixels. Higher resolution means more pixels per unit area. Font smoothing/anti-aliasing can leverage high DPI for clarity. Concept / Approach: More pixels allow finer detail in images and smoother letterforms. With suitable font size and UI scaling, screens can also display more characters or columns of data without loss of readability. Step-by-Step Solution: Relate resolution to graphical detail: more pixels = sharper graphics.Relate resolution to text clarity: anti-aliased glyphs render more precisely.Relate resolution to information density: higher DPI can show more characters at the same physical size.Therefore, all listed benefits apply; select ‘‘All of the above’’. Verification / Alternative check: Compare the same content on low-DPI and high-DPI screens; detail, legibility, and layout capacity improve with resolution. Why Other Options Are Wrong: Choosing only one benefit ignores the others; the advantages are cumulative. None of the above contradicts well-established display principles. Common Pitfalls: Forgetting that UI scaling can trade off ‘‘more characters’’ for comfort; capability still exists to display more content at higher DPI if desired. Final Answer: All of the above

Question 12

Natural Language Generation (NLG): Which choices describe core planning decisions an NLG system must make before producing output?

Accepted Answer

Introduction / Context: Natural Language Generation systems plan and realize text from structured data. This question evaluates understanding of content determination and discourse planning, two fundamental NLG planning stages. Given Data / Assumptions: NLG requires selecting relevant information (what to say). NLG must organize and time information (when and how to say it). High-level business purpose is typically specified externally by the application. Concept / Approach: Core NLG pipeline: content selection, document planning (ordering/timing), microplanning (lexicalization/aggregation), and surface realization. The system must decide what content to include and how to structure its presentation over a discourse. Step-by-Step Solution: Identify core planning: choose facts → order them.Map to options: (a) content selection; (b) timing/organization.Note that ‘‘why it is being used’’ is a design requirement, not the NLG planner’s internal decision.Select ‘‘Both (a) and (b)’’. Verification / Alternative check: Standard NLG architectures consistently list content selection and discourse planning as essential pre-realization steps. Why Other Options Are Wrong: (a) or (b) alone is incomplete; both are necessary. (c) is external to the generator; it does not describe a generation decision. None of the above contradicts NLG fundamentals. Common Pitfalls: Confusing strategic goals of the application with the tactical planning decisions the NLG system performs. Final Answer: Both (a) and (b)

Question 13

AI philosophy: What is the standard definition of Weak AI in the context of artificial intelligence research?

Accepted Answer

Introduction / Context: Weak AI and Strong AI are philosophical positions about the goals and interpretations of AI systems. The question checks whether you can distinguish weak AI (simulation of intelligence) from strong AI (actual mind equivalence). Given Data / Assumptions: Weak AI: machines act as if intelligent for tasks. Strong AI: machines possess minds with consciousness and understanding. Cognitive modeling is a separate methodological stance. Concept / Approach: Weak AI asserts that achieving behaviorally intelligent performance suffices for practical AI; it does not claim the system truly ‘‘understands.’’ Hence, the correct description focuses on outputs judged intelligent by human standards. Step-by-Step Solution: Match weak AI to behavioral equivalence: intelligent-seeming output.Contrast with strong AI: full human-level cognition as a real property.Reject umbrella statements that conflate both.Choose the concise weak AI definition. Verification / Alternative check: Textbook treatments define weak AI as constructing systems that act intelligently without metaphysical claims about consciousness. Why Other Options Are Wrong: Strong AI embodiment is not weak AI. Cognitive science modeling aims to explain mind; it is not the weak AI definition. ‘‘All of the above’’ mixes incompatible stances. Common Pitfalls: Assuming weak AI denies usefulness; it simply remains agnostic about genuine understanding. Final Answer: A set of programs that produce outputs considered intelligent if a human produced them

Question 14

Process improvement terminology: A process that is repeated, evaluated for results, and refined in cycles is best described as what?

Accepted Answer

Introduction / Context: Many engineering, analytics, and AI workflows progress in cycles: plan, execute, measure, and improve. The question assesses your familiarity with the standard term for such cyclical refinement. Given Data / Assumptions: The process repeats steps multiple times. Each cycle incorporates feedback from prior outcomes. Aim is continuous improvement or convergence. Concept / Approach: An iterative process loops over the same sequence, adjusting parameters or methods based on evaluation metrics, until goals are met or progress plateaus. Step-by-Step Solution: Identify key words: repeated, evaluated, refined.Associate with iterative methodologies (e.g., agile, CRISP-DM, EM algorithm).Select ‘‘Iterative’’ as the matching term. Verification / Alternative check: Across software and data science, ‘‘iterative’’ specifically denotes cycles with feedback loops, differentiating it from one-shot or purely descriptive approaches. Why Other Options Are Wrong: Diagnostic focuses on fault identification. Descriptive summarizes; it does not refine by repetition. Interpretive explains; it does not imply cyclical improvement. Common Pitfalls: Confusing iterative with incremental; iterative reworks the same components based on learning, whereas incremental adds new components or scope. Final Answer: Iterative

Question 15

LISP machine platforms: Which hardware features were typically associated with classic LISP machines used for AI research?

Accepted Answer

Introduction / Context: LISP machines (from vendors such as Symbolics and Lisp Machines, Inc.) were engineered to accelerate symbolic processing for AI. This question examines your knowledge of their characteristic hardware traits. Given Data / Assumptions: Goal: accelerate garbage collection, dynamic typing, and list processing. UI catered to development: rich keyboards and pointing devices. Period: 1980s specialized workstations. Concept / Approach: These systems paired generous memory and specialized CPUs or microcode with development-friendly peripherals, including mice and keyboards with dedicated Lisp editing keys and symbols, to support interactive environments. Step-by-Step Solution: Match performance needs to hardware: large RAM and Lisp-optimized processor.Recognize developer ergonomics: mouse + specialized keyboard layouts.Combine into ‘‘Both (a) and (c)’’ as the accurate description. Verification / Alternative check: Historical documentation highlights hardware support for tagged architectures, stack-based execution, and integrated GUIs with tailored input devices. Why Other Options Are Wrong: Letter-quality printers and 8-inch drives were era-typical peripherals, not defining features of Lisp machines. ‘‘None of the above’’ ignores well-documented specifications. Common Pitfalls: Attributing generic office peripherals as core identifiers; what set these machines apart was architecture and developer I/O design. Final Answer: Both (a) and (c)

Question 16

Starting a Lisp environment: Which command names are commonly used to invoke the Lisp system on typical installations?

Accepted Answer

Introduction / Context: Different Common Lisp implementations provide launch commands that reflect either the language (LISP/CL) or a vendor-specific binary name. The question checks recognition of generic invocation names across platforms. Given Data / Assumptions: We focus on generic, language-oriented command names. Implementations vary: SBCL, CLISP, Allegro, CMUCL, etc. Historically, commands like ‘‘lisp’’ or ‘‘cl’’ have been common wrappers. Concept / Approach: While many vendors use their own executable names (e.g., sbcl, clisp), generic entry points often include ‘‘lisp’’ or ‘‘cl’’. Therefore, acknowledging both ‘‘LISP’’ and ‘‘CL’’ covers standard, language-centric invocations. Step-by-Step Solution: Identify conventional names that map to Common Lisp runtime loaders.Exclude vague or nonstandard entries that do not commonly launch Lisp.Select ‘‘Both b and c’’ to capture typical invocations. Verification / Alternative check: On Unix-like systems, symlinks ‘‘lisp’’ or ‘‘cl’’ are frequently provided; Windows shortcuts likewise reflect these names for certain distributions. Why Other Options Are Wrong: ‘‘AI’’ is not a standard shell command for Lisp. ‘‘None of the above’’ contradicts widely used command names. Common Pitfalls: Assuming a single universal executable; in practice, vendor names vary, but ‘‘lisp’’ and ‘‘cl’’ remain common generic launchers. Final Answer: Both b and c

Question 17

In artificial intelligence (AI) planning, which approach builds a plan using successive representations at multiple levels of detail, refining high-level actions into lower-level steps as needed?

Accepted Answer

Introduction / Context: Planning in artificial intelligence (AI) is about deciding a sequence of actions that transform the world from an initial state to a desired goal state. Some approaches reason at a single level of detail, while others organize decisions into layers. The question asks for the planning style that explicitly uses several levels of description so that a high-level plan is progressively refined into concrete, executable steps. Given Data / Assumptions: The plan is represented at multiple abstraction levels. Higher-level actions can be decomposed into lower-level subactions. The process is iterative: refine top goals into more detailed operations. Concept / Approach: Hierarchical planning (often called Hierarchical Task Network, or HTN, planning) models tasks at different granularities. A task such as ‘‘travel to city’’ can be decomposed into ‘‘book ticket,’’ ‘‘go to airport,’’ and ‘‘board flight,’’ which in turn decompose further. This layered organization lets planners reason efficiently, reuse methods, and delay low-level commitments until necessary. Step-by-Step Solution: Identify the keyword: successive representations at different levels implies abstraction layers.Match to planning styles: hierarchical planning uses decomposition; non-hierarchical does not.Exclude distractors: project planning is a management term, not an AI technique.Conclude that hierarchical planning fits the described behavior. Verification / Alternative check: Classic AI planning literature describes HTN planners as operating via methods that refine compound tasks into primitive actions. This is precisely the idea of successive representations across levels of detail. Why Other Options Are Wrong: Project planning: Concerned with schedules and resources, not multi-level symbolic decomposition. Non-hierarchical planning: Operates at a single granularity; no layered refinement. All of the above / None of the above: Contradict the clear definition of hierarchical planning. Common Pitfalls: Confusing managerial project plans with AI plan representations; assuming any planner implicitly uses multiple levels (many do not). Final Answer: hierarchical planning

Question 18

In the manufacturing life cycle, which stage is best described as the mapping of function onto form—translating required functions into concrete shapes, materials, and configurations?

Accepted Answer

Introduction / Context: Manufacturing follows a product life cycle that includes conceiving an idea, designing it, producing it, distributing it, and supporting it in the field. A classic definition of design emphasizes transforming functional requirements (what the product must do) into a physical embodiment (how it is shaped and built). The question asks which stage performs this function-to-form translation. Given Data / Assumptions: We have distinct stages: design, production, distribution, and service. ‘‘Mapping function onto form’’ means choosing geometry, materials, and structure to realize desired behaviors. The focus is upstream of manufacturing and downstream of raw concept ideation. Concept / Approach: Design turns requirements into specifications and artifacts: drawings, models, and bills of materials. It balances performance, safety, cost, and manufacturability. Techniques include requirements analysis, functional decomposition, embodiment design, and detailed CAD work that fully defines the form that will deliver the intended function. Step-by-Step Solution: Identify where functional needs are converted to tangible product definition.Recognize that distribution and field service occur after production and do not create form.Note that project management coordinates work; it does not itself perform the mapping.Select ‘‘Design’’ as the stage that maps function to form. Verification / Alternative check: Standard product development frameworks (e.g., Pahl & Beitz) explicitly define design as embodiment and detail design—where function becomes form via architecture and part definition. Why Other Options Are Wrong: Distribution: Logistics and delivery, not shaping the product. Field service: Maintenance and support after deployment. Project management: Planning and oversight, not technical embodiment. None of the above: Incorrect because ‘‘Design’’ fits exactly. Common Pitfalls: Confusing process coordination with the creative technical act of defining geometry and structure. Final Answer: Design

Question 19

Which milestone is widely regarded as a pivotal event in the history of artificial intelligence (AI), formally launching the field and agenda for early research?

Accepted Answer

Introduction / Context: AI emerged from a series of landmark ideas and meetings. While several publications laid intellectual groundwork, one event is commonly cited as the formal birth of AI as a research field: the 1956 Dartmouth Conference, proposed and organized by John McCarthy with Marvin Minsky, Claude Shannon, and Nathaniel Rochester. The question asks for the pivotal event that inaugurated AI as a distinct discipline. Given Data / Assumptions: Multiple historically important dates are provided. We are selecting the event that explicitly convened researchers to define and launch AI. Pivotal implies field-forming, not just influential. Concept / Approach: While Turing’s 1950 essay profoundly influenced thinking, the Dartmouth workshop brought researchers together under the banner ‘‘artificial intelligence’’ to set a concrete research agenda. This is why textbooks and histories treat Dartmouth 1956 as AI’s official starting point, even though earlier works inspired it. Step-by-Step Solution: Review each option’s impact on AI’s formation.Recognize that Dartmouth 1956 explicitly coined and organized “AI” as a field program.Select the conference as the pivotal launching event.Acknowledge other items as influential precursors or less central. Verification / Alternative check: Standard AI histories cite the Dartmouth Conference proposal as the canonical birth certificate for AI as a field with a shared identity and agenda. Why Other Options Are Wrong: 1950 Turing essay: Foundational but not the field’s formal launch. 1949 ‘‘Organization of Behavior’’: Neuropsychology (Hebb); foundational but not the AI field launch. 1961 title listed: Not a canonical AI founding milestone. None: Incorrect because Dartmouth 1956 is accepted as pivotal. Common Pitfalls: Equating influential publications with the convening event that defined AI’s identity and research program. Final Answer: 1956, Dartmouth University Conference organized by John McCarthy

Question 20

Digital Equipment Corporation (DEC) popularized the first expert system routinely used in an industrial setting, known as XCON. Under what other name was this rule-based configurator widely known?

Accepted Answer

Introduction / Context: XCON was a landmark expert system deployed by Digital Equipment Corporation (DEC) to configure VAX computer orders. It demonstrated practical business value for rule-based systems by reducing configuration errors and speeding fulfillment. The question asks for the alternate name by which XCON was known in the research and practitioner community. Given Data / Assumptions: XCON is an expert system used in industry. It configured VAX systems and options. We need the alias used in the literature and within the project team. Concept / Approach: XCON was commonly referred to as R1 (short for Rule-based system 1). It encapsulated thousands of production rules that mapped customer requirements to compatible components, ensuring technically valid system configurations. R1's success helped catalyze widespread interest in expert systems for commercial problems beyond academic prototypes. Step-by-Step Solution: Identify the system: XCON, DEC’s configurator.Recall its literature alias: R1.Eliminate distractors tied to hardware (VAX, PDP-11) or unrelated names (MAGNOM).Select R1 as the correct alternate name. Verification / Alternative check: Case studies and AI textbooks consistently identify XCON as R1, emphasizing its rule-based architecture and measurable operational benefits at DEC. Why Other Options Are Wrong: VAX and PDP-11: DEC hardware product lines, not the expert system's alias. MAGNOM: Not the accepted alternate name for XCON. None: Incorrect because R1 is the recognized alias. Common Pitfalls: Confusing the configurator (software) with the systems it configured (VAX). Final Answer: R1

Artificial Intelligence Questions