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AI starts with problem representation
The same environment can be easy or impossible depending on how states, actions, observations, and objectives are encoded.
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01:198:440 · Computer Science · School 01 — New Brunswick School of Arts and Sciences
Introduction to Artificial Intelligence is a broad survey of classical AI methods, usually organized around intelligent agents, search, knowledge or reasoning under constraints, uncertainty, planning, and machine-learning-based decision making.
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Introduction To Discrete Structures I · Calculus II For Mathematical And Physical Sciences · 01:640:192
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Students rate teaching
SIRS · 925 responses · 2014–2025
Spring 2027
6 open in snapshot · 2 instructors
When it meets
Livingston
Checked-in sections · current seat counts are unavailable.
MW 5:40 PM · Livingston · 3 sections · 3 open
AI-generated overview
Higher means more demanding · AI summary
6 units · topic sequence may vary by instructor
The course usually begins by treating AI systems as agents that observe, reason, and act. From there, classical search becomes the first major toolkit for turning a problem into states, actions, and cost-guided exploration.
Once basic pathfinding-style search is in place, most Rutgers AI syllabi branch into richer decision settings such as optimization, game playing, and constraints. This is where alpha-beta pruning, CSPs, and planning ideas often show up together.
Several Rutgers descriptions emphasize that AI is not just search speed but also representation. Logic-style reasoning, explicit world models, and planning encode what the agent believes and how it should act when the environment is structured.
A major middle block introduces what happens when the agent does not know the world perfectly. Bayesian networks, hidden Markov models, filtering, utility, and decision processes all formalize acting under uncertainty.
Rutgers-hosted AI course materials often connect uncertainty to action selection through Markov Decision Processes and reinforcement learning. The common thread is choosing good behavior over time instead of only classifying a fixed example.
Later weeks typically move into supervised learning methods that help an agent infer patterns from data. Depending on the offering, this can include naive Bayes, perceptrons, SVMs, neural networks, and a lighter look at perception or vision-related ideas.
Public r/rutgers posts generally frame 440 as one of the more demanding but also more distinctive CS electives: interesting if you want breadth across AI methods, but often described as more project-heavy and less lightweight than students first assume.
The same environment can be easy or impossible depending on how states, actions, observations, and objectives are encoded.
Many early topics differ mainly in how they rank possibilities, prune choices, or structure what counts as a good partial solution.
When observations are noisy or incomplete, the agent needs beliefs, probabilities, and expected-utility reasoning rather than only deterministic logic.
Later AI topics show how data-driven models can supply the patterns, scores, or policies that are hard to specify manually.
Common pitfall
Instead
Separate deterministic search, probabilistic inference, and learning problems because each assumes a different kind of knowledge.
Common pitfall
Instead
First define the states, variables, or probabilities clearly; only then choose A*, CSP reasoning, an HMM, or a classifier.
Common pitfall
Instead
When probabilistic models or learning units begin, review the math early instead of waiting until the project or exam.
Common pitfall
Instead
Student discussion and official syllabi both point to projects or programming-heavy assignments, so leave time for debugging and partner coordination.
6 open · Livingston
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114526 | Open | Boularias | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Friday 2:15 PM-3:10 PM at BE 253TIL 232BE 253 | Livingston |
| 0214527 | Open | Boularias | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Tuesday 7:45 PM-8:40 PM at TIL 116TIL 232TIL 116 | Livingston |
| 0314528 | Open | Boularias | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Tuesday 5:55 PM-6:50 PM at LSH B117TIL 232LSH B117 | Livingston |
| 0514529 | Open | Xintong Wang | Monday 5:40 PM-7:00 PM at LSH A102; Wednesday 5:40 PM-7:00 PM at LSH A102; Wednesday 9:35 PM-10:30 PM at BE 250LSH A102BE 250 | Livingston |
| 0614530 | Open | Xintong Wang | Monday 5:40 PM-7:00 PM at LSH A102; Wednesday 5:40 PM-7:00 PM at LSH A102; Monday 7:45 PM-8:40 PM at LSH B117LSH A102LSH B117 | Livingston |
| 0714531 | Open | Xintong Wang | Monday 5:40 PM-7:00 PM at LSH A102; Wednesday 5:40 PM-7:00 PM at LSH A102; Wednesday 7:45 PM-8:40 PM at BE 250LSH A102BE 250 | Livingston |
Historical student surveys
Teaching
3.85
Course quality
3.79
Response rate
29.5%
Coverage
28 offerings · 2014–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Abdeslam Boularias | 6 | 4.33 | 4.27 |
| Charles Cowan | 4 | 4.88 | 4.78 |
| Troy Mcmahon | 4 | 2.28 | 2.20 |
| Bekris K | 3 | 4.13 | 4.07 |
| Boularias Abdeslam | 2 | 3.70 | 3.80 |
| Kostas Bekris | 2 | 4.00 | 3.70 |
Catalog and planning context
Credits
4
Current campuses
Livingston
Current availability
6 open of 6
Catalog terms
Fall, Spring, Summer
Core codes
None listed
Loaded terms
6
((01:198:205 or 14:332:202 or 14:332:312) and (01:640:152)) <em> OR </em> ((01:198:205 or 14:332:202 or 14:332:312) and (01:640:192))
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