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510104 B Intelligent Systems
Teaching Scheme Examination Scheme
Lectures: 3 Hrs/week Theory: 100 Marks
Total Credits : 03
1. Intelligent Agents
Introduction. How agents should act, structure: Table-driven, Simple reflex, Goal-based,
Utility-based, Agents that keep track of world, Environments.
2. Problem Formulation
Problem solving, Formulating problems: Knowledge and problem types, Well-defined
problems and solutions, Measuring problem-solving performance, Choosing states and
actions.
3. Search Methods
Searching for solutions, Search strategies: Time, space, optimality and completeness
issues. Un-informed search methods: Breadth-first, Depth-first, Iterative deepening, Bidirectional
search, Avoiding repetitions, Constraint satisfaction search. Informed search
methods: Best first search: Greedy search, A*, Heuristic functions, Memory bounded
search: IDA*, SMA*. Iterative improvement algorithms: Hill climbing, Simulated
annealing, Application in CSPs.
4. Planning
A simple planning agent. From problem solving to planning: Representation of actions,
Representation of states, Representation of goals, Representation of plans. Basic
representation for planning: Representations for states and goals, Representation for
actions, Situation space and plan space, Representations for plans.
5. Partial Order Planning
Example: partial order planning, Initial plan, Achieving preconditions, Protected links
and threats, Promotion and demotion, Recovering from dead ends. A partial-order
planning algorithm, Planning with partially instantiated operators, Knowledge
engineering for planning: Blocks world, Shakey's world.
6. Practical Planning
Practical planners: Spacecraft assembly, Job shop scheduling, Space mission scheduling,
Buildings and aircraft carriers. Hierarchical decomposition: Extending the language,
Modifying the planner. Analysis of hierarchical decomposition: Decomposition and
sharing, Decomposition versus approximation. More expressive descriptions: Conditional
effects, Negated and disjunctive goals, Universal quantification, A planner for expressive
operator descriptions. Resource constraints: Using measures in planning, Temporal
constraints.
7. Planning and Acting
8
Conditional planning and execution monitoring, Conditional planning: The nature of
conditional plans, Algorithm for generating conditional plans, Extending the plan
language. A simple re planning agent: Bounded vs. unbounded indeterminacy, Simple re
planning with execution monitoring. Fully integrated planning and execution. Discussion
and extensions: Comparing conditional planning and re planning, Coercion, abstraction
and aggregation.
8. Uncertain Knowledge and Reasoning
Uncertainty, Probabilistic Reasoning Systems, Making simple decisions, Making
complex decisions. Reasoning: Agents that reason logically, First-order logic, Inferences
in 1st order logic
Reference Books:
1. Russell S., Norving P., “Artificial Intelligence – Modern Approach”
2. Henry P., “Artificial Intelligence”, 3rd Ed., Winstone
3. Patric H., “Lisp programming language”, Winstone
For more details, visit http://www.unipune.ernet.in/stud_info/Syllabi/Syllabus_2008.html
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