CSE304 review
Past papers are available at ../past-papers/cse304.
Lecture 2
Notion of an agent
Def1: An agent is a computer system that is capable of independent action on behalf of its user or owner.
Def2: An agent is a computer system capable of autonomous action in some environment, in order to achieve its delegated goals.
Notion of multi-agent systems
A multi-agent system is one that consists of a number of agents, which interact with one-another.
In most general cases, agents will be acting on behalf of users with different goals and motivations.
To unsuccessfully interact, they will require the ability to cooperate, coordinate, and negotiate with each other, much as people do.
Agent Design
TBA
Society Design
TBA
Properties of Environments
Accessible vs. inaccessible
Deterministic vs. non-deterministic
Static vs. dynamic
Discrete vs. continuous
Decisions and Actions
The key problem for an agent is to decide which of its action to perform in order to best satisfy its(design) objectives.
Intelligent Agents
Reactivity: perceive environment, response quickly to changes
Pro-activeness: take goal-directed behavior
Social ability: interact with other agents
cooperation
coordination
negotiation
Difference between objects and agents
Objects encapsulate some states
Objects communicate via message passing
Objects have methods corresponding to operations that may be performed on this state
Agents embody a stronger notion of autonomy than objects - in particular they decide whether or not to perform an action on request from another agent
Agents are capable of flexible behavior
Lecture 3
Agent Architectures
An agent architecture defines:
key data structures
operations on data structures
control flow beteween operations
hybrid agent combines best from symbolic reasoning agents and reactive agents
symbolic reasoning: use explicit logical reasoning in order to decide what to do
reactive: ?
Deductive Reasoning Agents
Traditional approach to build AI systems (symbolic AI)
symbolic representation of environment and behavior
syntactic manipulation of symbolic representation
symbolic representation -> logical formulae
syntactic manipulation -> logical deduction (theorem proving)
Transduction Problem: the problem of translating the real world into an accurate, adequate symbolic description, in time for that description to be useful Representation/Reasoning Problem: the problem of how to symbolically represent information about complex real-world entities and processes, and how to get agents to reason with this information in time for the results to be useful
...
Lecture 4
Practical Reasoning
Practical reasoning is reasoning directed towards actions - the process of figuring out what to do
Practical reasoning a matter of weighing conflicting considerations for and against competing options, where the relevant considerations are provided by what the agent desires/values/cares about and what the agent believes.
Practical reasoning is different than theoretical reasoning (directed towards beliefs)
Practical Reasoning = Deliberation + Means-Ends Reasoning
deliberation: deciding what state of affairs we want to achieve - the outputs of deliberation are intentions
means-ends reasoning: deciding how to achieve these states of affairs - the outputs of means-ends reasoning are plans
Intentions & Desires:
Intentions are stronger in influencing actions, than desires
Intentions drive means-end reasoning
Intentions persist
Intentions constraint further deliberation
Intentions influence beliefs upon which practical reasoning is based
Symbolic Representation of Beliefs, Desires and Intentions
B a variable for current beliefs
Bel set of all such beliefs
D a variable for current desires
Des set of all desires
I a variable for current intentions
Int set of all intentions
Means-Ends Reasoning
Means-ends reasoning is the process of deciding how to achieve an end (an intention an agent has) using the available means (i.e., the actinos an agent can perform)
Means-end reasoning is better known as planning
Planning is essentially an automatic programming
Planner
a goal, intention or task
the current state of environment - the agent's beliefs
the actions available to the agent
As output a planner generates a plan
Basic Control Structure for a Practical Reasoning Agent
A loop:
observes the world, and updates beliefs
deliberates to decide what intention to achieve
uses means-ends reasoning to find a plan to achieve these intentions
executes the plan
Note: Doesn't it basically reinforcement learning?
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