CSE 591 Autonomous Agents: Theory and practice
Fall 2001
T Th 1:40 -- 2:55 PM; ECG G 335
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The meanings of the word `agent' in dictionary.com are
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1. one that acts or has the power or authority to act.
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2. one empowered to act for or represent another.
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3. a means by which something is done or caused.
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4. a force or substance that causes a change.
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5. a representative or official of a government or administrative
department of a government.
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6. a spy.
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7. (in linguistics) the noun noun phrase that specifies the
person through whom or the means by which an action effected.
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The meanings of the word `autonomous' in dictionary.com are
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1. not controlled by others or by outside forces.
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2. independent in mind or judgement or government; self
directed.
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3.
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a. independent of the laws of another state or
government; self-governing.
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b. of or relating to a self-governing entity.
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c. self-governing with respect to local or internal
affairs.
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In our context, an artificial computer-driven agent is an entity
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that acts and that causes change. Since we would like to have
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some control over the agent, lest it makes us its slave, the qualifier
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`autonomous' in the context of an artificial computer-driven agent
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means that its actions are not micro-managed by other entities (people),
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but we do have some control over it. In other words an autonomous
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artificial computer-driven agent, which we will simply refer henceforth
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as an autonomous agent, can take high-level directives and goals and
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can figure out what actions it needs to take and execute those actions.
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To be able to do this among other things, an autonomous agent must
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understand high level directives, must know what actions it can do and
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what changes these action would cause, must have knowledge about the
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environment it is in and how it might change or react to its
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actions, must have the reasoning ability to figure out what it
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needs to do to achieve its goal, need to revise its plans if the
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environment does no co-operate, need to be able to assimilate its
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observations and make conclusions that it missed observing or can
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not directly observe and learn from its interaction with the
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environment.
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In this course we will cover the theory behind endowing the above
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capabilities to an agent. In particular,
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1. We will describe several languages which can be used to express
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directives or goals to an agent. These languages will be based on
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temporal logic, and we will have notion such as temporal goals,
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and maintenance goals.
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2. We will describe action description languages using which an
agent
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can express the impact of its actions on the environment.
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3. We will describe languages which express an agents plan of action.
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4. We will define when an agents plan of action, based on its
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knowledge of the type (2), satisfies the directives or goals of
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type (1), assuming that the environment is co-operative.
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We will discuss how to create simple plan of actions.
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5. We will then remove the assumption about the co-operativeness
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of the environment and describe a language which can be used to record
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observations. We will then define `observation assimilation' and
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revising the original plan of action to create new `plans from the
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current situation'.
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6. We will generalize the above (1-5) to include `knowledge producing
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actions', which in their purest forms do not change the world, but
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change the agent's knowledge about the world. We will describe how
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agents can make plans using such knowledge producing actions to
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achieve various kinds of goals: achievement goals, knowledge goals,
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diagnostic goals.
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7. We will discuss several agent architectures: deliberative,
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reactive and hybrid and how and where they use 1-6.
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8. We will consider agents in stochastic worlds, and/or with
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actions with stochastic effect. We will consider the issue of
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learning in such a scenario.
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9. We will discuss the complexity (how hard it is) of the various
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tasks (such as planning) in 1-8 that an agent must do.
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10. Finally, there will be a project where students will be
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required to develop a software agent based on 1-8.
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Home Work + small assignments 20-30%
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2 Exams
50%
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project
20-30%
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There will be one exam in the middle of the semester and one on the last
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day of the class. There will not be any exam on the day of the finals.
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Study material: Will be based on several papers
and chapters from
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the following two books, and slides.
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1. Heteregenous agent systems. Subrahmanian, Bonatti, Dix, Eiter,
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Kraus, Ozcan and Ross. MIT Press. 2000.
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2. Knowledge in action: logical foundations for specifying and
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implementing dynamical systems. Raymond Reiter. MIT Press. 2001.
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Other books that may be useful for the project.
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a. Multi-agent systems: a modern approach to distributed AI. Ed:
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Gerhard Weiss. MIT Press. 1999.
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b. Software Agents. Ed: Jeffrey M. Bradshaw. AAAI Press/MIT Press.
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1997.
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c. Intelligent Agents, several volumes. Springer.
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