Maes, P. (1990), Situated Agents Can Have Goals, Robotics and Autonomous Systems, 6:49-70.

@InProceedings{Maes90,
  author =       "Patti Maes",
  title =        "Situated Agents Can Have Goals",
  booktitle =    "Designing Autonomous Agents",
  pages =        "49--70",
  year =         "1990",
  editor =       "Patti Maes",
  publisher =    "MIT Press",
  summary =      "spreading activation networks. runtime arbittraton
                 among actions with respect to goals of system in
                 situation.",
}

Author of the summary: Jim Davies, 2000, jim@jimdavies.org

Cite this paper for:

• deliberative thinking paradigm
• emergent functionality
• Existing situated agent systems have problems with goals
• What an action selector should do [p52]
• goal-orientedness is the opposite of opportunism
• sticking to one goal as an emergent property
• deictic representation: describe only the immediate environment so that you don't need a new symbol for every thing on earth. [p67]
• indexical-functional representations

Summary:

Detailed outline:

This led to the introduction of reactive systems, situated automata, situated agents, interactional systems, routines, subsumption architectures, behavior-based architectures, universal plans, and action networks, to name a bunch. [references for these architectures are in the paper.]

These new architectures were characterized by:

• direct coupling of perception and action
• distributedness and decentralization
• dynamic interaction with the environment
• intrinsic mechanisms to cope with resource limits and incomplete knowledge

In the system presented, activities are not hard wired and are not precompiled. Existing systems have problems with goals:

• situation desciptions are exclusive so there is never a conflict of action [p50]
• the rules are hand-coded (which results in domains that are too specific) or "compiled on the basis of a declarative description of the desired behavior of the agent." This is no good either because the information relevent to the choice of action is available only at runtime- the programmar really cannot anticipate it.
• goals are crucial because: [p51]
  • You shouldn't have to reprogram the agent to change the goal.
  • goals cut down the space of possible actions
  • complex agents have many complex goals. Goal mediation and interaction is important.
  • they are important for self-consciousness and improvement of performance.

• favor goal satisfaction, particularly actions that satisfy more than one goal.
• exploits opportunities, can change current goal focus when necessary
• generally sticks to a goal rather than changing all the time
• looks ahead for future difficulties and opportunities (plans)
• gracefully degrades
• reactive and fast

Competence modules (cms) are linked:

• Successor links: links any cms from y to x where something in the add list of x is a condition of y.
• Predecessor links: from y to x whenever there is a a successor link from x to y. [p54]
• Conflicter link: links any cms from y to x where something in the delete list of x is a condition of y.

Links make cms inhibit and activate each other. The observed situation activate cms with partial matches in the condition slot. Goals are also a source of activation. There are once-only and permanent goals (achieved continuously). Goals activate cms that have that goal in the add-list. Already achieved goals inhibit cms that would undo them. Executable cms spread activation forward through successor links, non-execuatbles spread activation to predecessor links. Conflicters inhibit each other. There is decay as well. [p56] Activation is 1/n where n is the number of propositions in the relevent list. This evens out the amount of activation for cms with more or fewer preconditions or whatever. The effect of a proposition is spread throughout the things it's linked to.

You can change a parameter to make it more goal-oriented and less opportunistic. [p59]

The system shows interesting biases. It favors sticking to one goal because when you act on one line of solution, you get closer to that solution's completion, and the way the activation works, that line of action will win out, generally. [p62]

The "thoughfulness" can be changed by the parameter that determines how long activation spreads before a decision is made. Longer time means looking farther ahead into the future. [p66] A long time makes for a closer-to-optimal solution, but this is no good in a rapidly changing environment. More thoughtfulness is also a speed tradeoff. [p67]

Why won't it run into the same problems as AI planners? [p67]

• algorithm is less costly. It does not keep track of a hypothetical future state or partial plan.
• The system is not restarted at every timestep
• All interactions are local, and would be a good candidate for a parallel implementation.

deictic representation: describe only the immediate environment so that you don't need a new symbol for every thing on earth. (indexical-functional representations.)

limitations:

• occasionally gets into loops (because it has no memory of what it has done.
• How to select parameters?

Summary author's notes:

• looks a lot like a production system to me.