Philosophy, Computing, and Artificial Intelligence

PHI 319. The Observation-Thought-Decision-Action Cycle.

To implement the observation-thought-decision-action cycle in the logic programming/agent model on a machine, we need to think more clearly about the steps in the cycle.

The Cycle as a Sequence of Instructions

The cycle, in the "Fox and Crow" example, begins when the fox realizes it is hungry. The fox, as a machine, has sensors. The information the sensors provide become beliefs in the KB.

We can think of this step in the cycle as revising the beliefs in the KB based on the information perception provides. For the fox, we add the proposition that he is hungry to his KB. In general, though, the process of revising beliefs based on new information is more complicated. For now, then, we can pretend for now that there is a function, revise, that returns a revised set of beliefs given a set of beliefs and an observation (a percept).

So, at this point in our understanding, the observation-thought-decision-action cycle looks something like this:

1. KB ← KB_o;    /* KB_o are the initial beliefs */
2. while alive do
3.      get next percept p;
4.      KB ← revise (KB, p);
?.
?. end-while

Somehow the fox has a set of initial beliefs. The fox makes an observation ("get next percept p"). The fox revises his KB on the basis of this observation. Some other things happen. The cycle repeats.

What are the "others things [that] happen"?

In the case of the fox, the observation led to a belief ("I am hungry") that triggered a maintenance goal. The maintenance goal, in turn, gave rise to an achievement goal.

Achievement goals function like desires. The fox gets the desire to have food and eat it.

More generally, we can pretend there is a function, options, that returns a set of desires (achievement goals) given the KB and the set of maintenance goals the agent possesses.

1. KB ← KB_o;    /* KB_o are the initial beliefs */
2. while alive do
3.      get next percept p;
4.      KB ← revise (KB, p);
5.      D ← options (KB, MG);
?.
?. end-while

Intentions and Plans in Practical Reasoning

Once we imagine that options returns a set of desires (achievement goals), we need a way to select the desire (achievement goal) the agent intends to pursue. To solve this problem, we can pretend there ⊕ Michael Bratman (in Intentions, Plans, and Practical Reason (Harvard University Press, 1987)) argues that rational agents tend to commit themselves to their intentions and thus tend not to interrupt this commitment by recalculating their desires and forming new intentions.

To understand Bratman's idea, notice that steps (3) - (6) in the algorithm do not occur instantaneously. They take time, and the world might have changed during this time in such a way that the agent would have formed a new set of intentions if he were to recalculate.

There are limits to the extent of an agent's commitment to an intention before it is acting irrationally, but the agent must sometimes commit itself if it is ever to do anything more than calculate its intentions. is a function, filter, that returns this intention given the KB and the set of desires (achievement goals).

1. KB ← KB_o;    /* KB_o are the initial beliefs */
2. while alive do
3.      get next percept p;
4.      KB ← revise (KB, p);
5.      D ← options (KB, MG);
6.      I ← filter (KB, D);
?.
?. end-while

Given the intention, what is the mechanism for achieving it?

The agent, it seems, must have plans.

The agent can construct them or retrieve them from a plan library. We can pretend (as we have gotten good at it) that there is a function, plan, that returns a plan given the KB and the intention.

What happens in the function "execute (π)"?

One possibility is that the agent executes the steps in the plan until it is empty:

1. KB ← KB_o;    /* KB_o are the initial beliefs */
2. while alive do
3.      get next percept p;
4.      KB ← revise (KB, p);
5.      D ← options (KB, MG);
6.      I ← filter (KB, D);
7.      π ← plan (KB, I);
8.      while not empty (π) do
9.          α ← head of π;
10.          execute (α);
11.          π ← tail of π;
12.          end-while
13. end-while

This gives the agent some but not much intelligence.

For non-empty plans, the agent executes the plan one step at a time without thinking about how the world might have changed in the time it takes to execute the plan.

Think about the example of the "fox and crow." The fox would not be very intelligent if it were to praise the crow and then were to try to pick up the cheese whether or not the crow drops it.

Logic-Based Production Systems

Kowalski's Logic-Based Production System (LPS) ⊕ For brief explanations of LPS, see How to do it with LPS (Logic-based Production System) and LPS. is an extension of logic programming (LP). The idea is to supplement logic programming with the observation-thought-decision-action cycle.

The resulting model of intelligence in LPS is interesting, but it takes some effort to see it.

The following is a LPS Kowalski has provided program for a version of the fox and the crow example. The graph (in the side notes) shows how the timeline unfolds when the program is run. ⊕ timeline for the fox and the crow


      fluents has/2, near/2.

      events becomeHungry/1, beNear/2.

      actions sing/1, obtain/2, eat/2, praise/2, pickUp/2.



      food(cheese).

      initially has(crow, cheese).

     
     
      observe becomeHungry(fox) from 1.



      if becomeHungry(Agent) 

      then food(X), obtain(Agent, X), eat(Agent, X).



      if praise(Agent, crow) 

      then sing(crow).



      obtain(Agent, Object) 

      if  beNear(Agent, Object),

      near(Agent, Object),

      pickUp(Agent, Object).



      beNear(fox, cheese) 

      if has(crow, cheese), 

      praise(fox, crow).



      sing(crow) terminates has(crow, cheese) 

      if has(crow, cheese).

      
      sing(crow) initiates near(fox, cheese) 

      if has(crow, cheese).



      pickUp(Agent, Object) initiates has(Agent, Object).

⊕ 1. KB ← KB_o;    /* KB_o are the initial beliefs */
2. while alive do
3.      get next percept p;
4.      KB ← revise (KB, p);
?.
?. end-while We can see in the graph of this LPS program (in the side notes) that the fox, at time 1, observes it has become hungry and thus forms the belief in its KB that it is hungry. Once the fox has this belief, because of how its mind works, it gets the achievement goal to obtain and eat some food:

food(X), obtain(fox, X), eat(fox, X)

To achieve the first goal on this list of achievement goals, the fox first passes "food(X)" as a query to its KB. The reply is that "cheese" is a food. This in turn transforms the list of achievement goals so that it is

obtain(fox, cheese), eat(fox, cheese)

It is initially a little unclear, though, how we are supposed to think about the intelligence in this step.

Something has gone wrong if the achievement goal triggered by a maintenance goal is logical consequence how the agent already understands the world to be. An achievement goal is something the agent must change the world to achieve, but notice that the fox's achievement goal is indefinite. The goal is to make it true that there is a food, that the fox obtain it, and that the fox eat it. The only way to make this true is to make an instance of it be true.

⊕ 1. KB ← KB_o;    /* KB_o are the initial beliefs */
2. while alive do
3.      get next percept p;
4.      KB ← revise (KB, p);
5.      D ← options (KB, MG);
6.      I ← filter (KB, D);
?.
?. end-while So by passing "food(X)" as a query to its KB, the fox is asking itself whether "∃X food(X)" is a logical consequence of what it believes in order to give itself an instance of the indefinite goal it can try to achieve.

The next step in the fox's thinking also requires some reflection to work out. The fox passes "obtain(fox cheese)" to its KB. It might be that this part of the achievement goal requires no action on the part of the fox because "obtain(fox cheese)" is a logical consequence of the beliefs in its KB, but this is not that is going on.

In fox's KB, there is a rule for "obtain(fox, cheese)." This rule, though, is not quite what we would expect from the point of view of logic programming. The rule contains "events," "actions," and "fluents."

What are these things?

Events and actions change state. A fluent is a state that can change over time.

The rule, then, in the fox's KB for "obtain(Agent, Object)" says in part that "obtain(Agent, Object)" is true if the event "beNear(Agent, Object)" happens. ⊕ "beNear" and "pickUp" are written in lower camel case. This practice writes without spaces or punctuation. The first word is in lower case. Subsequent words begin with an upper case letter.

1. KB ← KB_o;    /* KB_o are the initial beliefs */
2. while alive do
3.      get next percept p;
4.      KB ← revise (KB, p);
5.      D ← options (KB, MG);
6.      I ← filter (KB, D);
7.      π ← plan (KB, I);
8.      while not empty (π) do
9.          α ← head of π;
10.          execute (α);
11.          π ← tail of π;
12.          end-while
13. end-while This event can only happen if an instance of it happens. The fox has a rule for one instance. This instance of the event event "beNear(Agent, Object)" is the event "beNear(fox, cheese)." The rule for "beNear(fox, cheese)" says that this event happens if "has(crow, cheese)" is true and the fox makes "praise(fox, crow)" true. The fox can make this true because "praise" is a basic action for the fox.

So by passing "obtain(fox cheese)" to its KB, the fox is checking whether this part of its achievement goal is a logical consequence of its KB and taking action to make it a logical consequence if it determines that it is not.

What we have Accomplished in this Lecture

We saw in the lecture on the "fox and crow" that it is not straightforward to incorporate the thinking involved in achieving an achievement goal into the logic programming/agent model. To begin to clarify this thinking, we tried to set out the steps in the observation-thought-decision-action cycle as sequence of instructions in an algorithm. Further, we briefly considered Kowalski's Logic-Based Production System (LPS). This system is Kowalski's attempt to combine logic programming with an observation-thought-decision-action cycle.

Philosophy, Computing, and Artificial Intelligence

The Cycle as a Sequence of Instructions

Intentions and Plans in Practical Reasoning

Logic-Based Production Systems

What we have Accomplished in this Lecture

Home These lecture notes are incomplete. Comments or suggestions are welcome!

Home

These lecture notes are incomplete. Comments or suggestions are welcome!