Knowledge Acquisition and Problem Solving

PowerPoint PresentationCS 785 Fall 2004
Overview
Demo: Agent Teaching and Rule Learning
The Rule Learning Problem in Disciple
Strategies for Explanation Generation
What is Learning?
acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality),
discover new knowledge and theories (by creating hypotheses that explain some data or phenomena), or
acquire skills (by gradually improving their motor or cognitive skills through repeated practice, sometimes involving little or no conscious thought).
Learning results in changes in the agent (or mind) that improve its competence and/or efficiency.
Learning is a very general term denoting the way in which people and computers:
2004, G.Tecuci, Learning Agents Center
The Disciple agent is concerned with the first type of learning: acquiring and organizing knowledge from a subject matter expert (by building, modifying and organizing internal representations of some external reality). The external reality is a strategic scenario and how a subject matter expert reasons to identify and test strategic COG candidates for that scenario.
The architecture of an intelligent agent
Problem Solving
in the knowledge base.
Implements a general problem solving method that uses the knowledge from the knowledge base to interpret the input and provide an appropriate output.
Data structures that represent the objects from the application domain,
general laws governing them, actions that can be performed with them, etc.
Ontology
Rules/Cases/…
Overview
Generalization and specialization rules
A generalization rule is a rule that transforms an expression into a more general expression.
A specialization rule is a rule that transforms an expression into a less general expression.
The reverse of any generalization rule is a specialization rule.
Fundamental to learning are the processes of generalization and specialization.
We will present several basic rules for generalizing or specializing expressions representing concepts. These rule are used to generalize concepts or to specialize concepts.
Turning constants into variables
Generalizes an expression by replacing a constant with a variable.
?O1 is multi_group_force
The set of multi_group_forces with any number of subgroups.
Allied_forces_operation_Husky
Axis_forces_Sicily
Japan_1944_Armed_Forces
The top expression represents the following concept: the set of multi group forces with 5 subgroups. This set contains, for instance, Axis_forces_Sicily from the Sicily_1943 scenario. By replacing 5 with a variable ?N1 that can take any value, we generalize this concept to the following one: the set of multi group forces with any number of subgroups. In particular ?N1 could be 5. Therefore the second concept includes the first one.
Conversely, by replacing ?N1 with 5, we specialize the bottom concept to the top one.
The important thing to notice here is that by a simple syntactic operation (transforming a number into a variable) we can generalize a concept. This is how an agent generalizes concepts.
Climbing/descending the generalization hierarchy
Generalizes an expression by replacing a concept with a more general one.
?O1 is single_state_force
The set of single state forces governed by representative democracies
democratic_government representative_democracy
democratic_government
representative_democracy
parliamentary_democracy
One can also generalize an expression by replacing a concept from its description with a more general concept, according to some generalization hierarchy. The reverse operation, of replacing a concept with a less general one, leads to the specialization of an expression.
The agent can also generalize a concept by dropping a condition. That is, by dropping a constraint that its instances must satisfy.
This rule is illustrated in the next slide.
Dropping/adding condition
Generalizes an expression by removing a constraint from its description.
?O1 is multi_member_force
The set of multi-member forces that have international legitimacy.
The set of multi-member forces (that may or may not have international legitimacy).
Generalizing/specializing numbers
Generalizes an expression by replacing a number with an interval, or by replacing an interval with a larger interval.
?O1 is multi_group_force
The set of multi_group_forces with exactly 5 subgroups.
The set of multi_group_forces with at least 3 subgroups and at most 7 subgroups.
The set of multi_group_forces with at most 10 subgroups.
A concept may also be generalized by replacing a number with an interval containing it, or by replacing an interval with a larger interval. The reverse operations specialize the concept.
Yet another generalization rule, which is illustrated in the next slide, is to add alternatives.
According to the expression from the top of this slide, ?O1 is any alliance. Therefore this expression represents the following concept: the set of all alliances.
This concept can be generalized by adding another alternative for ?O1, namely the alternative of being a coalition. Now ?O1 could be either an alliance or coalition. Consequently, the expression from the bottom of this slide represents the following more general concept: the set of all alliances and coalitions.
Adding/removing alternatives
The set including both the alliances and the coalitions.
Generalizes an expression by replacing a concept C1 with the union (C1 U C2), which is a more general concept.
Overview
Representative learning strategies
Empirical inductive learning from examples
The learning problem
• a set of positive examples (E1, ..., En) of a concept
• a set of negative examples (C1, ... , Cm) of the same concept
• a learning bias
• other background knowledge
• a concept description which is a generalization of the positive
examples that does not cover any of the negative examples
Purpose of concept learning:
Predict if an instance is an example of the learned concept.
Learning from examples
Compares the positive and the negative examples of a concept, in terms of their similarities and differences, and learns the concept as a generalized description of the similarities of the positive examples. This allows the agent to recognize other entities as being instances of the learned concept.
Requires many examples
Illustration:
Negative examples of cups: N1 …
Description of the cup concept: has-handle(x), ...
The goal of this learning strategy is to learn a general description of a concept (for instance the concept of “cup”) by analyzing positive examples of cups (i.e. objects that are cups) and negative examples of cups (i.e. objects that are not cups). The learning agent will attempt to find out what is common to the cups and what distinguishes them from non-cups. For instance, in this illustration, the agent may learn that a cup should have a handle because all the positive examples of cups have handles, and the negative examples of cups do not have handles. However, the color does not seem to be important for a cup because the same color is encountered for both cups and non-cups.
To learn a good concept description through this learning strategy requires a very large set of positive and negative examples. On the other hand, this is the only information that the agent needs. That is, the agent does not require prior knowledge to perform this type of learning.
The result of this learning strategy is the increase of the problem solving competence of the agent. Indeed, the agent will learn to do things it was not able to do before. In this illustration it will learn to recognize cups, something that it was not able to do before.
Explanation-based learning (EBL)
The EBL problem
has-handle(o1), has-flat-bottom(o1), up-concave(o1),...
(e.g. features present in the examples)
• BK: cup(x) Ü liftable(x), stable(x), open-vessel(x).
liftable(x) Ü light(x), graspable(x).
up-concave(x).
Explanation-based learning
cup(o1): color(o1, white), made-of(o1, plastic), light-mat(plastic), has-handle(o1), has-flat-bottom(o1), up-concave(o1),...
Learns to recognize more efficiently the examples of a concept by proving that a specific instance is an example of it, and thus identifying the characteristic features of the concept.
The proof identifies the characteristic features:
Proof generalization generalizes them:
• color(o1,white) is not needed to prove cup(o1)
• made-of(o1, plastic) is needed to prove cup(o1)
• made-of(o1, plastic) is generalized
cup(o1)
stable(o1)
liftable(o1)
graspable(o1)
light(o1)
light-mat(plastic)
made-of(o1,plastic)
The goal of this learning strategy is to improve the efficiency in problem solving. The agent is able to perform some task but in an inefficient way. We would like to teach the agent to perform the task faster. Consider, for instance, an agent that is able to recognize cups. The agent receives a description of a cup that includes many features. The agent will recognize that this object is a cup by performing a complex reasoning process, based on its prior knowledge. This process is illustrated by the proof tree from the left hand side of this slide. The object o1 is made of plastic which is a light material. Therefore o1 is light. o1 has a handle and therefore it is graspable. Being light and graspable, it is liftable. And so on … being liftable, stable and an open vessel, it is a cup. However, the agent can learn from this process to recognize a cup faster. Notice that the agent used the fact that o1 has a handle in order to prove that o1 is a cup. This means that having a handle is an important feature. On the other hand the agent did not use the color of o1 to prove that o1 is a cup. This means that color is not important. Notice how the agent reaches the same conclusions as in learning from examples, but through a different line of reasoning, and based on a different type of information.
The next step in the learning process is to generalize the tree from the left hand side into the tree from the right hand side. While the tree from the left hand side proves that the specific object o1 is a cup, the tree from the right hand side shows that any object x that is made of some light material y, has a handle and some other features is a cup. Therefore, to recognize that an object o2 is a cup, the agent only needs to look for the presence of these features discovered as important. It no longer needs to build a complex proof tree. Therefore cup recognition is done much faster.
Finally, notice that the agent needs only one example to learn from. However, it needs a lot of prior knowledge to prove that this example is a cup. Providing such prior knowledge to the agent is a very complex task.
• Needs only one example
General features of Explanation-based learning
Learning by analogy
• ACCESS: find a known entity that is analogous with the input entity.
• MATCHING: match the two entities and hypothesize knowledge.
• EVALUATION: test the hypotheses.
• LEARNING: store or generalize the new knowledge.
Learns new knowledge about an input entity by transferring it from a known similar entity.
The learning problem
The learning method
Learning by analogy: illustration
Illustration: The hydrogen atom is like our solar system.
Learning by analogy is the process of learning new knowledge about some entity by transferring it from a known entity. For instance, I can teach students about the structure of the hydrogen atom by using the analogy with the solar system. I am telling the students that the hydrogen atom has a similar structure with the solar system, where the electrons revolve around the nucleus as the planets revolve around the sun.
In this illustration, the ACCESS problem is already solved. I have already told the students what the source is (the solar system).
Learning by analogy is the process of learning new knowledge about some entity by transferring it from a known entity.
For instance, I can teach students about the structure of the hydrogen atom by using the analogy with the solar system. I am telling the students that the hydrogen atom has a similar structure with the solar system, where the electrons revolve around the nucleus as the planets revolve around the sun.
The students may then infer that other features of the solar system are also features of the hydrogen atom. For instance, in the solar system, the greater mass of the sun and its attraction of the planets cause the planets to revolve around it. Therefore, we may conclude that this is also true in the case of the hydrogen atom: the greater mass of the nucleus and its attraction of the electrons cause the electrons to revolve around the sun. This is indeed true and represents a very interesting discovery.
The main problem with analogical reasoning is that not all the facts related to the solar system are true for the hydrogen atom. For instance, the sun is yellow, but the nucleus is not. Therefore, facts derived by analogy have to be verified.
A general heuristic is that similar causes have similar effects. That is, if A is similar to A’ and A causes B. Then we would expect A’ to cause B’ which should be similar to B.
Learning by analogy: illustration
Illustration: The hydrogen atom is like our solar system.
The Sun has a greater mass than the Earth and attracts it, causing the Earth to revolve around the Sun.
The nucleus also has a greater mass then the electron and attracts it.
Therefore it is plausible that the electron also revolves around the nucleus.
General idea of analogical transfer: similar causes have similar effects.
The main problem with analogical reasoning is that not all the facts related to the solar system are true for the hydrogen atom. Therefore, facts derived by analogy have to be verified.
A general heuristic is that similar causes have similar effects. That is, if A is similar to A’ and A causes B. Then we would expect A’ to cause B’ which should be similar to B.
Multistrategy learning
Multistrategy learning is concerned with developing learning agents that synergistically integrate two or more learning strategies in order to solve learning tasks that are beyond the capabilities of the individual learning strategies that are integrated.
Examples
Learning
The individual learning strategies have complementary strengths and weaknesses. For instance learning from example requires a lot of example while explanation-based learning requires only one example. On the other hand, learning from examples does not require any prior knowledge while explanation-based learning requires a lot of prior knowledge.
Multistrategy learning attempts to synergistically integrate such complementary learning strategies, in order to take advantage of their relative strengths to compensate for their relative weaknesses.
The Disciple agent uses a multistrategy learning strategy, as will be presented in the following.
Overview
GIVEN:
• an example of a problem solving episode;
• a knowledge base that includes an object ontology and a set of problem solving rules;
• an expert that understands why the given example is correct and may answer agent’s questions.
DETERMINE:
generalization of the specific problem solving episode.
Input example
Which is a member of Allied_Forces_1943?
I need to
Therefore I need to
Identify and test a strategic COG candidate corresponding to a member of the Allied_Forces_1943
This is an example of a problem solving step from which the agent will learn a general problem solving rule.
IF
Identify and test a strategic COG candidate corresponding to a member of a force
The force is ?O1
Identify and test a strategic COG candidate for a force
The force is ?O2
Plausible Upper Bound Condition
IF
Identify and test a strategic COG candidate corresponding to a member of the ?O1
Question
Answer
?O2
THEN
INFORMAL STRUCTURE OF THE RULE
FORMAL STRUCTURE OF THE RULE
This is the rule that is learned from the input example. It has both a formal structure (used for formal reasoning), and an informal structure (used to communicate more naturally with the user).
Let us consider the formal structure of the rule. This is an IF-THEN structure that specifies the condition under which the task from the IF part can be reduced to the task from the THEN part. This rule, however, is only partially learned. Indeed, instead of a single applicability condition, it has two conditions:
1) a plausible upper bound condition which is more general than the exact (but not yet known) condition, and
2) a plausible lower bound condition which is less general than the exact condition.
Completely learning the rule means learning an exact condition. However, for now we will show how the agent learns this rule from the input example shown on a previous slide. The basic steps of the learning method are those from the next side.
Overview
Example of a
Basic steps of the rule learning method
3. Generalize the example and the explanation into a plausible version space rule.
1. Formalize and learn the tasks
2. Find a formal explanation of why the example is correct.
This explanation is an approximation of the question and the answer, in the object ontology.
1. Formalize the tasks
I need to
Therefore I need to
The force is US_1943
Therefore I need to
The force is Allied_Forces_1943
Sample formalization rule:
obtain the task name by replacing each specific instance with a more general concept
for each replaced instance define a task feature of the form “The concept is instance”
Because the tasks from the modeling are in unrestricted English Disciple cannot reason with them. We need to formalize these tasks.
For each task we need to define an abstract phrase that indicates what this task is about (the task name), and a list of specific phrases that give all the details about the task (the task features). The task name should not contain any instance (such as Allied_Forces_1943). All these instances should appear in the task features. In general, the task name may be obtained from the English expression in the left hand side by simply replacing each specific object with a more abstract concept. Then we will add a corresponding task feature that specifies the value for this abstract concept.
Identify the strategic COG candidates
for the Sicily_1943 scenario
for Anglo_allies_1943
in the Sicily_1943 scenario?
Identify the strategic COG candidates for
the Anglo_allies_1943 which is
for a scenario
for an opposing force
Identify the strategic COG candidates for
an opposing force which is
a multi-member force
Any other formalization is acceptable if:
the task name does not contain any instance or constant;
each instance from the informal task appears in a feature of the formalized task.
These are some other examples of task formalizations.
Task learning
Identify and test a strategic COG candidate for ?O1
INFORMAL STRUCTURE OF THE TASK
Identify and test a strategic COG candidate for US_1943
The force is ?O1
The top part of this slide shows the English expression and the formalized expression of a specific task.
From the English expression of the specific task the agent learns the informal structure of the general task by replacing the specific instance US_1943, with the variable ?O1.
From the formalized expression of the specific task, the agent learns the formal structure of the general task. The formal structure also specifies the conditions that ?O1 should satisfy. However, the agent cannot formulate the exact condition, but only two bounds for the exact condition that will have to be learned. The plausible lower bound condition is more restrictive, allowing ?O1 to only be a single-state force. This condition is obtained by replacing US_1943 with its most specific generalization in the object ontology.
The plausible upper bound condition is less restrictive. ?O1 could be any force. This condition is obtained by replacing US_1943 with the most general sub-concept of <object> which is more general than US_1943. The plausible upper bound condition allows the agent to generate more tasks, because now ?O1 can be replaced with any instance of force. However, there is no guarantee that the generated task is a correct expression.
The agent will continue to improve the learned task, generalizing the plausible lower bound condition and specializing the plausible upper bound condition until they become identical and each object that satisfies the obtained condition leads to a correct task expression.
2. Find an explanation of why the example is correct
US_1943
has_as_member
Allied_Forces_1943
The explanation is an approximation of the question and the answer, in the object ontology.
US_1943
Which is a member of Allied_Forces_1943?
I need to
Therefore I need to
The expert has defined the example during the modeling process.
During the task formalization process, the expert and the agent have collaborated to formalize the tasks. Now the expert and the agent have to collaborate to also formalize the question and the answer. This formalization is the explanation from the bottom of this slide. It consists of a relation between two elements from the agent's ontology:
“Allied_Forces_1943 has_as_member US_1943”
It states, in Disciple’s language, that US_1943 is a member of Allied_Forces_1943.
An expert can understand such formal expressions because they actually correspond to his own explanations. However, he cannot be expected to be able to define them because he is not a knowledge engineer. For one thing, he would need to use the formal language of the agent. But this would not be enough. He would also need to know the names of the potentially many thousands of concepts and features from the agent’s ontology (such as “has_as_member”).
While defining the formal explanation of this task reduction step is beyond the individual capabilities of the expert and the agent, it is not beyond their joint capabilities. Finding such explanation pieces is a mixed-initiative process involving the expert and the agent. In essence, the agent will use analogical reasoning and help from the expert to identify and propose a set of plausible explanation pieces from which the expert will have to select the correct ones.
Once the expert is satisfied with the identified explanation pieces, the agent will generate a general rule.
Condition
IF
The force is ?O1
The force is ?O2
Notice that the explanation is first re-written as a task condition, and then two generalizations of this condition are created: a most conservative one (the plausible lower bound condition) and a most aggressive one (the plausible upper bound condition).
The plausible lower bound is the minimal generalization of the condition from the left hand side of the slide.
Similarly, the most general generalization is the plausible upper bound.
Analogical reasoning
similar example
The force is Germany_1943
Therefore I need to
The force is European_Axis_1943
Therefore I need to
The agent uses analogical reasoning to generalize the example and its explanation into a plausible version space rule. This slide provides a justification for the generalization procedure used by the agent.
Let us consider that the expert has provided to the agent the task reduction example from the bottom left of this slide.
This reduction is correct because “Allied_Forces_1943 has_as_member US_1943”.
Now let us consider the European_Axis_1943 which has as member Germany_1943. Using the same logic as above, one can create the task reduction example from the bottom right of the slide.
This is a type of analogical reasoning that the agent performs. The explanation from the left hand side of this slide explains the task reduction from the left hand side. This explanation is similar with the explanation from the right hand side of this slide (they have the same structure, being both less general than the analogy criterion from the top of this slide). Therefore one could expect that this explanation from the right hand side of the slide would explain an example that would be similar with the initial example. This example is the one from the right hand side of the slide.
To summarize: The expert provided the example from the left hand side of this slide and helped the agent to find its explanation. Using analogical reasoning the agent can perform by itself the reasoning from the bottom right hand side of the slide.
Generalization by analogy
DOMAIN(has_as_member) RANGE(The force is) =
RANGE(has_as_member) RANGE(The force is) = force force = force
Knowledge-base constraints on the generalization:
initial example
Therefore I need to
The force is ?O2
Therefore I need to
The force is ?O1
Notice that in the previous illustration we could have used any other forces ?O1 and ?O2 instead of European_Axis_1943 and Germany_1943. As long as ?O1 has as member ?O2, the agent would hypothesize that in order to identify and test a strategic COG for ?O1 one could identify and test a strategic COG for ?O2.
The agent uses various constraints from the knowledge base to restrict the values that the variables ?O1 and ?O2 could take. For instance, ?O1 should have the feature “has_as_member” and the domain of this feature (i.e. the set of objects that may have this feature) is multi_member_force. Therefore ?O1 should be a multi_member_force.
Also, ?O1 is the value of the task feature “The force is” the range of which is “force”. Therefore ?O1 should also be a force. From these two restrictions, we conclude that ?O1 should be a multi_member_force.
Using this kind of reasoning, the agent generalizes the example from the left hand side of this slide to the expression from the right hand side of this slide.
Universe of
Characterization of the learned rule
As discussed previously, the plausible upper bound condition of the learned rule is an analogy criterion that allows the agent to solve problems by analogy with the example from which the rule was learned. Because analogy is only a plausible reasoning process, some of the examples covered by the rule may be wrong. The plausible upper bound of the rule is therefore only an approximation of a hypothetical exact condition that will cover only positive examples of the rule. That is why it is called plausible upper bound.
The plausible lower bound condition of the rule covers the input example that is known to be correct. However, the bound is a minimal generalization performed in the context of an incomplete ontology (generalization hierarchy). Therefore it is also a plausible bound.
The previous slide shows the most likely relation between the plausible lower bound, the plausible upper bound and the hypothetical exact condition of the rule. Notice that there are instances of the plausible upper bound that are not instances of the hypothetical exact condition of the rule. This means that the learned rule could also generate wrong solutions to some problems, as already mentioned. Also, there are instances of the hypothetical exact condition that are not instances of the plausible upper bound. This means that the plausible upper bound does not cover all the cases in which the solution provided by the rule would be correct.
Similarly, there may be cases that are covered by the plausible lower bound, without being covered by the hypothetical exact condition. All these situations are a consequence of the fact that the explanation of the initial example might be incomplete, and that the representation language for learning (which is based on the object ontology) might also be incomplete. These results are consistent with what one would expect from an agent performing analogical reasoning.
Overview
Input example
THEN
Identify the strategic COG candidates with respect to the industrial civilization of US_1943
Who or what is a strategically
critical industrial civilization
element in US_1943?
Industrial_capacity_of_US_1943
This slide shows an example of a problem solving step from which the agent will learn a rule. The domain used for illustration is Center of Gravity analysis.
IF
Identify the strategic COG candidates with respect to the industrial civilization of a force
The force is ?O1
THEN
A strategic COG relevant factor is strategic COG candidate for a force
The force is ?O1
to the industrial civilization of ?O1
Question
civilization element in ?O1 ?
This is the rule that is learned from the previous example. It has both a formal structure (used for formal reasoning), and an informal structure (used to communicate more naturally with the user). Let us consider the formal structure of the rule. This is an IF-THEN structure that specifies the condition under which the task from the IF part can be reduced to the task from the THEN part. This rule, however, is only partially learned. Indeed, instead of a single applicability condition, it has two conditions: 1) a plausible upper bound condition which is more general than the exact (but not yet known) condition, and 2) a plausible lower bound condition which is less general than the exact condition. Completely learning the rule means learning this exact condition. However, for now we will show how the agent learns the rule in this slide from the example shown on the previous slide.
Explanation of the example
explanation:
element in US_1943?
The expert has defined the example during the modeling process. The left hand side of this slide shows how the expert reasons using task reduction. All the expressions are in natural language. The agent, however, does not reason with natural language. It needs formal logical expressions, like the ones shown in the right hand side of this slide.
During the task formalization process, the expert and the agent have collaborated to formalize the tasks. Now the expert and the agent have to collaborate to also formalize the question and the answer. This formalization is the explanation from the right hand side of this slide. It consists of various relations between certain elements from the agent's ontology:
US_1943 has_as_industrial_factor Industrial_capacity_of_US_1943
Industrial_capacity_of_US_1943 is_a_major_generator_of War_materiel_and_transports_of_US_1943
They state, in Disciple’s language, that US_1943 has as industrial factor its industrial capacity, which is a major generator of war materiel and transports.
An expert can understand these formal expressions because they actually correspond to his own explanations. However, he cannot be expected to be able to define them because he is not a knowledge engineer. For one thing, he would need to use the formal language of the agent. But this would not be enough. He would also need to know the names of the potentially many thousands of concepts and features from the agent’s ontology (such as “is_a_major_generator_of”).
While defining the formal explanation of this task reduction step is beyond the individual capabilities of the expert and the agent, it is not beyond their joint capabilities. Finding these explanation pieces is a mixed-initiative process involving the expert and the agent. In essence, the agent will use analogical reasoning and help from the expert to identify and propose a set of plausible explanation pieces from which the expert will have to select the correct ones.
Once the expert is satisfied with the identified explanation pieces, the agent will generate a general rule.
What is the form of the explanation?
The explanation is a sequence of object relationships that correspond to fragments of the object ontology.
explanation:
General heuristics for explanation generation
Look for the relationships between the objects from the question and the answer
Look for the relationships between an object from the IF task and an object from the question or the answer
War_materiel_and_transports_of_US_1943 …
IF
THEN
Who or what is a strategically
element in US_1943?
The expert selects an object from the example.
The agent generates a list of plausible explanations containing that object.
The expert selects the correct explanation(s).
Industrial_capacity_of_US_1943 IS …
The agent may need help to propose additional necessary explanations.
For instance, the previously found explanation
“US_1943 -- has_as_industrial_factor --> Industrial_capacity_of_US_1943”
does not express the fact that Industrial_capacity_of_US_1943 is a strategically critical element of US_1943. To help the agent to generate an formal explanation that expresses this fact, the expert may provide two types of hint.
One hint is to select one object from the example. In this case the object would be Industrial_capacity_of_US_1943. The agent will then generate plausible explanations containing this object, as illustrated in this slide. The expert will then need to select the correct explanation, as shown in this slide.
Another type of hint is to select two objects from the example. In this case the agent will show the relationships between these two objects. For instance, if the agent would have not generated the explanation
“US_1943 -- has_as_industrial_factor --> Industrial_capacity_of_US_1943”,
the expert could have simply selected these objects. Then the agent would have generated the above explanation.
IF
THEN
a multi-member force?
for Anglo_allies_1943
a multi-member force
The expert provides a hint: Anglo_allies_1943
The agent generates a list of plausible explanations containing that object.
The expert selects one expression that ends in “…” and clicks on EXPAND:
Anglo_allies_1943 IS …
Anglo_allies_1943 --- component_state US_1943 …
The agent generates the list of possible expansions of the expression and the experts select the explanation:
Anglo_allies_1943 IS Strategic_COG_relevant_factor
Anglo_allies_1943 IS Force
Anglo_allies_1943 IS Military_factor
Anglo_allies_1943 IS Multi_member_force
Anglo_allies_1943 IS Multi_state_force
Anglo_allies_1943 IS Multi_state_alliance
Anglo_allies_1943 IS Equal_partners_multi_state_alliance
In this example, according to the question and the answer from the upper left of the slide, the explanation should express the fact that Anglo_allies_1943 is a multi member force. The expert first gives a hint to the agent by selecting “Anglo_allies_1943”. Then the agent generates several explanations that start with Anglo_allies_1943.
In order to reduce the number of plausible explanations that the expert has to analyze, the agent may generate only the beginning of a plausible explanation, followed by “…” The expert may select such an expression and my ask the agent to expand it, as illustrated in this slide.
Indeed, the expert selected “Anglo_allies_1943 IS …”, because these are the explanations that express the fact that Anglo_allies_1943 is of a certain type.
Then the expert requests the agent to expand this explanation and the agent lists all the types of Anglo_allies_1943. After that the expert selects the correct explanation.
The next slide also illustrates the hint refinement process, this time for a different type of explanation.
It is important to stress that the hint may also be an object from a previously selected explanation, not necessarily from the initial example.
Hint refinement (another example)
has_as_political_leader
PM_Churchill
has_as_ruling_political_party
Conservative_party
Hint:
Generate:
Britain_1943 -- has_as_governing_body governing_body_of_Britain_1943 --
IF
The force is ?O1
THEN
The force is ?O1
to the industrial civilization of ?O1
Question
civilization element in ?O1 ?
Using this form of generalization based on analogy, the agent generalizes the example provided by the expert and its explanation to this plausible version space rule. Notice that the IF task and the THEN task are those from the example, where the instances have been replaced with variables. The lower bound condition of this rule requires the variable to take only the values from the example (for instance, ?O1 should be US_1943). We know that for these values of the variables the task reduction is correct (it is the task reduction indicated by the expert).
However, the plausible upper bound condition of this rule allows the variables to take any value that satisfies the analogy criterion (for instance, ?O1 could also take the value Germany_1943). The task reduction generated in this way may sometimes be correct and sometimes may be wrong, and has to be confirmed or rejected by the expert. In any case the agent will learn from the expert as will be explained in the following. Basically the lower bound will be generalized to cover the examples that are correct. Also the upper bound will be specialized to eliminate the examples that are not correct. In this way the two bounds will converge toward one another until they will become identical.
Analogical reasoning heuristic
1. Look for a rule Rk that reduce the current task T1.
2. Extract the explanations Eg from the rule Rk.
3. Look for explanations of the current task reduction that are similar with Eg.
Example to be explained:
Explanation Eg
PUB condition
PLB condition
THEN accomplish T1a,…T1d
Previously learned rule Rk:
Justification of the heuristic
This heuristic is based on the observation that the explanations of the alternative reductions of a task tend to have similar structures. The same factors are considered, but the relationships between them are different.
T1
T1a
T1b
T1e
Ab
Aa
Ae
Question:
Answers:
Explanations:
Eb
Ea
Ee
Q
Another analogical reasoning heuristic
1. Look for a rule Rk that reduce a similar task to similar subtasks.
Another heuristic is to generate explanations of a task reduction by analogy with the explanations corresponding to the reduction of a similar task.
This heuristic is based on the observation that similar problem solving episodes tend to have similar explanations:
Justification of the heuristic
Yet another analogical reasoning heuristic
The plausible explanations found by the agent can be ordered by their plausibility (based on the heuristics used).
1. Look for a rule Rk that reduces a task that is similar to the current task even if the subtasks are not similar.
The goal of using these heuristics is to have the agent propose explanations ordered by their plausibility with the expert choosing the right ones, rather than requiring the expert to define them.
No explanation necessary
candidates should I
consider for Britain_1943?
I consider strategic
Anglo_allies_1943
with respect to the governing element
of Britain_1943, a member of
Anglo_allies_1943
Sometimes no formal explanation is necessary. In this example, for instance, each time I want to identify the strategic COG candidate for a state, such as Britain, I would like to also consider the candidates with respect the governing element of this state.
We need to invoke Rule Learning, but then quit it without selecting any explanation. The agent will generalize this example to a rule.
Overview
Disciple-RKF/COG:
Agent Teaching and Rule Learning
We will now demonstrate how Disciple learns general tasks and rules from the expert’s reasoning.
First the expert and Disciple have to formalize the English statements of the tasks.
This is done in the Formalization mode.
In the formalization mode the tool shows:
The modeling
in English
The formalized
When the expert clicks on “Formalize” Disciple will propose a formalization of the task
The expert may accept it or
he may edit it
After the tasks from the current task reduction step are formalized the expert may explain this example to Disciple, which will learn a rule from it
The Rule Learning tool helps the expert to explain the example to Disciple
The tool shows the English form of the example
Disciple uses analogical reasoning and other heuristics to propose plausible explanations pieces that justify the task reduction step.
The expert selects those explanation pieces that correspond to the meaning of the question-answer pair from the task reduction example and clicks on “Accept”
The expert may direct Disciple to generate explanation pieces related to certain objects from the example
The expert may direct Disciple to generate explanation pieces related to certain objects from the example
When the expert is satisfied with the identified explanation pieces he can click on “End learning”. Disciple will then create a general rule corresponding to this example and its explanation.
During rule refinement the two conditions will converge toward one another, ultimately leading to a rule with a single condition.
Notice that it has a plausible upper bound condition and a plausible lower bound condition.
This is the general task reduction rule learned by Disciple.
This is the general task reduction rule learned by Disciple.
In addition to the formal structure of the rule, which is used in problem solving and learning, Disciple maintains also an informal structure of the rule.
The informal structure is used in the communication with the user.
After learning a rule from the current task reduction step Disciple returns to the formalization mode.
Following the same procedure, Disciple will learn another rule from the this task reduction step:
The expert selects the next task reduction step by clicking on the corresponding question-answer pair
When the expert clicks on “End learning” Disciple analyzes the accepted explanations to determine whether additional explanations are needed.
If Disciple determines that additional explanations are needed it will asks the expert to provide them
After learning a new rule from the current task reduction step Disciple returns to the formalization mode.
Following the same procedure, Disciple learned another rule from the last task reduction step:
Recommended reading
Tecuci G., Building Intelligent Agents: A Theory, Methodology, Tool and Case Studies, Academic Press, 1998.
Tecuci G., Boicu M., Marcu D., Stanescu B., Boicu C. and Comello J., Training and Using Disciple Agents: A Case Study in the Military Center of Gravity Analysis Domain, in AI Magazine, AAAI Press, Menlo Park, California, 2002. http://lalab.gmu.edu/publications/default.htm
Tecuci G., Boicu M., Bowman M., and Marcu M., with a commentary by Burke M.: An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a High Performance Knowledge Base for Course of Action Critiquing, in AI Magazine, 22, 2, 2001, pp. 43-61. AAAI Press, Menlo Park, California, 2001. http://lalab.gmu.edu/publications/default.htm Describes the course of action domain.
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planet
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