Towards a Psychology of
Rational Agency
Paul Bello1,2, Yingrui Yang2, Selmer Bringsjord2,3 & Kostas Arkoudas2,3
Air Force Research Laboratory – Information Directorate1
Department of Cognitive Science2
Department of Computer Science3
Rensselaer Polytechnic Institute
Paul.Bello@rl.af.mil yangyri@rpi.edu brings@rpi.edu arkouk@rpi.edu
Presentation Summary
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Some Questions…
What Can AI/Computer Science Tell Us?
What Can Philosophy/Economics Tell Us?
What Can Psychology Tell Us?
Is There a Synthesis?
Humble (Yet Promising) Beginnings…
Implications and Applications…
Some Questions…
• What is an agent, and what stance should we take on
mental attitudes and constructions?
• If we admit these mental representations, what should
they look like, and how do they guide behavior of the
individual possessing them?
• What happens when agents interact, either
cooperatively, or competitively? How do the mental
representations of each individual interact
holistically?
• CAN WE MODEL ANY OF THIS????
AI and Computer Science
• Taxonomy of agents in Russell & Norvig
– Sensors, Effectors, “Processing Unit”. From reflex agents to BOID.
• Variety of models
– Bayesian models for belief, decision-theoretic models for intentions,
desires.
– Logical models
• Ranges from the exceedingly simple (propositional calculus) to the exceedingly
complicated (multi-modal logics)
• Interaction
– Mostly goal-driven planning, heuristic search, probabilistic inference
• Models?
– Of course. That’s AI’s bread and butter.
• Issues
– Not very informed from the psychological dimension. Lip service paid to
philosophy and economic theory. Usually heuristics and short-cuts.
Philosophy and Economics
• Vastly more complex definitions
– Philosophy: Intentionality, Utilitarianism, etc. Argument between PSSH
and connectionists.
– Economics: Rationality and Four Requirements
• Usually modeled using foundational mathematics
– Philosophical logic, set theory, and representations of uncertainty.
Emphasis on remaining true to philosophical roots.
• Interactions
– Utilitarian semantics, Adam Smith, Game theory
• Models?
– Yup.
• Issues
– Overly formal. Not psychologically plausible. Optimality emphasized.
Philosophers not concerned with intractability.
Psychology and Cognitive Science
• Usually left to the more “philosophically oriented.” Closest
thing is the debate between psychological paradigms.
• Cognitivism: Mental representations. MM, ML, Concept
Hierarchies, etc.
• Interactions: Behavioral Game Theory
• Models?
– A ton. Some with representation, some without. Overall, captures
some general phenomenon in human behavior/cognition/mental
processing.
• Issues
– Affect-by-Affect approach. No systematicity. Not informed by
normative theory (most of the time). Hard to capture computationally.
Synthesis
• Yes! Resoundingly so. Need patience to bridge
disciplinary gaps, and find the non-null intersection.
• Focus of effort: Deontic preference logic
– How do we reason about context-dependent situations,
including social settings?
– Bridge the gap between individual cognition and group
cognition.
• Approach: Narrow the divide between the normative
and the psychological as much as possible, then
implement the narrowed system in a computational
architecture.
General Algorithm
• Leverage RAIR lab expertise in developing machine
reasoning systems. Produce a natural deduction
system for a deontic preference logic.
• Perform experiments in the empty domain of the
psychology of philosophical logic. Focus on the
deontic “distinctions” present in the literature on
philosophical logic.
• Rework natural deduction system so it is informed by
experimental results.
Hold On a Sec…
• What’s this natural deduction stuff?
• What’s deontic logic about, and how is it
useful?
Natural Deduction
• An intuitive framework for suppositional reasoning.
– Assumptions, discharging, and conditional introduction.
– Amenable to the goal-decomposition paradigm pioneered
by Herb Simon & co. Means-Ends.
– Some more complex systems like Hyperproof become
suitable for the all important “heterogeneous” style of
reasoning that we claim is what most folks do.
• Consistent with psychological models of reasoning.
– Braine’s mental logic
– Rip’s production style mental logic
Deontic Logic (SDL)
• Logic of norms (obligations)
• Introduces a new modal operator O(p) standing for “it ought to
be the case that p”
• Separates possible states of affairs into “deontically ideal” and
non-ideal situations.
• Closed under the simple rules
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–
–
–
Modus ponens: p implies q, p, therefore q
Necessitation of obligation: p yields O(p)
Distribution of obligation: O(p implies q) gives O(p) implies O(q)
Non-contradictory obligation: it can’t be the case that O(p) and not
O(p)
– Normal propositional rules
Deontic Paradox 1
• You should not insult
someone.
• If you insult someone,
you should do it in
private.
• Insulting someone in
private logically implies
that you insult them.
• You insult someone
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•
•
•
O(i)
i  O(p)
pi
i
Dyadic Deontic Logic
• Uh oh. Lots of deontic material talks about
“sub-ideal” situations.
• SDL fails miserably on this kind of material.
What to do?
• Dyadic deontic logic: O(a|b): if b (is done),
then a ought to be (done).
• Basically: in the best non-ideal situations
where b holds, a should hold as well.
• Regular SDL is subsumed: O(a|T) = O(a)
Deontic Paradox 2
• A person should not
commit murder.
• It should be that if someone
doesn’t commit murder, he
should not be punished for
it.
• If the person commits
murder, he should be
punished for it.
• A suspect commits murder
•
•
•
•
O(m)
O(m  p)
m  O(p)
m
Deontic Paradox 3
• Usually, you should not insult someone.
• When someone harms the public interest, you
should insult him.
• How to model this???
1 & 3 Combined
• Usually you should not insult someone.
• If you insult someone, you should do it in
private.
• Insulting someone in private implies that you
insult him.
• If someone harms the public interest, then you
should insult him.
Van der Torre’s DIODE Logic
• Syntax
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–
–
–
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Standard propositional language.
A finite set of violation propositions, one per deontic statement.
A finite set of exception propositions, one per deontic statement.
A set of background facts
A set of conditional obligations.
• Semantics
– Basically insure that situations are ordered in terms of increasing
numbers of violations. Preferred situations have the least number of
violations.
– Semantics for exceptions. Normal worlds are separated from
“exceptional circumstances”
– Conditions to ensure the proper mixing of these two notions.
Solutions to Deontic Paradoxes
i p
i p
V1
i p
V1, V2
h i
normal
F = {i}
exceptional
mp
m p
V1
m p
V2
mp
V1, V2
normal
exceptional
F = {m}
i h
p
Ex1
hip
Ex1
h i
h i p
V1
Ex1
hip
V3
Ex1
h i p
V2
h i
V1
Ex1
h i
V2
h ip
V1, V2
Partial Taxonomy
• Obligations
–
–
–
–
Unconditional vs. Conditional (CTD problem)
Normal vs. Exceptional (defeasibility vs. CTD)
Prima Facie vs. conditional (overriding conditions)
….
A Sample ND Schema
i p
…
i p
i p
V1
i p
V1, V2
i p
V1
i p
V1, V2
F = {i}
i p
V1
i p
V1, V2
F = {i}
V1
i p
V1
V2
O(i)
Decisions, Decisions…
• Well, we’ve talked about reasoning, and even
about preference…
• What about decisions?
– Where’s the probability?
• Let’s have a look at some new interesting
work…
Kahneman/Tversky 1
Imagine that the U.S. is preparing for the outbreak of an unusual Asian
disease, which is expected to kill 600 people. Two alternative programs to
combat the disease have been proposed. Assume that the exact scientific
estimates of the consequences of the programs are as follows:
Program A: 200 people will be saved.
Program B: 600 people will be saved with probability 1/3 and 0 people will
be saved with probability 2/3.
Which of the two programs would you favor?
Kahneman/Tversky 2
Imagine that the U.S. is preparing for the outbreak of an unusual Asian
disease, which is expected to kill 600 people. Two alternative programs
to combat the disease have been proposed. Assume that the exact
scientific estimates of the consequences of the programs are as follows:
Program C: 400 people will die.
Program D: 0 people die with 1/3 probability and 600 people die with
2/3 probability.
Which of the two programs would you favor?
Bello/Yang 1
Bello/Yang 2
Kahneman/Tversky Results
Bello/Yang Results
?
However, it wouldn’t be unreasonable to expect that they
are analogous to the Kahneman/Tversky results, but
purely explainable without resorting to traditional
decision-theoretic devices.
Implications and Applications
• Implications: Next-Generation Logic-Based AI
– A synthesis of philosophical, psychological, and
computational dimensions for higher-order cognitive
function.
• Reasoning and Decision-Making!
– MARMML as an embodiment and a test-bed.
• Application: RASCALS for…
– Third-Generation Wargaming
– Intelligence Analysis
– Educational Technologies
Wargaming: A Cognitive Approach
Long-term
Planning
Strategic
Decisions
Knowledge
Tactics
Resource
Management
Ethical Norms
Decision
Problem
Formation
Entity Interdependence
Physics
Infrastructure
Environment
Opportunism Terrain Model
Urban Models
output
Human and
Machine Reasoning
Computational
Cognitive Modeling
High-level
Perception &
Action
Low-level
Cognitive system
Game
input
Environment
SLATE
Educational Technology