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CS 7180: Behavioral Modeling and Decision-‐making in AI Introduction and Overview Prof. Amy Sliva September 5, 2012
Basic course informa@on • Instructor: Prof. Amy Sliva • OfAice: 256 West Village H • OfAice hours: Wednesday before class (10:00-‐11:00am), Friday after class (1:30-‐2:30pm), other times by appointment
• Email: [email protected] • Phone: 617-‐373-‐4239
• Class Times: W,F 11:45am-‐1:25pm • Location: 155 Ryder Hall
• Turn off your cell phones during class! • If your phone rings, I get to answer it J
Communica@on • Website: • http://www.ccs.neu.edu/course/cs7180f12/ • Syllabus, lecture notes, readings, assignments, etc.
• Piazza: • https://piazza.com/northeastern/fall2012/cs7180/
• When you have questions, please ask them through Piazza • You will get answers faster (I may not get to email questions…) • Someone else might have the same question • You can also send private messages to the instructor if necessary
Prerequisites • This course assumes a basic familiarity with: • search algorithms (i.e., depth Airst, breadth Airst, heuristic, etc.) • propositional and Airst-‐order logic • probability theory • basic complexity theory
• We will quickly review these topics as needed, but will not cover them in depth
Readings • Textbook • Stuart Russell and Peter Norvig. Arti%icial Intelligence: A Modern Approach, 3rd Edition. Prentice Hall 2010. ISBN: 978-‐0-‐13-‐604259-‐4
• Website located at http://aima.cs.berkeley.edu/
• Additional required readings • Research publications • Book chapters • Available on the course website using your CCS password or on Piazza (If you need a CCS account, follow the instructions at http://howto.ccs.neu.edu/howto/accounts-‐homedirs/how-‐to-‐sign-‐up-‐for-‐a-‐ccis-‐account/)
• This course is partially a seminar—do the readings and be ready to discuss them in class
Research paper presenta@ons • Research presentations give you practice discussing and demonstrating your Aindings to peers and supervisors • 1 presentation per student • 2 or 3 students per paper—divide the material how you choose • Powerpoint not required, but recommended • Present content of the paper and a critique of the challenges, future directions, applications, etc.
• Lead a discussion and answer questions
• Sign up on Friday, Sept. 14 for a research paper to present
Assignments and Exams • Homework assignments • Problem sets to practice the material • You will have 1 to 2 weeks for each assignment • You can collaborate with other students and on Piazza, but list their names on your assignment when you turn it in
• The Ainal answers you turn in must be your own
• Midterm Exam • November 2, 2012 • Covers lecture material AND research papers
Term Project • To be done in teams of 3 (or 4) • Miniature version of the research projects you will do repeatedly throughout your career
• Schedule • Mid-‐September: Your teams should be formed by this time • October 19: Term project proposals due by midnight (11:59:59pm) • October 24: Present your proposals in class (10 minutes per group) • December 3: Term project reports due by midnight • December 5, 7: Present your reports in class (25 minutes per group)
• Start thinking NOW about who you want to work with. Some things to keep in mind when forming your teams: • Do you feel comfortable with your team? • Do their interests and abilities complement yours? • Do you think you can depend on them? • Do you think you can work well together?
Grading • Assignments: 15% • Presentations: 15% • Midterm: 30% • Term Project: 40%
• Class attendance and participation in research discussions will also be taken into account
Homework 1—Get to know your classmates
• Log in to the Piazza for CS7180 • Go to the message titled: • Homework 1—post your message here!
• Post a message telling us • Who you are… • Why you are interested in this course… • What you hope to get out of it…
• This will help you choose project teams (and help me provide a good course!)
So, what is this course all about? Behavioral Modeling and Decision-‐making in ArtiAicial Intelligence…
So, what is this course all about? Behavioral Modeling and Decision-‐making in ArtiBicial Intelligence… • Three main topics: • Agent behavior • Strategic decision-‐making • ArtiAicial intelligence approaches
Agent behavior • What is an agent?
• How can we model, understand, and forecast their behavior?
Agents can take ac@on! • Philosophy deAinition: • An entity (person or otherwise) with the capacity to act in a world
• Russell and Norvig AI deAinition: • Agent is anything that perceives its environment through sensors and acts upon the environment through actuators
• Examples: • Human agents (individuals, organizations, groups) • Robotic agents • Software agents
Agents and environments
Agent Sensors
Actuators
EnvironmentPercepts
Actions
?
Components of an intelligent agent • If we are analyzing (or designing) an agent, we need to answer a few questions: 1. What can the agent do? (range of possible actions) 2. What is the environment? 3. What does the agent know? • History of its own previous inputs and actions • Properties of the environment and world knowledge • Knowledge of its own goals, preferences, etc.
4. Can we devise an agent function? • Mathematical description of behavior • Mapping of any percept sequence to an action
Vacuum-‐cleaner world
• Percepts: location and contents, e.g., [A,Dirty] • Actions: Left, Right, Suck, NoOp
A B
Determining agent performance • Rational agent is one that does the “right” thing • Must deAine a performance measure • Costs (penalties) and rewards
• Chooses an action that maximizes expected score
• Rationality depends on 1. Success criterion deAined by performance measure 2. “Behavior” of the environment (e.g., can a clean square get dirty
again?) 3. Possible actions 4. Percept sequence
• Autonomy • Rational agents require learning to compensate for incorrect or incomplete starting knowledge
What can we learn from agent behavior? • The world is full of agents taking actions • Complex feedback between behavior and the environment • Social science studies human behavior in various contexts • Formal methods analyzes the behavior of software systems • Cybersecurity looks at software, hardware, AND human agents
• Behavioral models can help us understand how agents impact an environment and vice versa • What is the relationship between CEOs organizing a merger and movements in the stock market?
• What political or economic inputs lead to violent conAlict? • What system conAigurations lead to security breaches?
Aspects of behavioral modeling • How do we represent knowledge (actions, environment, beliefs, goals, etc.)? • Formal logic, set theoretic or state-‐space representations • Temporal representations for dynamic environments
• Can we construct a model describing the agent function? • Logic rules, statistical correlations, etc. • Approximate and probabilistic models—observational data may not tell the whole story
• Can we predict how an agent will behave using our model? • Logical inference • Probabilistic reasoning (Bayes nets, MDPs, HMMs) • Utility, rational choice, and game theory
Agent behavior • What is an agent? • Entity with capacity to perceive and act in an environment • DeAined set of possible actions, environmental factors, knowledge, goals, beliefs, etc.
• Behavior determined as a function of environment • Acts rationally and perhaps autonomously
• How can we model, understand, and forecast their behavior? • Formal representation of knowledge about actions and environment • Mathematical description of behavior—approximate agent function • Logical inference, statistical analysis, probabilistic reasoning to predict outcomes from the model
Strategic decision-‐making • How can we think strategically?
• What is the best choice in a given situation?
• How can we make “good” decisions without all the facts?
Strategic decisions depend on agent behavior • Military deAinition of strategy • Coordination and general direction of operations to meet overall objectives
• Game theory deAinition of strategic move • Commitment to reduce one’s options given the anticipated response of the other player
• “We may wish to control or inAluence the behavior of others…and we want, therefore, to know how the variables that are subject to our control can affect their behavior. …[T]he ability of one participant to gain his ends is dependent to an important degree on the choices or decisions that the other participant will make.” —Thomas Schelling, The Strategy of Con5lict, 1960.
• Need behavioral models to make optimal decisions
Ra@onality is making the “right” decision • DeAine a metric for success or a goal • Win this soccer match • Invest 100K for a 30% return • Reduce the number of violent crimes by 20-‐40%
• Balance potential costs • Financial, resources, physical, etc.
• Rational decisions based on preferences • Given the situation, which combination of outcomes and costs is the most preferable
• Different agents will have different ranking and preferences
AI approaches to decision-‐making • Automated Planning • Start state (S), goal (G), and set of possible actions • Actions have deAined effects—we know the resulting state • Given the start state, what sequence of decisions will achieve our goal
• Game theory • Possible behaviors with associated costs and payoffs • Choose highest payoff and lowest cost, given other players’ actions • Nash equilibrium—all players have chosen the best strategy, given the decisions of the other players • May not be optimal payoff for a single player, but no one could improve by unilaterally making a different decision
Decision-‐making under uncertainty • Decision-‐making is not so bad when it is deterministic • Use one of the previous methods to get the “best” result • Real-‐world agents are almost NEVER deterministic…
• Gets more complicated if we need to plan an entire sequence of actions • Playing chess and need to look several moves ahead • Need a long term economic strategy • Address a sequence of network attack elements
• What if we do not know what the effects of our actions will be?
• What if the environment can change over time?
• What if our behavioral model is incomplete or approximate?
Nondeterminis@c, stochas@c, and par@ally observable domains • Planning under uncertainty • Sequential decisions in a nondeterministic environment • Actions have probabilistic effects • Markov decision process—stochastic model for identifying optimal plan
• Partially observable domains • Uncertainty about the current state • POMDPs
• Utility theory • Uses preferences to make rational decisions under uncertainty • DeAine a utility function mapping each state to value of desirability • Choose an action that maximizes the expected utility
Strategic decision-‐making • How can we think strategically? • Use behavioral models to understand external agents and respond to, prevent, or induce behaviors
• What is the best choice in a given situation? • Representation of actions and effects • DeAinition of success and achievement of goals • Consideration of potential cost or risk
• How can we make “good” decisions without all the facts? • Choose most “probable” strategy towards the goal—maximize expected success
• Estimate possible future behavior of other agents
Ar@ficial intelligence approaches • What is artiBicial intelligence?
• Why use AI for behavioral modeling and strategic decision-‐making?
What is ar@ficial intelligence? • ArtiAicial systems with humanlike ability to think, understand, and reason (cf. cognitive science)
• Solve problems too large to Aind the best answer algorithmically • Heuristic (incomplete) methods
• Solve problems that are not well-‐understood
• All of these deBinitions are relevant to behavioral modeling and decision-‐making • Techniques learned in this class will be of the second two, but the we may be modeling autonomous AI agents like the Airst
Heuris@cs in behavior and decision-‐making • Heuristic is an inexact way of solving a problem • Uses context, domain knowledge, or experience to solve more quickly • Finds approximate solution when exact methods fail to Aind one
• Tradeoff between accuracy and efBiciency
• Expert knowledge can be crucial in behavioral modeling • Fill in gaps in missing data • Make principled simplifying assumptions—solve an easier problem
• Behavioral data is inherently large, complex, and noisy • Requires approximate solutions
Complexity of real-‐world behavioral data • Human behavior and interactions involve hundreds or thousands of possible actions, even in a simple model • Counterterrorism model—41 actions (i.e., kidnap, suicide bomb, etc.) • 241 (about 1012) behavior combinations for a group! • Actions take arguments denoting intensity from 0-‐7 • 241×8 = 2328 possible behaviors
• Does not account for location, etc. • If we look at only 100 locations in a country we have 232,800 ≈ 109,900 possible behaviors!
Decision-‐support systems • Decision-‐support systems (DSS) are computational tools that facilitate human decision-‐making • Humans are in the loop, but most of the analysis is done computationally
• Often used in business management, clinical diagnostic, or military contexts
• Users can address decision-‐making in complex, dynamic environments that would otherwise be impossible • Improves the efAiciency of decisions through automation • Presents novel combinations of actions or decisions
• DSS using AI modeling are called intelligent decision-‐support systems (IDSS)
Decision-‐support architecture
Database Model Interface
• Database of inputs including specialized domain knowledge • Model that analyzes and transforms data into decisions based on user criteria • Interface inputting data and analyzing computed decisions • User ultimately makes the decision
IDSS for strategic analysis • Strategic decisions often involve a human component • Full automation is impossible or undesirable
• Decision domain is too complex to address by humans alone • Use AI behavioral analysis and decision-‐making as the modeling component of DSS
• Components of an IDSS for behavioral decision-‐making • Large-‐scale behavioral database • Raw observations of agent behavior and behavioral models
• Decision-‐making engine • Dynamic model or simulation utilizing probabilistic models, planning, utility theory, or game theoretic decision criteria for choosing a decision
• Interface allowing users to simulate various outcomes, compare possible options, and understand agent behavior
Ar@ficial intelligence approaches • What is artiBicial intelligence? • Model of human cognition and reasoning • Solves problems too large to Aind precise answers using heuristics • Solves problems that are not well-‐understood by humans
• Why use AI for behavioral modeling and strategic decision-‐making? • Manage analytic complexity (i.e., scale, heterogeneity, and dynamic relationships) in behavioral data
• Approximate behavior in problems—social phenomena, large-‐scale software security, Ainancial markets—that are not well-‐understood
• Provide decision-‐support so human users can address these problems
Decision-‐making AI in ac@on! • Time to talk about the Davenport paper…
• Checkered past of automated decision/decision-‐support • Relied very heavily on expert knowledge (i.e., expert systems) • Extremely complex to use and not part of the usual workBlow • Mistrusted by decision-‐makers—can my job really be reduced to that set of numbers?!
• Making a comeback! • Systems are easier to create, manage, and use • Modeling and decision-‐making more automated in the workAlow • EfBicient application of decisions
• Humans are still in the loop
Automated decision-‐making in business • Common in business sectors with highly structured data • Banking, insurance, travel, transportation • Emerging in health care, utilities, and agriculture
• What decisions to automate? • Made frequently and rapidly using electronically available data • Applying rules and standards consistently
• What not to automate • Decisions that are made rarely or based on “fuzzy” criteria, such as personal opinion
Augmenta@on rather than automa@on • Even if automation is possible, it may not be desirable • Ethical, legal, Ainancial issues • Medical diagnosis and prescription • Foreign policy or security decisions (we’ll get to this next…)
• Use decision-‐support systems to augment decision-‐making
• Combination of AI and human judgment leads to better decisions • Prescription error declined by 55% when physicians used DSS • Banks tailor credit card offers based on DSS recommendations
Challenges of implemen@ng decision automa@on • Managers need to deAine appropriate limits • When do we rely on automation? When DSS?
• Automation as part of a larger decision-‐making process • How do we make decisions if the system is down? • What if there isn’t enough data? • Even automation is part of a larger human organization
• Using automated decision systems still requires expertise • Expert domain knowledge required for constructing models • Analysis of performance and maintenance • Decision automation is interdisciplinary
Computa@onal Social Science Domain • Many strategic decision-‐making problems are interdisciplinary • Medical diagnostics, social science, biology, public policy, security, etc.
• Proliferation of modeling and decision-‐making in social science • Emerging interdisciplinary Aield of computational social science • Leverage data that was previously unavailable • Expand AI techniques to complex realms involving human behavior
• Let’s look at the McNamara & Trucano reading…
Integra@ng DSS into applied social science • Computational social science perfect candidate for IDSS • Model agent—human individuals or groups—behavior so we can make better decisions about the world
• Not just observational, but potential for real-‐world impact • National security, economics, medicine, social policies, pandemics, etc.
• Challenge: how to integrate behavioral models and DSS into real-‐world, high-‐stakes decision-‐making? • Models don’t forecast, people forecast • Models produce some output (behavior) given an input (environment, third party behavior)
• Human judgment determines whether it is predictive, i.e., applicable to the unknown future
Going from the research lab to the real world
• Real world is very different from a research context • Decision-‐makers faced with ethical, legal, economic issues
• Common research issues ampliAied by the reality of consequences • How can we validate and verify the correctness of our models? • Can the models be used for prediction and decision support? • What HCI problems must be addressed?
Valida@ng and verifying a behavioral model • Two types of evaluation • VeriBication—is my model internally correct given the data? • Validation—does my model represent the external world?
• Model evaluation is a challenge in all sciences • Even harder in social science because unable to conduct a controlled study! • What is the ground truth for validation? • Competing theories, but no way to test and discard hypotheses • DifAiculty of reliably reproducing outcomes
• Users unlikely to be modeling experts, and expect an accurate model for their decision-‐making
Forecas@ng using behavioral models • Forecasting/decision-‐making is a process, not a technology • Formulate a problem • Collect data • Build a model • Evaluate the model • Use the model for planning and decision-‐making • Audit the process to ensure its proper application
• Interpreting complex ideas like uncertainty is a challenge • True in weather forecasts, but even more difAicult in social science • 40% chance a government will collapse—what does that REALLY mean?
• Robust process necessary to overcome inaccuracies and uncertainty
The right user interface is crucial for DSS • Who is the user? • Statistics and modeling expert, or subject-‐matter analyst?
• What types of tools will they learn, trust, and utilize in the decision-‐making workBlow? • Even non-‐experts require transparency of the models • Incorporate end user in development of the models and DSS system
• Responsibility of researchers to design accurate models so decision-‐makers can use them appropriately
