agent oriented theory of human activity
Post on 06-Jan-2016
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The general “Aim”
• Apply Agent-based modeling techniques to general activity systems theory to model human travel behavior.
• What is Activity Systems theory?– People’s travel behavior can be understood in
the context of activities they want to do.
Definitions
• Activity – Episode = Discrete event occurring over time.– Trajectory = actual behavior over time.– Pattern = Analytical description of trajectory in time
and space.– Action space = Set of actions that are feasibly
reached over space and time.– Calendars = demands to engage in activities – Programs = Agenda of activities that must be
performed– Schedules = Planned trajectory that an individual
decides.
Various theories on Activity systems analysis
• Theory 1(Constraints)– States that Human behavior is a constrained
trajectory through time and space.– Types of Constraints
• Capability constraints arising due to physical limitations
• Coupling constraints arising from interactions• Authority constraints define personal control of
resources e.g. I cannot shop at a store if it is closed
Theory2 (Motivation)
• Concentrates on propensity factors that drives humans to do stuff.
• Not articulated properly and a lot of different cases exist.– Main Idea: Human behavior in space is
characterized by the motivation to participate in various activities.
Theory 3 and 4
• Balancing “Motivation and Constraints”– Neither all activities nor all constraints are
equal in the eyes of the actor or a weighted theory.
• Adaptation– Individual is situated in an environment that
both motivates and constrains his behavior.
Idea in the thesis
• Combine the theories just described with Agent based modeling philosophy.
• Agent-based View– A Human-agent occupies a universe filled with other
agents.– Agent’s knowledge gained solely through sensors.– Effectors– Achieve GOALS by interaction with other agents.
Activity as Interaction
• The agent-based view states that the behavior of an agent depends upon the interaction it has with the other agents.
=> Activity = Interaction
• Thus, Human Activity can be viewed as both mechanism of constraint and source of motivation.
Defining the Human Agent
• Some Assumptions: Assume you can synthesize a population of agents in an urban environment by using some techniques.
• Such a technique also specifies the social structure and things like physical proximity.
• Now, we seek to produce for each agent, the following time-varying vector:– Y(t)= [XL(t),XC(t),XA(t)]’– XL, XC and XA stand for location, social impact and
interaction respectively
Representing dynamics
• Y(t)=[XL(t),XC(t),XA(t)]’ =[f(XL(t-1),XA(t-1)),f(XC(t-1),XA(t-1)),f(R(t-1),P(t-1))]
R(t): Resources available to the agent at time t.
P(t): Agent’s plan.
Specifying Resources or Interfaces
• View:– The resources available effectively define the
channels upon which an individual can interact with the environment to engage in an activity.
– Each agent therefore has an interface that it presents to other agents which represents the types of interactions it can have.
– R(t) = f(XL(t),XC(t),L(t), T(t),C(t))• L(t), T(t) and C(t) are the land-use system, the transportation
system and the socio-cultural system respectively.
So What?
• The goal of activity and travel forecasting is to predict this trajectory Y over time. (Economic models)
• The goal of transportation science is to describe and understand how human behavior produces the trajectory. (Learning problem)
• The behavior is dependent on the plan P:– P(t)=f (P(t-1), XL,XC,E(t))
• where E(t)= (L(t),T(t),C(t)) is the environment.
Specifying Agent internals
• Assume that the environment is enumerable E= (e1,e2,……).
• The Agent has only partial knowledge of the world and so it considers the environment as R = (r1,r2,r3….).– ri is a subset of E.
• Define two functions, – f: E → M (Sensory input to form messages)– f: M → R (messages encoded to develop a
perspective of the world)
Action-space and Agent’s view of the Action space
• Same as Sensory input.– Available Actions S (s1,s2,….)– Agents view: A(a1,a2….)
• ai is a subset of S.
• To summarize: E and R define the possible states of the objective world and the agent's ability to perceive that world.
• S and A define the universe of possible actions and the agent's subjective knowledge of them.
Completing the Agent description
• Interpretation.– attribute a causal sense to the perceived world
according to the agent's experience– f: H → I (Historical information to Interpretation)
• Decisions– f: I → A (Interpretation to activities)
• Assessing response for Actions through sensors.– F: E x S → E
Completing the Agent description
• Agent’s utility functions– U = Z(I,B) where U is a Real number.
• Z can be interpreted as the agent's utility function, with B defining the utility weights and I defining the perceived values of the relevant attributes.
• Pay-off functions.– f: I X A → U
• which is a mapping from the universe of possible interpretation-action combinations to some payoff measure in a range of utilities U.
Learning
• 4 Levels– Learning about the states of the world
(improving perception)• Increase or decrease states in R.
– Learning About the Opportunity Space• Increase or decrease states in A.
– Learning About Interpretations of Historical Trajectories
– Learning About the Decision Rules
Summary
• The focal agent is the human being, who is relationally situated to physical and social hierarchies that both motivate and constrain his behavior.
• This behavior is limited to interactions with other agents (people, institutions etc) from which the person derives some environmental payoff.
• Interactions can be conceived as a “negotiation process” which is the next chapter in the thesis.
Introduction
• Recap: Human Activity involves the interactive exchange of resources between individuals.
• View this as “Negotiation”
• Negotiation is driven by physical and social laws.
• Develop model according to this criteria and also try to reduce its complexity.
Design of Activity Negotiated Kernel
• Use Distributed Problem solving architecture (DPS)
• Model a urban system as a multi-agent system where agents represent people, institutions and places.
• Use an event-driven discrete model because the number of activities is not likely to exceed 50.
DPS and Contract Net Protocol (CNP)
• How to view DPS as negotiation based protocol (Davis and Smith 1983)---Ans:CNP.
• Problems in DPS– Each agent has an incomplete local
knowledge– Synchronize behavior so that agents don’t
interfere with actions of other agents.
Activity engagement as DPS
• Turn the CNP argument on its head.• Activity engagement is the process used to solve
the problem of activity completion.• Problems:
– No centralized problem solver in human activity negotiation.
• Solution:– View the task manager as an abstraction that
represents the logic representing how physical and social constraints affect the laws of the environment.
Additions to CNP for Travel Domain
• Contracts involving multiple agent
• Non-binding contracts– Terminate some activity at will.
• Binding Contracts– E.g. Travel activities using Rail
• Simultaneous Activities
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