Agent Oriented theory of Human Activity

Thesis: Craig Rindt (Chapter 3)

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.

Chapter 4

The Micro-simulation Kernel

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

Summary