1. Agent Oriented theory of Human Activity Thesis: Craig Rindt (Chapter 3)

2. 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.

3. 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.

4. 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

5. 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.

6. 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.

9. 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.

12. 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.

13. 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

14. 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.

15. 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.

16. 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.

17. 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)

18. 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.

19. 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

20. 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.

21. 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

22. 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.

23. Chapter 4 The Micro-simulation Kernel

24. 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.

26. 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.

27. 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.

29. 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.

31. 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

35. Summary