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Temporal Probabilistic Agents J. Dix, S. Kraus, VS Subrahmanian Clausthal (DE)/ Bar-Ilan(IL)/ Maryland (USA) 1.

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Presentation on theme: "Temporal Probabilistic Agents J. Dix, S. Kraus, VS Subrahmanian Clausthal (DE)/ Bar-Ilan(IL)/ Maryland (USA) 1."— Presentation transcript:

1 Temporal Probabilistic Agents J. Dix, S. Kraus, VS Subrahmanian Clausthal (DE)/ Bar-Ilan(IL)/ Maryland (USA) 1

2 2 Prof. Dr. Jürgen Dix – DESIR seminaire – Motivation  Agents must be able to leverage existing code rather than reinvent everything from scratch.  Agents must be able to reason about  State  Time  Uncertainty in the state

3 3 Prof. Dr. Jürgen Dix – DESIR seminaire – Applications  Built 4 applications that motivated the need for this theory. 2 examples:  US Navy: Finding where and when an enemy submarine will be in the future, and determining which of many friendly assets will try to attack it. Joint with BBN, Lockheed, NRL and many other partners. -Terrain DB -Sensor DB -Predictive Models -Route Planner  US Army: Finding where and when an enemy tank will be in the future, and determining which of many coalition assets will try to attack it, where, and when. Joint with BAE. -Terrain Mobility Map -Sensor DB -Optimization code -Route/flight Planner

4 4 Prof. Dr. Jürgen Dix – DESIR seminaire – Overview  Part I: A Bird’s Eye View on IMPACT  Part II: Adding time  Part III: Adding uncertainty  Part IV: Adding time and uncertainty

5 5 Prof. Dr. Jürgen Dix – DESIR seminaire – I.1 Motivations  Agents are for everyone! Agentize arbitrary Legacy Code.  Knowledge is distributed and heterogenous. Code-Call machinery  Agents act wrt a clearly articulated semantics. Agent Program

6 6 Prof. Dr. Jürgen Dix – DESIR seminaire –  Code Call: d:f( arg 1,…,arg k ).  Code Call Atom: in( X,d:f( arg 1,…,arg k )). Execute function f defined in agent d on the specified arguments. Returns a set of objects. I.2 Data Access Succeeds if X is in the set of objects returned –in(X, stock:portfolio( ‘dix’ )). Find all stocks in dix’ portfolio. –in(X, pred:dest( ‘ibm ’)). Find all pairs (T,C) s.t. the price of ibm will change by C% within the next T units of time. –stock:portfolio( ‘person’ ). –pred:dest( ‘stock’ )

7 7 Prof. Dr. Jürgen Dix – DESIR seminaire – I.3 Data Access: Code Call Conditions  A conjunction of  code call atoms and  comparison atoms.  in(X, pred:dest( S )) & X.percent > 30 & X.time < 2 weeks & in(S,stock:portfolio ( dix )).  The above says: find X,S such that:  S is a stock in the portfolio of dix,  X is a prediction that the stock S is going up by at least 30% within the next two weeks.

8 8 Prof. Dr. Jürgen Dix – DESIR seminaire – I.4 What is an Agent?  Agents are built on top of legacy or specialized software modules or systems.  They access data using API functions. Software module Implementation Interface/API

9 9 Prof. Dr. Jürgen Dix – DESIR seminaire – I.4 What is an Agent? msg box Agent state (distributed) Implementation Interface/API  There is a message box with suitable functionality.  Code manipulates data structures, the contents of which (including msgbox ) describe the agent’s state.

10 10 Prof. Dr. Jürgen Dix – DESIR seminaire – I.4 What is an Agent? Actions msg box Agent state (distributed) Implementation Interface/API  Agents have available actions to change the state.  Actions are declaratively described through add/delete lists containing code call conditions. They are implemented through software code.

11 11 Prof. Dr. Jürgen Dix – DESIR seminaire – I.4 What is an Agent? Agent Program Actions msg box Agent state (distributed) Implementation Interface/API  Agents have an “agent program” encoding the agent’s operating principles  The program consists of rules that are declarative and can be easily written or modified.

12 12 Prof. Dr. Jürgen Dix – DESIR seminaire – I.4 What is an Agent? Integrity Constraints Action Constraints Agent Program Actions msg box Agent state (distributed) Implementation Interface/API  Integrity Constraints (to preserve properties of the state)  Action constraints (to ensure actions do not interfere)

13 13 Prof. Dr. Jürgen Dix – DESIR seminaire – I.4 What is an Agent? Agent Program Actions msg box Agent state (distributed) Implementation Interface/API  Based on semantics and current state, actions are executed.  These actions change its own state and other agents states as well. Actions Integrity Constraints Action Constraints Semantics

14 14 Prof. Dr. Jürgen Dix – DESIR seminaire – I.5 Agent Program (1) Set of rules of the form Op a( arg1,…,argn ) & Op 1 a 1 ( ) & … & Opa n ( )  Op is a “deontic modality” and is either  P - permitted  O - obligatory  Do - do  F – forbidden  If code call condition is true and the deontic modalities in the rule body are true, then Op a( arg1,…,argn ) is true.

15 15 Prof. Dr. Jürgen Dix – DESIR seminaire – I.5 Agent Program (2)  Most important part of the agent.  Agent program rules must be carefully crafted to avoid inconsistencies.  IMPACT provides facilities to create such rules.  A class of programs is defined that can be efficiently implemented: Regular Agents. EXAMPLE: Do buy(S)  in(X, pred:dest( S )) & X.percent > 30 & X.time < 2 weeks F buy(S)  in(S, stock:portfolio (dix )). & Do diversify_portfolio(dix)

16 16 Prof. Dr. Jürgen Dix – DESIR seminaire – II Adding Time  Basic framework does not allow to express time.  If a prediction package expects a stock to rise K% after T units of time and K>25, then buy the stock some units between (t now + T –5) and (t now + T –2). Do buy(S) : [(t now + T –5),(t now + T –2)] <--- in(...): [t now,t now ]  Semantics: Time is 1, 2, 3,...  We introduce temporal annotations [t1,t2]. Semantics needs to be defined for all timepoints.  Semantics has to reflect the obligations and committments (into the future) made in the program.  (see Dix/Kraus/Subr.: Temporal Agent Programs, AIJ 2001)

17 17 Prof. Dr. Jürgen Dix – DESIR seminaire – III TP Adding Probabilities  Basic framework does not allow to express uncertainty.  Requires changes in the code call mechanism: Code calls return random variables.  Status atoms are true only with a certain probability.  This is modelled by probabilistic annotations.  Evaluating code call conditions is difficult: What is the probability of a conjunction of events? (see Dix/Nanni/Subr.: Probabilistic Agents, TOCL 2000)

18 18 Prof. Dr. Jürgen Dix – DESIR seminaire –  Most commercial prediction packages compute (based on observations of the market) probability intervals linked with time intervals:  If a stock is expected to go over $50 per share (with 80% probability) in the next two weeks, then we should buy it. If the probability is less than 40%, we should not. IV Adding both Time and Probabilities

19 19 Prof. Dr. Jürgen Dix – DESIR seminaire –  Define annotations carrying both temporal and probabilistic information: [strategy,tc,l,l‘,pdf]  strategy: determines probability of conjunctive events  tc: temporal constraint t now +5

20 20 Prof. Dr. Jürgen Dix – DESIR seminaire – IV.2 TP Agent Program  Set of rules of the form Op a( arg1,…,argn ) [ ] [ ] & Op 1 a 1 ( ) [ ] & …& Opa n ( ) [ ] where [ ] are temporal probabilistic annotations.  Formal definitions of what it means that a [ ] is true in a state.  SEMANTICS: Fixpoint operator associating a meaning to TP agent programs.

21 21 Prof. Dr. Jürgen Dix – DESIR seminaire – IV. 2 TP Agents (2) Do buy(X):[s, t now +5

22 22 Prof. Dr. Jürgen Dix – DESIR seminaire – Conclusions  Developed an agent programming language that allows us to build agents that  Leverage commercial software without reinventing it;  Take actions in accordance with an agent program and integrity constraints in the presence of time and uncertainty about the state of the world  Developed a declarative semantics and the concept of a feasible TPSI.  Developed fixpoint computation algorithm to find feasible TPSis when they exist.


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