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Action, Change and Evolution: from single agents to multi-agents

Chitta BaralProfessor, School of Computing, Informatics & DSEKey faculty, Center for Evolutionary Medicine & Inform.Arizona State UniversityTempe, AZ 85287

Action, Change and Evolution: importance to KR & R

Historical importance Applicability to various domains Various knowledge representation aspects Various kinds of reasoning

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Heracleitos/Herakleitos/Heraclitus of Ephesus (c. 500 BC) - interpreted by Plato in Cratylus

"No man ever steps in the same river twice, for it is not the same river and he is not the same man.“

Panta rei kai ouden menei

All things are in motion and nothing at rest.

Alternate interpretation of what Heraclitus said … different waters flow in rivers staying the same. In other words, though the waters are always

changing, the rivers stay the same. Indeed, it must be precisely because the waters are

always changing that there are rivers at all, rather than lakes or ponds. The message is that rivers can stay the same over time

even though, or indeed because, the waters change. The point, then, is not that everything is changing, but that the fact that some things change makes possible the continued existence of other things. 4

Free will and choosing ones destiny

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Where does that line of thought lead us?

Change is ubiquitous But one can shape the change in a desired

way Some emerging KR issues How to specify change How to specify our desires/goals regarding the

change How to construct/verify ways to control the

change 6

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“Action and Change” is encountered often in Computing as well as other fields Robots and Agents Updates to a database

Becomes more interesting when updates trigger active rules Distributed Systems Computer programs … Modeling cell behavior

Ligand coming in contact with a receptor Construction Engineering …

Various KR aspects encountered Need for non-monotonicity Probabilistic reasoning Modal logics Open and closed domains Causality Hybrid reasoning

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Various kinds of reasoning

Prediction Plan verification; control verification Narratives Counterfactuals Causal reasoning Planning; control generation Explanation Diagnosis Hypothesis generation

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Initial Key Issue: Frame Problem Motivation: How to specify transition between states

of the world due to actions? A state transition table would be too space consuming!

Assume by default that properties of the world normally do not change and specify the exceptions of what changes. How to precisely state the above? Many finer issues!

To be elaborate upon as we proceed further.

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Origin of the AI “frame” problem

Leibniz, c.1679 "everything is presumed to

remain in the state in which it is"

Newton, 1687 (Philosophiae Naturalis Principia Mathematica) An object will remain at

rest, or continue to move at a constant velocity, unless a resultant force acts on it.

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Early work in AI on action and change 1959 McCarthy (Programs with common sense), 1969 McCarthy and Hayes 1969 (Some philosophical

problems from the standpoint of AI) – origin of the “frame problem” in AI.

1971 Raphael – The frame problem in problem-solving systems (Defines the frame problem nicely)

1972 Sandewall – An approach to the frame problem 1972 Hewitt – PLANNER 1973 Hayes – The Frame problem and related problems in AI 1977 Hayes – The logic of frames 1978 Reiter – On reasoning by default

Quotes from McCarthy & Hayes 1969

In the last section of part 3, in proving that one person could get into conversation with another, we were obliged to add the hypothesis that if a person has a telephone he still has it after looking up a number in the telephone book. If we had a number of actions to be performed in sequence we would have quite a number of conditions to write down that certain actions do not change the values of certain fluents. In fact with n actions and m fluents we might have to write down mn such conditions.

We see two ways out of this difficulty. The rest is to introduce the notion of frame, like the state vector in McCarthy (1962). A number of fluents are declared as attached to the frame and the effect of an action is described by telling which fluents are changed, all others being presumed unchanged.

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In summary … Action and Change is an important topic in

KR & R Its historical basis goes back to pre Plato and

Aristotle days In AI it goes back to the founding days of AI It has a wide applicability It involves various kind of KR aspects It involves various kinds of reasoning

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Outline of the rest of the talk Highlights of some important results and turning points

in describing the world and how actions change the world (physical as well as mental)

Other aspects of action and change: here we will talk about mostly our work Specifying Goals Agent architecture Applications

A future direction Interesting issues with multiple agents

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The Yale Shooting Problem: Hanks & McDermott (AAAI 1986)

Nonmonotonic formal systems have been proposed as an extension to classical first-order logic that will capture the process of human “default reasoning” or “plausible inference” through their inference mechanisms, just as modus ponens provides a model for deductive reasoning. …

We provide axioms for a simple problem in temporal reasoning which has long been identified as a case of default reasoning, thus presumably amenable to representation in nonmonotonic logic. Upon examining the resulting nonmonotonic theories, however, we find that the inferences permitted by the logics are not those we had intended when we wrote the axioms, and in fact are much weaker. This problem is shown to be independent of the logic used; nor does it depend on any particular temporal representation.

Upon analyzing the failure we find that the nonmonotonic logics we considered are inherently incapable of representing this kind of default reasoning.

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Reiter 1991: A simple solution (sometimes) to the frame problem Combines earlier proposal by Schubert (1990) and Pednault

(1989) together with a suitable closure assumption. Intermediate point:

Poss(a,s) preR+(a,s) R(do(a,s) )

Poss(a,s) preR-(a,s) ~R(do(a,s) )

Poss(a,s)

[ R(do(a,s) ) preR+(a,s) R(s) ~ preR

-(a,s) ) ]

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Lin & Shoham 1991: Provably correct theories of actions

“… argued that a useful way to tackle the frame problem is to consider a monotonic theory with explicit frame axioms first, and then to show that a succinct and provably equivalent representation using, for example, nonmonotonic logics, captures the frame axioms concisely”

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Sandewall – Features and Fluents 1991/1994 Book ; IJCAI 1993; 1994 JLC: The range of applicability of

some non-monotonic logics for strict inertia Propose a systematic methodology to analyze a proposed theory in terms

of its selection function When

Y is a scenario description (expressed using logical formulae), (Y) is the set of intended models of Y S(Y) is the set of models of Y selected by the selection function S Validation of S means showing

S(Y) = (Y) for an interesting and sufficient large class of Y. Range of applicability is the set Z: Y Z S(Y) = (Y)

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The language A - 1992

1992. Gelfond & Lifschitz. Representing actions in extended logic programs. Journal of Logic Programming version in 1993.

Syntax Value proposition

F after A1; …; Am initially F Effect proposition

A causes F if P1, …, Pm Domain Description: a set of propositions

Semantics Entailment between Domain Descriptions & Value Propositions Entailment defined by models of domain descriptions

Models defined in terms of initial states and transition between states due to actions

Sound translation to logic programs

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Kartha 93: Soundness and Completeness of three formalizations of actions

Used A as the base language Proposed translations to Pednault’s scheme Reiter’s scheme A circumscriptive schemed based on a method by

Baker Proved the soundness and completeness of the

translations.21

1990-91-92 1990: I first learn about Frame problem from

Don Perlis 1991-92: Learn more about it from Michael

Gelfond

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Effect of actions executed in parallel: IJCAI 93; JLP 97 (with Gelfond) Initial frame problem

Succinctly specifying state transition due to an action

What if we allow actions to be executed in parallel? Do we explicitly specify

effects of each possible subsets of actions executed in parallel? Too many

Do we just add their effects? May not match reality

l_lift causes spilled r_lift causes spilled {l_lift, r_lift} causes ~spilled if

~spilled {l_lift, r_lift} causes lifted initially ~spilled, ~lifted paint causes painted

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Our Solution and similar work Inherit from subsets under normal

circumstances; and use specified exceptions when necessary.

High level language: syntax and semantics Logic programming formulation Correctness theorem

Similar work by Lin and Shoham in 1992.

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Our Solution: Excerpts from the high level language semantics Execution of an action a in a state causes a fluent literal f if

a immediately causes f (defined as: there is a proposition a causes f if p1, …, pn such that p1, …, pn hold in )

a inherits the effect f from its subsets in . (i.e. there is a b a, such that execution of b in immediately causes f and there is no c such that b c a and execution of c in immediately causes ~f.)

E+(a, f : f is a fluent and execution of a in causes f } E-(a, f : f is a fluent and execution of a in causes

~f } a, E+(a, \E-(a,

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Our Solution: Excerpts from the logic programming axiomatization Inertia

holds(F, res(A,S)) holds(F,S), not may_i_cause(A, F’,S), atomic(A), not undefined(A,S).

Translating “a causes f if p1, …, pn” may_i_cause(a,f,S) not h’(p1,S), …, not h’(pn,S). cause(a,f,S) h(p1,S), …, h(pn,S).

Effect axioms holds(F, res(A,S)) cause(A,F,S), not undefined(A,S). undefined(A,S) may_i_cause(A, F,S), may_i_cause(A, F’,S).

Inheritance axioms holds(F, res(A,S)) subset(B,A), holds(F, res(B,S)), not noninh(F,A,S), not undefined (A,S). cancels(X,Y,F,S) subset(X,Z), subseeq(Z,Y), cause(Z,F’,S). noninh(F,A,S) subseeq(U,A), may_i_cause(U, F’,S), not cancels(U,A,F’,S). undefined(A,S) noninh(F,A,S), noninh(F’,A,S).

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Effect of actions in presence of specifications relating fluents in the world Examples of “state constraints”:

dead iff ~alive. at(X) at(Y) X = Y.

Winslett 1988: ’ (a,) if ’ satisfies the direct effect (E) of an action plus state

constraints (C) and There is no other state ” that satisfies E and C and that is

closer (defined using symmetric difference) to than ’. But?

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Problems in using classical logic to express state constraints Lin’s Suitcase example (Lin - IJCAI 95)

flip1 causes up1 filp2 causes up2 State Constraint: up1 up2 open initially up1, ~up2, ~open. What happens if we do flip2?

But up1 up2 open is equivalent to ~open up2 ~up1 Marrying and moving (me - IJCAI 95)

at(X) at(Y) X = Y. married_to(X) married_to(Y) X = Y. Ramification vs. Qualification.

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Causal connection between fluents We Suggested in IJCAI 95 that a causal specification (in particular:

Marek and Truszczynski’s Revision programs) be used to specify “state constraints” out(at_B) in(at_A). out(at_A) in(at_B). in(married_to_A), in(married_to_B).

Presented a way to translate it to logic programs. Thus a logic programming solution to the frame problem in presence

of “state constraints” that can express causality and that distinguished between ramification and qualification.

We proved soundness and completeness theorems. McCain and Turner presented a conditional logic based solution in the

same IJCAI. (1995) Lin 1995: Embracing causality in specifying indirect effects of actions Thielscher 1996 Used in RCS-Advisor system developed at Texas Tech university.

Knowledge and Sensing Moore 1979, 1984

for any two possible worlds w1 and w2 such that w2 is the result of the execution of a in w1 the worlds that are compatible with what the agent knows in w2 are exactly the worlds that are the result of executing a in some world that is compatible with what the agent knows in w1

Suppose sensef is an action that the agent can perform to know if f is true or not. Then for any world represented by w1 and w2 such that w2 is the result of sensef happening in w1 the world that is compatible with what the agent knows in w2 are exactly those worlds that are the result of sensef happening in some world that is compatible with what the agent knows in w1 and in which f has the same truth value as in w2.

Scherl & Levesque 199330

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Knowledge and Sensing Effect Specifications

push_door causes open if ~locked, ~jammed push_door causes jammed if locked flip_lock causes locked if ~ locked flip_lock causes ~ locked if locked

initially ~ jammed, ~ open Goal: To make open true P1: If ~locked then push_door else flip_lock; push_door P2: sense_locked If ~locked then push_door else flip_lock; push_door

Formalizing sensing actions: a transition function based approach (with Son AIJ 2001)

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s1

s1

s1, s2, s3, s4, …s1‘, s2’, s3’, …

s1, s2, s3, s4, …sensef

Combining narratives with hypothetical reasoning: planning from the current situation

With Gelfond & Provetti JLP1997 – The language L Besides effect axioms of the type a causes f if p1, …, pn

We have occurrence and precedence facts of the form f at si

a occurs_at si

si preceeds sj

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An example rent causes has_car hit causes ~has_car drive causes at_airport if has_car drive causes ~at_home if

has_car pack causes packed if at_home

at_home at s0 ~at_airport at s0 has_car at s0 PLAN EXECUTE s0 preceeds s1 pack occurs_at s1 OBSERVE s1 preceeds s2 ~has_car at s2 Needs to make a new PLAN

from the CURRENT situation34

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From sensing and narratives to dynamic diagnosis: basic ideas (With McIlraith, Son: KR2000) Diagnosis: Reiter defined diagnosis to be a fault assignment to the various

component of the system that is consistent with (or explains) the observations; Thielscher extended it to dynamic diagnosis.

Dynamic diagnosis using L and sensing: Necessity of Diagnosis: When observation is inconsistent with the

assumption that all components were initially fine and no action that can break one of those component occurred. I.e., (SD \ SDab, OBS OK0) does not have a model

Diagnostic model M: is a model of the narrative (SD, OBS OK0) Narratives

OBS: s0 < s1 < s2 < s3

~light_on at s0 light_on at s1 ~light_on at s2 ~light_on at s3

turn_on occurs_at s0 turn_off occurs_at s1 turn_on occurs_between s2, s3

OK0: ~ab(bulb) at s0. Diagnostic plan: A conditional plan with sensing actions which when executed

gives sufficient information to reach a unique diagnosis.

Golog: JLP1997 (Levesque, Reiter, Lesperance, Lin, Scherl) A logic based language to program robots/agents Allows programs to reason about the state of the

world and consider effects of various possible course of actions before committing to a particular behavior I.e., it will unfold to an executable sequence of actions

Based on theories of action and extended version of Situation calculus

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Features of Golog Primitive actions Test actions (fluent formulas to be test in a situation) Sequence Non-deterministic choice of two actions Non-deterministic choice of action arguments Non-deterministic iteration (conditionals and while loops can

be defined using it) Procedures

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Lots of follow-up on Golog Work at Toronto Work at York Work at Aachen Etc.

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Other aspects of action description languages Non-deterministic effect of actions Probabilistic effect of actions with causal relationships;

counterfactual reasoning Defeasible specification of effects Presence of triggers Characterizing active databases Actions with durations Hybrid effects of actions Thielschers’ fluent calculus Event calculus Modular action description Learning action models …

Issues studied so far Mostly about describing how actions may

change the world

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Outline of the rest of the talk Highlights of some important results and turning points

in describing the world and how actions change the world (physical as well as mental)

Other aspects of action and change: mostly presenting our work Specifying Goals and directives Agent architecture Applications

A future direction Interesting issues with multiple agents

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Specifying goals and directives

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What are maintenance goals? Always f, also written as □ f

too strong for many kind of maintainability (eg. maintain the room clean)

Always Eventually f, also written as □ ◊ f. Weak in the sense it does not give an estimate on when f will be made true. May not be achievable in presence of continuous interference by belligerent

agents.

□ f ------------------ □ ◊k f -------------------------- □ ◊ f □ ◊3 f is a shorthand for □ ( f V O f V OO f V OOO f ) But if an external agent keeps interfering how is one

supposed to guarantee □ ◊3 f .

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Definition of k-maintainability: AAAI 00 Given

A system A = (S,A,Ф), where S is the set of system states A is the union of agent actions Aag, and environmental actions Aenv

Ф : S x A → 2 S A set of initial states S, a set of maintenance states E, parameter k, a

function exo : S → 2 Aenv about exogenous action occurrence we say that a control K k-maintains S with respect to E, if

for each state s reachable from S via K and exo, and each sequence σ = s, s1, . . . , sr (r <=k) that unfolds within k steps by executing K, we have

{s, s1, . . . , sr } ∩ E ≠ { }.

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b

cd

hf

g

a

a’

e

a

a

ae

No 3-maintainable policy for S = {b} with respect to E = {h}

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b

cd

hf

g

a

a’

e

a

a

a

3-maintainable policy for S = {b} with respect to E = {h} : Do a in b, c and d.

e

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Finding k-maintainable policies (if exists) : an overview (joint work with T. Eiter): ICAPS 04

Encoding the problem in SAT whose models, if exists, encode the k-maintainable policies.

This SAT encoding can be recasted as a Horn logic program whose least model encodes the maximal control.

(Maintainability is almost similar to Dijkstra’s self-stabilization in distributed systems.)

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Motivational goal: Try your best to reach a state where p is true.

~p, q,~r,~s

~p, ~q,~r,~s

~p, ~q,r,~s

p,s~p, q, r,~s

s1

s3

s4

s2

s5

a7

a7

a2

a5

a5a1

a6

a1a3

a4 a3

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Try your best to reach p: Policy 1

~p

~p

~p

p~p

s1

s3

s4

s2

s5

a7

a7

a2

a5

a5a1

a6

a1a3

a4 a3

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LTL, CTL* and -CTL* LTL: Next, Always, Eventually, Until For plans that are action sequences

CTL*: exists path, all paths For plans that are action sequences

-CTL*: exists path following the policy under consideration, all paths following the policy under construction. (ECAI 04) For policies (mapping states to actions)

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-CTL* not powerful enough! (AAAI 06) In 2 doing a2 in s1 is trying your

best but not in 1. How to make that distinction

while specifying our goal? -CTL* is not able to make

such a distinction. Consider the policy : where (s1) = (s2) = a2

is a “try your best” policy for 2 but not for 1.

But all -CTL* formulas have the same truth value with respect to both 2 and 1 , given s1, and

~p p

~p p

s1s2

s1s2

a1

a2

a2 a2

a2 a2

a2

1

2

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Expressing “Try your best” in P-CTL*: AAAI 06 P-CTL*: exists policy and for all policies A representation of “Try your best” in P-CTL*

A: Strong policy: all paths eventually lead to the goal state.

B: Strong cyclic policy: in all paths, in all states, there is a path that eventually leads to the goal state

C: Weak policy: exists a path that eventually leads to the goal state.

P-CTL* goal: If exists a strong policy then agent should take that Elseif exists a strong cyclic policy then agent should take that Elseif exists a weak policy then agent should take that.

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Non-monotonic goal specification: IJCAI 07, AAI08 and ongoing work Motivation

Initial goal: Please get a cup of coffee. Weakening: In case the coffee machine is broken; a cup of tea would be fine. Exception to Exception: Get a cup of tea only if the coffee machine can not

be easily fixed. Revising: If bringing tea, make sure it is hot.

Past work on non-monotonic temporal logics Fujiwara and Honiden, 1991: A nonomotonic temporal logic and its Kripke

Semantics. Saeki 1987: Non-monotonic temporal logic and its application to formal

specifications (in Japneese) Proposed a non-monotonic temporal logic in IJCAI 07 Currently working to develop a better language. Started working on natural language semantics to go from discourses in

English to a non-monotonic logical language.

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Other results related to goal specification Complexity of planning with LTL and CTL* goals:

IJCAI 01. The approach to find k-maintainable policies also

leads to novel algorithms for planning with respect to other temporal goals expressed in -CTL*: AAAI 05.

Diagnostic and repair goals (KR 00) Specifies that a unique diagnosis is reached, with certain

literals protected, certain literals restored, and certain literals fixed.

Knowledge temporal goals (IJCAI 01)

Outline of the rest of the talk Highlights of some important results and turning

points in describing the world and how actions change the world (physical as well as mental)

Other aspects of action and change: our work Specifying Goals Agent architecture Applications

A future direction Interesting issues with multiple agents

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Some of our contributions to control architectures and control execution languages

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My view of agent architecture Reactive, Deliberative and Hybrid

Fully reactive: sense-match-act cycle. Completely deliberative: sense-plan/replan-act a bit Hybrid: Reactive at low level; deliberative at high levels.

Our view of hybrid architecture (ETAI 98, Agent 98) Reactive for the most common, most critical, etc. Fully deliberative for rare cases. Between reactive and deliberative for the rest.

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Between deliberative and reactive (Condition, Reasoning program) pairs Different kinds of reasoning programs

Logic program based (Kowalski, Sadri, Pereira) Agent programming language (VS et al.) Planning using domain dependent knowledge

Temporal (Bacchus and Kabanza) Partial Order, hierarchical (HTN), SHOP? Procedural (GOLOG, Congolog) A combination of the above (ATAL99, AAAI04,ACM

TOCL06)

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Our AAAI 96 robot: 3rd in Office navigation contest

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AAAI 96 and 97 robot contests: Agents 98 AAAI 96: Robots were given a topological map and required to start from

a director’s office, find if conference room 1 was empty, if not then find if conference room 2 was empty. If either was empty then inform prof1 and prof2 and the director about a meeting in that room, otherwise inform the professors and the director that the meeting would be at the director’s office, and finally return to the director’s office. Do the above avoiding obstacles and without changing the availability status

of the conference rooms. We were third with 285 out of a total of 295 points.

AAAI 97: First place in the event “Tidy Up” of the home vacuum contest. Goal was to maintain several areas in an office environment clean.

For both we used our notion of correctness of reactive control and had proved the correctness of our control.

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Some other contributions Correctness of reactive programs (ETAI98) Automatic policy generation algorithms For maintainability goals (ICAPS 04) For specific types of goals in -CTL* (AAAI05)

Outline of the rest of the talk Highlights of some important results and turning

points in describing the world and how actions change the world (physical as well as mental)

Other aspects of action and change: our work Specifying Goals Agent architecture Applications

A future direction Interesting issues with multiple agents

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Some of our contributions to applicationsRobots; Active Databases; Workflows; Modeling cells; Question answering; CBioC

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Mobile Robots Discussed our robot in AAAI 96 and 97 contests. Took a break for a few years. A recent ONR MURI project involving Indiana

University (lead – Matthias Scheutz), Notre Dame (Kathy M. Eberhard), Stanford (Stanley Peters) and ASU (myself, Rao Kambhampati, Pat Langley and Mike McBeath) Effective Human Robot Interaction under Time Pressure

through Natural Language Dialogue and Dynamic Autonomy

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Active Databases and Workflows Formal characterization of active databases

(LIDS 96, DOOD 97, CL 00) Formalizing and reasoning about the

specification of workflows Coopis 2000

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Reasoning about cell behavior Biosignet-RR (ISMB 04, KR 04, AAAI05) Hypothetical Reasoning : side effect of drugs Planning: therapy design Explanation of observations: figuring out what is

wrong Biosignet-RRH (ECCB 05) Hypothesis generation

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Description of an NFB signaling pathway Binding of TNF- with

TNFR1 leads to TRADD binding with one or more of TRAF2, FADD, RIP.

TRADD binding with TRAF2 leads to over-expression of FLIP provided NIK is phosphorylated on the way.

TRADD binding with RIP inhibits phosphorylation of NIK.

TRADD binding with FADD in the absence of FLIP leads to cell death.

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Syntax by example bind(TNF-,TNFR1)

causes trimerized(TNFR1)

trimerized(TNFR1) triggers bind(TNFR1,TRADD)

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General syntax to represent networks e causes f if f1; …; fk

g1; … ; gk causes g h1; … ; hm n_triggers e k1; … ; kl triggers e r1; … ; rl inhibits e e is an event (also referred to as an action) and the rest

are fluents (properties of the cell) For metabolic interactions:

e converts g1; … ; gk to f1; …; fk if h1; … ; hm

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Semantics: queries and entailment Observation part of queries f at t a occurs_at t

Given the Network N and observation O Predict if a temporal expression holds. Explain a set of observations. Plan to achieve a goal.

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Prediction Given some initial

conditions and observations, to predict how the world would evolve or predict the outcome of (hypothetical) interventions.

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Prediction Binding of TNF- with

TNFR1 leads to TRADD binding with one or more of TRAF2, FADD, RIP.

TRADD binding with TRAF2 leads to over-expression of FLIP provided NIK is phosphorylated on the way.

TRADD binding with RIP inhibits phosphorylation of NIK.

TRADD binding with FADD in the absence of FLIP leads to cell death.

Initial Condition bind(TNF-α,TNF-R1)

occurs at t0 Observation

TRADD’s binding with TRAF2, FADD, RIP

Query predict eventually

apoptosis Answer: Yes!

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Explanation Given initial condition and

observations, to explain why final outcome does not match expectation.

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Explanation Binding of TNF- with

TNFR1 leads to TRADD binding with one or more of TRAF2, FADD, RIP.

TRADD binding with TRAF2 leads to over-expression of FLIP provided NIK is phosphorylated on the way.

TRADD binding with RIP inhibits phosphorylation of NIK.

TRADD binding with FADD in the absence of FLIP leads to cell death.

Initial condition: bound(TNF-,TNFR1) at

t0 Observation:

bound(TRADD, TRAF2) at t1

Query: Explain apoptosis One explanation:

Binding of TRADD with RIP

Binding of TRADD with FADD

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Other issues in reasoning about cell behavior Planning interventions Generating Hypothesis

Our observations can not be explained by our existing knowledge OR the explanations given by our existing knowledge are invalidated by experiments?

Conclusion: Our knowledge needs to be augmented or revised!

How? Can we use a reasoning system to predict some hypothesis that

one can verify through experimentation? Automate the reasoning in the minds of a biologist, especially

helpful when the background knowledge is humongous. Constructing pathways Studying drug-drug interactions

Outline of the rest of the talk Highlights of some important results and turning

points in describing the world and how actions change the world (physical as well as mental)

Other aspects of action and change: our work Specifying Goals Agent architecture Applications

A future direction Interesting issues with multiple agents

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Multi-agent action scenarios

Simple multi-agent actions Two agents need to lift a table Particular agents can do particular actions Different agents may be located in different

places – depending on where the action is occurring only the agents present there can execute the action

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Multi-agent action scenarios: Reasoning about each others’ knowledge (Muddy Children problem)

Three children playing in the mud. Common Knowledge: They can see each other’s forehead but

not their own Father says: One of you have mud in your forehead Father asks: Do you know if you have mud in your forehead? All Answer: No Father again asks: Do you know if you have mud in your

forehead? All Answer: No Father again asks: : Do you know if you have mud in your

forehead? All answer: Yes

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Muddy Children problem States are Kripke models Actions considered in the past:

Announcement actions Actions of interest: Ask and faithfully answer AAMAS talk tomorrow by co-author Greg

Gelfond.

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h

a,b,c a,b,c

a,b,cl ~l

A, B, C in a room and have no clue if the gun is loaded – this is common knowledge

On the left is a Kripke Model M

S1 and S2 are two possible real worlds

(S1, M) entails ~Ka l, ~Ka ~l, ~Kb l, ~Kb ~l, ~Kc l, ~Kc ~l, Ka ~Kb l, Ka ~Kb ~l, …

(S2, M) also entail the same …

s1 s2

h

a,b,c

a,b,ca,b,c

c

c

c

c

b

a,ba,b

a,b,c a,b,c

a,b,cl

l

l

~l

~l

~l

A peeks and finds out l; B sees A peeking; C has no clue

Ka l - A knows l ~Kb l - B does not know l ~Kb~l - B does not know ~l Kb (Ka l or Ka ~l)

B knows that A knows the value of l.

~Kc l, ~Kc ~l: C does not know the value of l.

Bc (~Ka l and ~Ka ~l) Bc Bb (~Ka l and ~Ka ~l):

C has no clue

h

a,b,c

a,b,ca,b,c

c

c

c

c

b

a,ba,b

a,b,c a,b,c

a,b,cl

l

l

~l

~l

~l

A peeks and finds out l; B sees A peeking; C has no clue

C has no clue: As far as C is concerned the old Kripke model is still the structure.

Thus we make a copy of the old Kripke model. (bottom)

B sees A peeking: So the edge labeled “a” is removed in the top part.

A and B know C has no clue: So c-edges are intrduced between the top part and bottom part and c-edges are removed in the top part.

Multi-agent scenarios: An action language

Initially: (We allow only restricted knowledge about the initial state) initially initially C initially C(Ki V Ki ~ )

Actions and effects executable a if a causes if a determines f a may_determine f a announces

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Multi-agent scenarios: An action language (cont.)

Agent roles agent observes a if agent partially_observes a if

An example peek(X) determines l X observes peek(X) Y partially_observes peek(X) if looking(Y) distract(X,Y) causes ~looking(Y) signal(X,Y) causes looking(Y) The plan: signal(a,b); distract(c); peek(a) will result in a knowing the

value of l, b knowing that a knows that value and c having no clue.

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Planning Scenarios A can do an action to distract C so that when he

peaks C has no clue. Similarly, A can do an action to make B attentive

towards what A is doing. A can even do action to confuse C In a battle field friendly agents need to Share knowledge as needed, and Work together to take steps so that foes have no clue or

confuse or misinform them towards a strategic goal.

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Conclusions

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Our Conclusions Action, Change and evolution are important issues that crop

up at times in Computer Science. They are an important domain for KR & R

Early focus on this had been on the frame problem – succinctly specifying what changes and what does not change due to actions

Over the years we have worked on that aspect as well as other important aspects such as: Goal specification Control specification and architecture Various kinds of reasoning Various applications

We are facing some interesting challenges in the multi-agent domain – past work in Dynamic epistemic logic is helping us.

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Research supported by Current support

NSF IARPA ONR

Past NSF NASA United Space Alliance ARDA/DTO

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THANK YOU(Special thanks to all the collaborators and colleagues, many of whom are here, who at different times and in different ways motivated us.)