A Sound and F ast Goal Recognizer
Neal Lesh and O r e n E t z i o n i* Department of Computer Science and Engineering University of Washington, Seattle, WA 98195
neal@https://www.doczj.com/doc/7017060867.html,, etzioni@https://www.doczj.com/doc/7017060867.html, (206) 616-1849 F AX: (206) 543-2969
A b s t r a c t
T h e bulk of previous work on goal and plan
recognition may be crudely stereotyped in one
of t w o ways. "N e a t" theories — rigorous, jus-
tified, but not yet practical. "Scruffy" systems
— heuristic, domain specific, but practical. In
contrast, we describe a goal recognition m o d-
ule that is provably sound and polynomial-time
and t h a t performs well in a real domain. Our
goal recognizer observes actions executed by a
h u m a n, and repeatedly prunes inconsistent ac-
tions and goals f r o m a graph representation of
the d o m a i n. We report on experiments on hu-
man subjects in the U n i x domain t h a t demon-
strate our a l g o r i t h m to be fast in practice. The
average t i m e to process an observed action w i t h
an i n i t i a l set of 249 goal schemas and 22 action
schemas was 1.4 cpu seconds on a SPARC-10.
1 I n t r o d u c t i o n and m o t i v a t i o n
Plan recognition (e.g. [Kautz, 1987; Pollack, 1990]) is the task of identifying an actor's plan and goal given a partial view of t h a t actor's behavior. We have focused on identifying the actor's goal. There are several poten-tial applications for an effective goal recognizer. Goal recognition is useful for enhancing intelligent user i n-terfaces [G o o d m a n and L i t m a n, 1992]. urthermore, a goal recognizer would allow an autonomous agent to pro-vide useful services to the people it interacts w i t h, such as completing their current tasks and offering advice on how to better achieve future goals. T h i s paper describes a goal recognition module. We do not consider how to integrate a goal recognizer into an agent's architecture.
In our scheme, the system observes the actor executing a sequence of actions. T h e actor does not necessarily know she is being observed (this is keyhole recognition). T h e system attempts to identify the actor's goal as early "Many thanks to Denise Draper, Terrance Goan, Robert Goldman, Steve Hanks, Henry Kautz, Nick Kushmerick, Di-ane Litman, Mike Perkowitz, Rich Segal, Tony Weida, and Dan Weld for comments and discussion. Special thanks to Keith Golden and Mike Williamson for their ongoing sup-ply of critique and coffee. This research was funded in part by Office of Naval Research grant 92-J-1946 and by National Science F oundation grant IRI-9357772. as possible. W h a t does it mean to identify the actor's goal? To a t t r i b u t e a goal to an actor is to predict t h a t the actor's actions, b o t h observed and unobserved, w i l l be the execution of one of the plans for t h a t goal. The actor's goal may be a conjunction of various goals such as "T a k i n g out the trash and fixing the car".
T h e following scenario illustrates the sort of conclu-sions we want our goal recognizer to produce. T h e ob-served actor is a computer user, entering commands into a U n i x shell. Suppose we observe:
>cd /p a p e r s
There are many plausible goals at this point. T h e actor might be searching for a particular file. Or perhaps she wants to know how much memory is used by the /p a p e r s directory. B u t her goal is not to find a free printer. Nor is she reading her m a i l. W h y bother to change directo-ries for these goals? Changing directories is irrelevant to these goals and we assume the actor does not execute irrelevant commands. If we allowed a r b i t r a r i l y many ir-relevant actions then we could not predict the actor's goal because all the observed actions m i g h t be unrelated to her goal. Suppose that we next observe:
>l s
p a p e r.t e x p a p e r.p s
T h e second line is the o u t p u t f r o m I s. T h e goals we previously rejected are still rejected. Let's reconsider the goal of determining the memory usage of /p a p e r s. T h e o p t i m a l approach is to execute du instead of I s. If actors acted optimally, we could reject this goal. B u t actors do not always act optimally. T h i s actor may next execute I s -1 o n p a p e r.t e x and o n p a p e r.p s and add the memory usages of the files together. T h u s, Is may be part of a suboptimal plan to determine the memory usage of /p a p e r s. Now we observe:
>g r e p m o t i v a t i n g p a p e r.t e x
We now reject the memory usage goal. We also reject the goal of searching for a file named p a p e r.t e x because the g r e p command does not contribute to i t. T h e actor might, however, be looking for a file t h a t contains "m o-t i v a t i n g" or perhaps for a file t h a t contains "m o t i v a t i n g" and is named p a p e r.t e x.
In the following sections we articulate the definitions, assumptions, and a l g o r i t h m we use to express, j u s t i f y, and produce these conclusions. A l t h o u g h we validate our system in U n i x, our a l g o r i t h m and f o r m a l results are domain independent.
1704 PLANNING
2 Overview of the paper
O u r objective is to b u i l d a goal recognizer that performs well in large, real domains. We need a way to quickly determine if a goal is inconsistent w i t h the observed ac-tions. Informally, a goal is inconsistent w i t h the observed actions if the actor could not possibly have executed the actions as part of a plan to satisfy the goal. To determine consistency, we must reason about all plans for each can-didate goal. To reason tractably, we borrow techniques for constructing and m a n i p u l a t i n g graph representations of planning problems, originally developed to produce search control for generative planners [Etzioni, 1991; S m i t h and Peot, 1993]. By analyzing interactions among the actions, action schemas, and goal schemas in our con-sistency graphs (defined below), we detect cases where no valid plan exists for a goal.
Most plan recognition algorithms run in exponential t i m e and have not been shown to perform well on large problems. In contrast:
? Our a l g o r i t h m is sound (never rejects a goal G un-less our assumptions entail that G is not the ac-
tor's goal) and runs in polynomial t i m e in the size of the i n p u t goal recognition problem. T h i s input is
smaller t h a n the corresponding i n p u t to most plan recognizers.
? Our i m p l e m e n t a t i o n is fast. We have tested our sys-t e m on data collected f r o m human subjects in the U n i x d o m a i n. T h e average time to process an ob-
served action w i t h an i n i t i a l set of 249 goal schemas
and 22 action schemas was 1.4 cpu seconds.1
In our f o r m u l a t i o n, goal recognition is semi-decidable. It follows t h a t our p o l y n o m i a l-t i m e algorithm is incom-plete, i.e. it is not guaranteed to reject every inconsistent goal. In our experiments, however, the a l g o r i t h m rejects most inconsistent goals.
T h i s paper is organized as follows. Section 3 defines our terms, the i n p u t and o u t p u t of a goal recognizer, and states our assumptions. Section 4 introduces consistency graphs, describes our a l g o r i t h m, and works through an illustrative example. Section 5 describes our empirical validation. F inally, sections 6 and 7 discuss related work, the l i m i t a t i o n s of our system, and future work.
3 Problem formulation
We begin w i t h the i n f o r m a l story. The actor constructs and then executes a plan to solve her current goal. A l-though plans may contain conditionals, the observable behavior t h a t results f r o m the execution of any plan is a sequence of actions. T h e system observes a prefix of this action sequence. A plan is consistent w i t h the observed actions A iff t h a t plan has an execution w i t h prefix A. A goal is consistent w i t h A iff there exists a consistent plan for t h a t goal. A key question is w h a t constitutes a plan for a goal? We assume the actor constructs plans w i t h-out any irrelevant actions. We further assume the actor constructs plan P for goal G only if P might achieve G :T h e data we have collected is publicly available. Send mail to neal@https://www.doczj.com/doc/7017060867.html, for details. given the actor's beliefs about the world. T h i s assump-
tion suggests the system has access to the actor's beliefs.
The system is given, as i n p u t, an arbitrary subset of the actor's beliefs. T h e more of the actor's beliefs the system
is given, the more goals it can prove inconsistent.
3.1 P l a n n i n g l a n g u a g e
Our formulation is general enough to accommodate many planning languages. We use U W L [Etzioni et ai, 1992], an extension of the S T R I P S language, because it
can express information-gathering goals and actions w i t h sensory effects. T h i s is necessary to distinguish between Unix commands such as pwd and cd. In U W L, states
are sets of literals. A literalis a possibly negated atomic formula. T h e conjunction of literals in a state describes
all relevant relationships in that world state. Models and goals are also sets of literals. T h e conjunction of
the literals in a model or goal is a partial description
of a state. A goal schema is a set of literals which can contain variables. A schema can be instantiated by re-placing variables w i t h constants. F or example, the goal schema for searching for a file w i t h some name ?n (where
?n is a variable) can be instantiated to f o r m the goal of searching for a file named p a p e r. t e x. An action schema consists of a name, a precondition set, and an effects set.
An action is an instance of an action schema, w i t h a unique id number. The CD action schema, for example,
can be instantiated into many different actions, such as
cd /p a p e r s or cd b i n. M u l t i p l e executions of the same action are distinguished by their id numbers.
Informally, plans are programs composed of nested conditionals and actions.
F
or brevity, we w i l l not de-
fine plans but instead make use of a function, Executor, which acts as an interpreter for plans. Executor is a many-to-one mapping that maps a plan and state to the action sequence t h a t results f r o m executing the plan in
the state. We often refer to an action being in a p l a n, or
one action coming before another. A c t i o n A i is in plan
P iff there exists a state S such that Executor(P ,S) —* [..,Ai,..]. If Ai and Aj are b o t h actions in plan P, then
A1 is before A3 iff in every execution of P in which A j appears, A1 appears prior to A j.
3.2 C o n s i s t e n c y
A plan is consistent w i t h the observed actions if some execution of the plan might produce those observations.
D e f i n i t i o n 1 Plan P is consistent with sequence of ac-
tions A iff there exists state S such that Executor maps
P and S to a sequence of actions of which A is a prefix.
Consistency of a goal is defined relative to a set of action schema A and a model M. A goal is consistent
w i t h the observed actions only if there exists a consistent plan, b u i l t out of A, for that goal. A plan for a goal must potentially-achieve the goal, given model M, and contain
no irrelevant actions. We now define these terms.
D e f i n i t i o n 2 Plan P potentially-achieves goal G given model M iff there exists a state S such that M C S and
G is satisfied by executing P in S.
If P potentially-achieves G given M then P also potentially-achieves G given any subset of M.T h i s
LESH AND ETZIONI 1705
? A goal recognizer takes a goal recognition problem
II as i n p u t and returns a set of goal schemas G'.
? T h e i n p u t II = (A,M,A,G) where
— A is a sequence of actions. A is assumed to be
a prefix of the actions executed by the actor.
— M is a set of beliefs. M is assumed to be a subset of the actor's beliefs.
— A is a set of action schemas. A is assumed to be a superset of the action schemas the actor
plans w i t h.
— G is a set of goal schemas. The actor's goal is assumed to be an instance of an element in G.
? T h e o u t p u t Q' is a subset of G such that for every goal schema in G Q' there exists an instance of t h a t
goal schema G,- and a plan P such that:
— P could achieve goal G, given the actor's
beliefs (constrained by M).
— P contains no irrelevant actions.
— P is composed of actions f r o m A.
— Some execution of P is a sequence of
actions w i t h prefix A.
? M u l t i p l e Goals: T h e actor's goal may be the con-j u n c t i o n of various goals, such as "T a k i n g out the
trash and fixing the car and w r i t i n g a paper"
Figure 1: Input/output specification for a goal recognizer. makes it easy to treat the beliefs M in the i n p u t II as a
subset of the actor's beliefs.
W h a t does it mean for a plan to contain no irrelevant actions? We require t h a t every action in the plan support some action in the plan or the goal.2 We define support between actions as follows (the definition of an action supporting a goal is very similar).
D e f i n i t i o n 3 Let A i and Aj be actions in plan P. A x supports Aj iff A i is before Aj, A i has an effect that uni-fies with a precondition p of Aj, and no action between A i and Aj in P has an effect which negates p.
Support is blocked only by an action that negates the supporting effect. One upshot is that we allow redundant sensory actions in the actor's plans. In other words, we do not assume that the actor remembers everything she learns f r o m executing sensory actions. We can now define consistency for goals.
D e f i n i t i o n 4 Goal G is consistent with action sequence A, given model M and action schemas A, iff there ex-ists plan P such that (1) P is consistent with A, (2) every action in P is an instance of a schema in A, (3) P potentially-achieves G given model M, and (4) every action in P supports an action in P or supports G.
A goal schema is consistent if any instance of that goal schema is consistent.
2This is a much weaker requirement than that the plan be minimal in order to contain no irrelevant actions. 3.3 I/O o f a g o a l r e c o g n i z e r
A goal recognition problem II is a four-tuple {A, M, A, G) where A is an action sequence (the observations), M is
a model (a subset of the actor's beliefs), A is a set of action schemas (the actor's plan is composed of actions
in this set), and G is a set of goal schemas, A goal rec-ognizer takes a goal recognition problem and returns G'
C G. Ideally, G' is the set of goal schemas in G t h a t are consistent w i t h A, given M and A. F igure 1 summarizes the i n p u t/o u t p u t specification for a goal recognizer.
We view goal recognition as the process of discarding goal schemas f r o m the i n p u t set G and returning the re-maining, unrejected goals. A sound recognizer never dis-cards a consistent goal. A complete recognizer discards every inconsistent goal.
When the recognizer returns G'', this means "the ac-
tor's goal is an instance of a schema in G*.n If the recog-nizer is sound, this conclusion is justified by the following assumption.
A s s u m p t i o n 1 The actor's goal is consistent, given model M. and action schemas A, with the (full) action sequence the actor executes, of which A is a prefix. Fur-ther, the actor's goal is an instance of a schema in G. Determining consistency is exponential in the length of the longest plan (t h a t doesn't go through the same state twice). In an unbounded domain, consistency is semi-decidable. In the next section, we describe a sound, polynomial-time, but incomplete goal recognizer.
4 Our goal recognizer
We now present our goal recognizer, i.e an a l g o r i t h m which takes a goal recognition problem II and returns
a set of goal schemas. T h i s goal recognizer is provably sound and runs in polynomial t i m e. In this section, we present our theorems and provide intuitions for why they are true. The full proofs are in [Lesh and E t z i o n i, 1995].
In section 5, we validate our a l g o r i t h m using data gath-ered in the Unix domain.
Our goal recognizer constructs and manipulates a sin-
gle consistency graph T based on the i n p u t I I. A con-sistency graph is a directed graph in which the nodes are actions, action schemas, and goal schemas. F igure 2 shows a simple consistency graph.
Informally, the consistency graph represents the plans the actor might be executing. T h e actions in T represent the observed part of the actor's plan. T h e action schemas
in T represent possible unobserved actions. T h e goal schemas in T represent the goals the actor m i g h t have. The edges in T indicate when one action can support another action or goal. A consistency graph is correct if all consistent plans are represented by the graph.
D e f i n i t i o n 5 A consistency graph(V,￡) is correct, relative to the input goal recognition problem II = {A, M, A,G), iff the following three properties hold:
(P I) V contains every consistent goal schema in G,
(P2) V contains action schema A E A if an instance of X is in any consistent plan for a goal in G, and
(P3) S contains the edge (Vi—*Vj) for every Vi,Vj G V where V i (or an instance of V i) supports Vj (or an in-stance ofV j) in some consistent plan for a goal in G.
1706 PLANNING
LESH AND ETZIONI 1707
strength of this assumption, we can reject a goal if no plan exists which could achieve i t , given the actor's be-liefs. A simple case where this arises is when a conjunct of some goal is false in the model M and not supportable by any effect of any schema in
T h e o r e m 6 (Impossible Conjunct)
I F G i s insupportable and false i n M THEN - G is legal.
T h e Obsolete Rule leverages our assumption that ev-ery action supports, directly or indirectly, the goal. If some action schema is not path-connected to any goal in then no instance of can support any goal in
Since is correct, it contains every consistent goal. Thus
cannot be in any consistent plan for a consistent goal.
The following a l g o r i t h m is a sound goal recognizer t h a t runs in p o l y n o m i a l t i m e in the size of I I.
F u n c t i o n 2 recognize(II) ::: Apply the rules to quies-cence on i n i t i a l i z e (I I ). Return all goal schemas in the resulting graph.
T h e o r e m 8 Recognize is sound and polynomial-time in the size of XI.
T h e initialize function produces a correct graph, and since we only apply legal rules, the final graph is correct as well. A correct graph contains every consistent goal schema. Thus our a l g o r i t h m returns every consistent goal schema and therefore is sound.
T h e i n i t i a l graph contains \G U Au A\'2 elements. In every iteration, we potentially apply every rule to every element. Each rule can be applied to an element in linear time in the size of the graph. Our a l g o r i t h m halts as soon as applying all the rules fails to remove anything. Thus, the m a x i m u m number of iterations is the number of elements in the i n i t i a l graph. An upper bound for the worst case r u n n i n g t i m e is thus k x \Q U A U A|6 where k is the number of rules. T h i s is a loose upper bound, intended only to show t h a t our a l g o r i t h m is polynomial. Our actual implementation is optimized for our cur-rent rule set. We have analyzed the dependency relation-ships among our rules, and fire only a subset of the rules in each iteration. A d d i t i o n a l l y , we often apply many rules in a single procedure. We test, for example, the connectedness between an observed action and all goals w i t h one procedure rather than a call for each goal. T h i s is roughly times faster than checking every goal sepa-rately. Furthermore, although the above a l g o r i t h m pro-cesses all observed actions at once, our actual system is incremental. We fold new actions into the processed graph rather t h a n re-initialize the graph every time a new action is observed. W h e n we observe a new action A n , we add to fully connect A n to and f r o m every node and then apply our rules to this new graph.
4.1 E x a m p l e t r a c e
Now we present a sample trace. T h e i n p u t to the recog-nizer is II = (A ,M , A, 0) where A = [cd /p a p e r s , g r e p
m o t i v a t i n g p a p e r.t e x ], contains representations o f C D , LS, G R E P , and L P Q , is empty (we know none of the actor's beliefs), G contains G\ = "F i n d a file named paper.tex", G 2 = "F i n d a file named paper.tex t h a t con-tains m o t i v a t i n g ", and G 3 = "F i n d a free printer."
T h e initialize function produces a graph similar to the one in Figure 2 except t h a t it is f u l l y connected.
In the first iteration, the M a t c h i n g Rule removes 61 edges, resulting in the graph shown in Figure 2. T h e
Order Rule removes edge p a p e r s ). The Prefix Rule removes e d g e s 'p a p e r s ), (LS /p a p e r s ), and ep). In the second iteration, Goal Connection removes G 1, G 3, and edges and because they point to removed goals.
In the t h i r d iteration, the Obsolete Rule removes L P Q. In the fourth iteration, no rules fire. Set is re-turned.
5 Experimental validation
In this section, we describe how we collected our data, how we converted it into goal recognition problems, and how our system performed on these problems.
We gather the raw data as follows. A f t e r reading some instructions, the subjects (students in our department) are given English goal descriptions. T h e subjects try to solve each goal by executing Unix commands. For each task, the subjects indicate success or failure. We p r o h i b i t subjects f r o m using certain constructs and commands such as pipes and the command awk.
We converted this raw data into goal recognition prob-lems. To generate the observed actions A, we matched each observed U n i x command to an instance of our ac-t i o n schema for t h a t c o m m a n d. We filtered o u t com-mands we considered to be typos. We let A be our ac-tion schemas for 22 U n i x commands, including all those used by our subjects. We let indicating t h a t we know n o t h i n g about the actor's i n i t i a l beliefs. T h i s is the most conservative setting. T h e goal space consisted of 249 goal schemas: 129 file-search goal schemas (goals of locating a file t h a t has some qualities), 15 assorted (non file-search) goals, and the 105 pairs of these 15 goals. We evaluated our goal recognizer on the following goals:
(G 1) Find a file named "core".
(G 2) F i n d a file t h a t contains "m o t i v a t i n g " and
whose name ends in ".t e x ".
(G 3) F i n d a machine t h a t has low load; and
determine if Oren Etzioni is logged into the ma-chine named chum.
(G 4) Compress all large 10,000 bytes) files in the
Testgrounds subdirectory tree. Goals G\ and G 2 are two of the 129 file-search goals. Goal G3 is one of the 105 pairs of the assorted goals. This goal demonstrates our ability to handle interleaved plans for multiple goals. Goal G 4 is one of the 15 assorted goals. Table 1 summarizes our results. An update is when one observed action is processed. T h e length of the plan is the number of actions the subjects executed to achieve the given goal. T h e remaining goals are the goals still in the graph after the last update.
1708 PLANNING
Our goal recognizer performed very well on these data. T h e average t i m e to process an observed action was
1.4 cpu seconds on a S P A R C 10 by code w r i t t e n in lisp.
A l t h o u g h the goal recognizer is incomplete in general, it detected every inconsistency between the goals and observations in our experiments. Thus, our a l g o r i t h m solved the goal recognition problem, as we have formu-lated it, very thoroughly and very quickly.
B u t did it work? D i d our mechanism recognize peo-ple's goals? In some sense, goal recognition occurs when the recognizer returns a single, consistent goal. T h i s
rarely occurred in our experiments.
F or example, for
goal G1, over half the goals remain. T h e subjects only executed cd and Is until they found c o r e. These com-mands b o t h support almost all file related goals. Thus, almost all file related goals are consistent w i t h the entire plans executed to solve goal G\.
A common assumption in many plan recognition paradigms is t h a t the actor's actions w i l l eventually serve to distinguish a single plan (or goal). Our investigations suggest this is unlikely to be true in the Unix domain, even w i t h relatively small sets of possible goals. We view our goal recognizer, which quickly prunes out the incon-sistent goals, as a useful module. The next step might be to assign probabilities to the remaining goals. In [Lesh and E t z i o n i, 1995] we propose a very different solution based on version spaces [Mitchell, 1982]. We view goals as hypotheses. W h e n a single, strongest consistent goal-hypothesis exists, we know t h a t achieving this goal w i l l benefit the actor. Due to space limitations, we describe a special case of this approach, subset convergence,t h a t works well on these problems.
We define subset convergence to occur when all the goals in the space share a common (non-empty) subset. Subset convergence is useful because an agent m i g h t be able to make use of the fact t h a t some goal G' is part of the actor's goal. T h e last column in Table 1 indicates when subset convergence occurred for each goal (except goal G, for which subset convergence did not occur).
Consider goal G2.A l l but 37 goals are rejected when a g r e p is observed. T h e remaining 37 goals all involve searching for a file t h a t contains some word and, possi-bly, has some other characteristics (e.g. name ends in ".t e x"). T h e goal of looking for a file t h a t contains some w o r d is a subset of all 37 of these goals. F or G2, subset convergence occurred, on average, after four actions were observed, which was an average of 12 actions before the subjects completed the task.
For goal G3, the subject's first command always i n d i-cated t h a t part of their goal either was to find a machine
w i t h a low load, or to determine if Oren was logged into chum. Thus, subset convergence occurred immediately.
By the second or t h i r d command, the goal space con-verged to goal G3.
For goal G4, the 15 unrejected goals are G4 itself and
the 14 pairs of the assorted goals that include G4.N o t h-
ing the subjects did to achieve G4 was contrary to (or inconsistent w i t h) the goal of compressing all large files and, for example, finding a free printer. A g a i n, subset convergence detects, early o n, t h a t all 15 goals include
the goal of compressing the large files.
6 Related work
Most plan recognizers (e.g. Kautz's) require, as i n p u t, a plan or event hierarchy which consists of top-level goals,
p r i m i t i v e actions, and composite or complex actions. Our input, however, differs significantly. Essentially, we take only the goals (input G) and p r i m i t i v e actions (in-
put A). Our definition of w h a t constitutes a valid plan
for a goal replaces the complex actions. Under our for-
m u l a t i o n, the goal recognizer must consider how the low level actions can be composed i n t o plans. E l i m i n a t i n g the complex actions is significant in t h a t there may be
up to 2'A I complex actions in the hierarchy. A l t h o u g h our input is more compact, it is less expressive; we do not allow arbitrary constraints to be placed between steps
in plans. We do, however, allow a r b i t r a r i l y long plans which an acyclic plan hierarchy does not.
A rarely duplicated feature of Kautz's theory and sys-
tem is the ability to recognize concurrent, interleaved plans. Kautz assumes t h a t actors execute the m i n i m u m number of consistent plans. We can proceed similarly if
we assume t h a t the concurrent execution of plans P1...P n
for goals G\...G n is always the execution of some single plan P for G\f\ ... A G n-T h e technique to recognize interleaved goal-solving is to first run our a l g o r i t h m, as
n o r m a l, on the given goal space G. If all goals are re-jected then run recognition on all pairs of goals, G2 . If this space collapses, run recognition on G3.A n d so on.
T h i s approach is not polynomial or sound because we ap-proximate consistency, if we allow arbitrary numbers of plans (or goals) to be interleaved. We believe, however,
t h a t people rarely interleave large numbers of plans. If we assume that actors only pursue at most, say, 3 or 4 goals simultaneously then our technique, which can ter-minate at G6 or G4, becomes p o l y n o m i a l and sound.
There has been some work (e.g. [Pollack, 1990]) on recognizing invalid plans. We allow some invalid plans, because we allow all plans which could achieve the goal based on the actor's incomplete model of the w o r l d. Some of these plans w i l l not achieve the goal when exe-cuted f r o m the actual world state. We cannot recognize plans b u i l t out of incorrect models of the action schemas,
as Pollack's system can. Her system does not consider
all allowable plans, as ours does, but instead searches for
a good explanatory p l a n.
There is some work on t r y i n g to select the best or most probable plan or combination of plans (e.g. [Charniak and G o l d m a n, 1991]). O u r work complements this re-
LESH AND ETZIONI 1709
search; our recognizer can produce the consistent goals which can then be subjected to more expensive prob-abilistic analysis. [Weida and L i t m a n , 1992] extend
t e r m subsumption to include plan recognition, motivated by the need to organize large numbers of plans, much like our desire to handle large domains. [Vilain, 1990] describes a p o l y n o m i a l -t i m e plan recognizer based on g r a m m a t i c a l parsing. His system is sound and complete on restricted classes of plan hierarchies unlike ours which approximates consistency relationships based on an ex-ponentially large plan space. [Bauer et al, 1993]'s def-i n i t i o n of when a plan can be refined to include actions resembles our definition of a plan being consistent w i t h actions, but their computational approach is quite dif-ferent f r o m ours. 7 Critique and future work
Our a l g o r i t h m requires p o l y n o m i a l time in size of the in-put. A more sophisticated a l g o r i t h m , described in [Lesh and E t z i o n i , 1995], runs in t i m e linear in the number of i n p u t goals (though still polynomial in |A|). B u t reason-able goal spaces may be exponentially large in relevant features of the d o m a i n , such as the number of predi-cates. Our solution is based on version spaces [Mitchell, 1982]. We view goals as hypotheses and explicitly com-pute only the strongest consistent hypotheses and the weakest consistent hypotheses. These two boundaries compactly represent the set of all consistent goals. In [Lesh and E t z i o n i , 1995], we identify a class of goals such that we can determine the consistency of 2n goals by ex-plicitly c o m p u t i n g consistency on only n goals.
Currently, we strongly leverage our assumption that every action in the actor's plan supports another action or the goal. On this basis we reject the goal of find-ing a free printer if we observe cd /p a p e r s. However, if we completely model our domain, then most actions can contribute to most goals. If the actor is searching for a file t h a t contains printer names then cd /p a p e r s can (indirectly) support finding a free printer. T h e problem is not t h a t we fail to recognize this obscure plan but that we fail because we don't model the world well. Eventu-ally, we w i l l need a stronger constraint than our current one t h a t every action support another action or the goal. On the other hand, our approach is sensitive to noisy or spurious actions. We assume every observed action is part of a goal-directed plan. T h i s may not adequately capture the role of certain actions such as returning to one's home directory or mopping one's brow. We are currently exploring the possibility of learning which ac-tions are regularly spurious by observing the actor over a long period of t i m e. These actions could then be filtered out f r o m the observations.
We have not yet addressed several additional issues. Recall t h a t the i n p u t observations are actions in our for-m a l action language. B u t how do we automatically pro-duce, for example, an instance of the CD action schema f r o m the observable string cd /p a p e r s ? W h a t if the actor executes a command which fails? F
urthermore, how do we know when the actor finishes one task and begins another? We believe that our goal consistency framework and experimental apparatus puts us in good
position to address these issues.
A l t h o u g h our a l g o r i t h m and f o r m a l results are domain-independent, this does not guarantee t h a t our goal recognizer w i l l be effective in every d o m a i n. Our case study in U n i x indicates t h a t our goal recognizer per-forms well there. We believe these results suggest t h a t our approach w i l l also work well in various software do-mains. More generally, our approach is particularly well suited to two classes of goals. F
irst, conjunctive search goals, such as the goal of looking for a file t h a t is large, that has not been touched for a m o n t h , etc. Second, conjunctive set goats, such as compressing all files t h a t are large, that have not been touched for a m o n t h , etc. Plans for both classes of goals can be very long, and thus task completion w i l l be especially useful.
References
[Bauer e t al, 1993] M. Bauer, S. Biundo, D. Dengler, J. Kohler, and Paul G. Phi-a logic-based tool for intelli-gent help systems. In Proceedings of the 13th International Joint Conference on Arttfictal Intelligence. 1993. [Charniak and Goldman, 1991] E, Charniak and R. Gold-man. A probabilistic model of plan recognition. In Proc. 9th Nat. Conf. on A.I., volume 1, pages 160-5, July 1991. [Etzioni et al., 1992] O. Etzioni, S. Hanks, D. Weld, D. Draper, N. Lesh, and M. Williamson. An Approach to Planning with Incomplete Information. In Proc. 3rd Int. Conf. on Principles of Knowledge Representation and Reasoning, October 1992. [Etzioni, 199l] Oren Etzioni. STATIC: A problem-space compiler for prodigy. In Proc. 9th Nat. Conf. on A.I., 1991. [Goodman and Litman, 1992] B. Goodman and D. Litman. On the interaction between plan recognition and intelligent interfaces. In User Modeling and User Adapted Interaction, volume 2, no. 1-2, pages 83-115, 1992-[Kautz, 1987] H. Kautz. A Formal Theory Of Plan Recogni-tion. PhD thesis, University of Rochester, 1987. [Lesh and Etzioni, 1995] N. Lesh and O. Etzioni. A sound, fast, and empirically-tested goal recognizer based on ver-sion spaces. Technical report, Draft. University of Wash-ington, 1995. [Mitchell, 1982] T. Mitchell. Generalization as search. Arti-ficial Intelligence, 18:203-226, March 1982. [Pollack, 1990] M. Pollack. Plans as Complex Mental Atti-tudes, pages 77-101. M I T Press, Cambridge, M A , 1990. [Smith and Peot, 1993] D. Smith and M. Peot. Postponing threats in partial-order planning. In Proc. 11th Nat. Conf. on A.I., pages 500-506, June 1993. [Vilain, 1990] M. Vilain. Getting serious about parsing plans: a grammatical analysis of plan recognition. In Proc. 8th Nat. Conf. on A.I., pages 190-197, 1990. [Weida and Litman, 1992] R. Weida and D. Litman. Ter-minological Reasoning with Constraint Networks and an Application to Plan Recognition. In Proc. 3rd Int. Conf. on Principles of Knowledge Representation and Reasoning, October 1992.
1710 PLANNING
帮妈妈做家务500字作文五篇 妈妈平时为家操劳，我们可以帮妈妈做点家务减轻她的负担。下面是橙子为您带来的是帮妈妈做家务感受作文，希望对您有所帮助。 篇一：帮妈妈做家务今天妈妈下班非常早，下午下课了她把我接上后对我说：“贞贞，今天妈妈回家给你做好吃的。”我迫不及待的问妈妈：“什么好吃的呀？”“回家你就知道了，但是，你必须先把作业做完以后才能吃，否则不许吃饭，好吗？” 我为了吃上好吃的，到家以后我就赶快把作业拿出来，做了起来。妈妈就在厨房里忙起来了。 等我把作业做完以后，到厨房一看，妈妈正在和面，她看到我以后对我说：贞贞快来帮忙。然后就把厨房里的围裙拿了出来，给我围上了。“做什么？妈妈。”今天妈妈给你做豆沙包吃，好不好？我一听，高兴地蹦了起来，因为我最爱吃豆沙包了。你把这个大盆端到客厅去，用筷子把盆里的陷搅一搅，一定要把陷搅匀呀！知道了吗。 经过我和妈妈的努力不一会儿就把豆沙包包好了，然后我们就把包好的豆沙包放进了微波炉里，定好时间。哎呀！就等香喷喷的豆沙包出炉了。这时候再看我们俩个人，手上、身上、脸上及头上全都是白面粉，简直就成了一个小雪人了。我和妈妈都笑了。 时间过得好慢啊！等啊等啊，终于香喷喷的豆沙包就要出炉了。妈妈打开微波炉，把豆沙包拿了出来，可是一看，我们做的豆沙包和
市场上买的不一样呀！因为市场买的是圆形的，可是我们做的却是扁扁的，这是怎么一回事儿呢？经过我们认认真真的分析以后，觉得问题应该出在面粉上，因为我们和面时放水太多了，面软了，在烤制的过程中就变成扁扁的了。虽然形状不好看，但是吃起来还是蛮好吃的吗！毕竟这是我们的劳动成果呀！ 希望小朋友们都要帮妈妈多做点家务，给妈妈减轻点负担。这样我们才是一个好孩子。 篇二：帮妈妈做家务今天，妈妈在家大扫除，我看着妈妈一会整理房间，一会擦桌子，忙得上气不接下气，我见妈妈每天都这样忙忙碌碌的，得多吃亏损呀！于是，我产生了一个念头，如果我能帮妈妈分担一些家务活，妈妈就会不这么累了吗？ 于是，我二话不说，卷起袖子，拿起抹布和洗洁精，就开始擦玻璃了。我先将抹布打湿，拧干，再将洗洁精洒在玻璃上，又用湿毛巾将污点全部擦干净，最后，再用干毛巾将洗洁精擦干净，这样，一块洁净的玻璃就出来了。 接着，我拿出扫把，准备帮妈妈妇地。我将我平时乱丢乱放的玩具和零食，放回该放的位置后，我就用扫把仔仔细细、认认真真打扫一遍后，把垃圾扫入灰斗中，再倒入垃圾桶里，再将拉圾倒入垃圾堆中后，扫地任务也完成了。 我再拿来拖把，将地板拖得干干净净、一尘不染。 拖完地后，我拿几双脏兮兮的鞋子，开始洗鞋，我先将鞋子打湿，再将鞋子洒上泡沫，然后我拿出刷子来，将鞋子的里里外外、上上下
A: Hi, Wu, what's up B: I am reading, see it's a good book. A: Well, I see. "历史深处的忧虑林达著" what's the book about B: It's about the different culture and life view between Chinese and American. A: Oh, cool, I know a lot about that have been working along with some American guys a long time. B: So tell me how A: First in China, if you get good benefit from your boss, you should not show you up, people will jealous and even do you sometimes. But in America, you should not worry about it, everybody will thumb up to you if you get good benefit by your hard work. It's more easy and open to deal with American, but it's so hard to deal with some Chinese guys. B: Hmmm, sometimes.... A: Second in China, if you are working hard, you will be jealous in most times. They will talk about your in the back, and maybe doubt what you want. It's seldom happening in America or other Western country. B: Well, it's.... A: Okay, Ancient China is the most beautiful and powerful country in the world, I think even today the Western will not get that culture in mind. (ring) Sorry, I get a call, see you later. B: See you.. A: oh! My God! Fancy meeting you here.A: 哦，天啊！太巧了，在这儿碰到你。B: Yes, what a surprise! We haven't seen each other almost half a year.B: 是呀！真没想到。我们已有半年没见了吧。A: Almost. Where are you heading nowA: 差不多。你去哪儿B: Oh. I'm going to attend a meeting about making Chinese culture various and globalized.B: 哦，我正要参加一个关于使中国文化多样化和全球化的会议。A: It's so significant. The east and west culture is a comparatively separate value system of their own society, and each style of culture has advantages and dregs. So the two cultures should nourish and benefit each other.A: 这会议很有意义。东西方文化是适应它们各自社会的相对独立的价值体系，每一种文化都有它们自己的优点和不足，因此，两
小蜜蜂的作文100字 导读：篇一：小蜜蜂 一个阳光灿烂的早晨，小蜜蜂高高兴兴去采蜜。 他想：今天肯定花开得特别多特别美。没想到，刚刚飞过一个荷花池，突然，乌云密布，不一会儿，电闪雷鸣，雷声隆隆，大雨哗哗。小蜜蜂想：来不及回家，我得赶快找个避雨的地方。于是他马上躲在一片荷叶下。这把伞真不错。小蜜蜂一点儿都没有淋到雨。还好不久雨就停了。小蜜蜂大声说：“谢谢太阳公公!” 这个早晨他采的蜜比往日还多。 篇二：一只小蜜蜂 上语文课的.时候，一只小蜜蜂从窗外飞了进来，我想那只小蜜蜂可能是迷了路。蜜蜂在倪瑜跃的头上飞来飞去，好像把倪瑜跃像小花一样，他来采蜜了。 倪瑜跃和蜜蜂玩起了猫抓老鼠的游戏，张老师和同学们看到都笑了。 篇三：快乐的小蜜蜂 周末两天，我每天中午洗碗，擦桌子。 洗碗不难洗，先把碗用水冲一下，然后用海绵擦上洗洁精，最后用清水将泡沫冲干净，把碗翻过来抖一抖，放入盆里晾干。擦桌子也很方便，先用毛巾将桌子上的垃圾收集起来扔到垃圾桶里，冲一冲毛巾，再擦一遍桌子，将毛巾洗干净就可以了。 我好快乐啊，可以帮帮妈妈做家务。
篇四：一只小蜜蜂 上语文课的时候，嗡嗡嗡一只小蜜蜂从窗外飞了进来。只见那只小蜜蜂在倪瑜跃的头上飞来飞去，好像把倪瑜跃当成了小花一样。 倪瑜跃吓坏了，一个劲儿东躲西藏，和蜜蜂玩起了躲猫猫的游戏。他的动作引起了大家的注意。老师和同学们看到都笑了。 篇五：有趣的小蜜蜂 上课了，我们突然发现了一只小蜜蜂飞进了教室。李老师大喊一声：“看!”大家转头一瞧，发现了一只小蜜蜂在教室的窗上爬，不知它在干什么，可真有趣。我仔细地观察，发现它着急地趴在窗户上：“bzzzbzzz”的叫，我想，小蜜蜂在说：“我要飞回大自然，但我爬不出去，谁来帮帮我!”它爬了半天还是没有爬出去。最后，李老师说：“它一定能靠自己的本领爬出去的。” 不一会儿，小蜜蜂不见了，它终于爬出去了，飞回了大自然的怀抱中。 多么有趣的小蜜蜂啊! 有关小蜜蜂的作文100字 1.有关小蜜蜂的作文500字 2.有关写小蜜蜂的作文 3.有关小蜜蜂的作文 4.和小蜜蜂有关的作文 5.《小蜜蜂》作文 6.小蜜蜂的作文
大学英语口语课教案 【篇一：大学英语口语课程教案】 大学英语口语课程教案 学院：外国语学院 学期： 2008-2009学年第一学期 学时 18 教材《大学英语基础口语教程第一册》 （let’s talk book 1） 授课教师大学英语口语精品课程组 授课对象 2008级普本 上课地点南、北校区、学院路校区 上课时间周一至周五 授课题目（teaching title）： unit 12 foreign customs 授课类型(class type)： lecturing and practice 授课课时安排（teaching periods）： 2 periods 本授课单元教学目标(teaching objectives)： in this unit students are required to: 1.master the basic vocabulary related to western wedding.
2.learn to describe the different manners between chinese and western customs. 3.learn the proper way to behave in western society. 本授课单元教学重点(main points of teaching)： 1.instruct students to speak and behave properly in western society concerning some important events. 2.analyze the different manners between chinese and western countries. 本授课单元教学难点(difficult points of teaching)： instruct the students to use cultural-related words to describe and compare the proper manners in chinese and western events.教学手段(teaching aids) multimedia (audios, videos, pictures, texts), blackboard. 本授课单元教学步骤及时间分配 (teaching procedures and time allotment)： 1. warming-up activities (about 15 minutes) 1.1 role play: a typical western wedding (about 10 minutes) student are assigned the task in the previous class. they are asked to perform a typical western wedding. useful materials and information should be collected before the class. (about 10 minutes)
Study English (1.1) A: Oh, hey, you look so worried, what's wrong? B: Um... Well, in fact, it's about my English. A: English? Is there something wrong with your English? B: Yes.Although I am always working hard in my English. I can't get a good mark in the test. A: That's why you are so upset? B: I think so. A: Well, guy, could you listen to my opinions? B: Of course. A: First, you should be absorbed in the class and summarize the main point what the teacher says... B:Yes,I do,But sometimes I can't keep up with teacher, A: Hey, listen to me.You don't need to write down all the things.When you are listening,just write down the long point and only the important parts. B:OK, and what? A:Secondly, you must be abiding and as soon as you meet a problem, go ahead to have the teacher to solve.Only if you do this, you can make it. B: Oh, I will take it from now on and thank you very much. A: That's all right. Likes and dislikes (1.3) A: Do you have any hobbies?What are they? B: I'm interested in reading or other relaxing sports. A: How do you spend your spare time? B: I usually read some books or do so some sports. A: What kind of book you are interested in? B: My favorite books are those of detectives. A: Well, those books are really good.I like them too.Do you think you are introverted or extroverted? B: In fact, I wouldn't call myself extroverted. Sometime I enjoy being by myself very much. But other times, I like sharing activities with others too, especially during these past few years. A: What kind of sports do you like? B: I like almost all sports, and I enjoy both playing and watching.I specially like tennis and mountain climbing. A: What kind of personality do you think you have? B: Well, I approach things very enthusiastically. I reckon, and I don't like to leave anything half-done. It makes me nervous—I can't concentrate on anything else until the first thing is finished. A:Really?Maybe I should learn from you. Oh, I remember I have something to do after a while, so I must say goodbye to you. B:OK, see you next time.
大学英语口语课教案
大学英语口语课教案 【篇一：大学英语口语课程教案】 大学英语口语课程教案 学院：外国语学院 学期：2008-2009学年第一学期 学时18 教材《大学英语基础口语教程第一册》 （let’s talk book 1） 授课教师大学英语口语精品课程组 授课对象2008级普本 上课地点南、北校区、学院路校区 上课时间周一至周五 授课题目（teaching title）： unit 12 foreign customs 授课类型(class type)： lecturing and practice 授课课时安排（teaching periods）： 2 periods 本授课单元教学目标(teaching objectives)： in this unit students are required to:
1.master the basic vocabulary related to western wedding. 2.learn to describe the different manners between chinese and western customs. 3.learn the proper way to behave in western society. 本授课单元教学重点(main points of teaching)： 1.instruct students to speak and behave properly in western society concerning some important events. 2.analyze the different manners between chinese and western countries. 本授课单元教学难点(difficult points of teaching)： instruct the students to use cultural-related words to describe and compare the proper manners in chinese and western events.教学手段(teaching aids) multimedia (audios, videos, pictures, texts), blackboard. 本授课单元教学步骤及时间分配(teaching procedures and time allotment)： 1. warming-up activities (about 15 minutes) 1.1 role play: a typical western wedding (about 10 minutes) student are assigned the task in the previous class. they are asked to perform a typical western wedding. useful materials and information
口语对话 1 赶时髦(go after fashion) A: Fashion show is around the corner, I’m so excited! 时装表演即将来临，我很兴奋！ B: Are there any good!这有什么好的！ A: I didn't see anything wrong with the clothes; they looked pretty nice to me. I think you don’t like it! Why?我没看出衣服有任何问题；在我看来它们都很不错。我觉得你不喜欢！为什么？ B: It was dumb. I think it's stupid for women to wear clothes like that. 这是愚蠢的。我认为女人们穿成那样是很愚蠢的。 A: The benevolent see benevolence and the wise see wisdom. 仁者见仁，智者见智。B: Do you really think people can wear that stuff and walk around the streets? 你真的认为人们可以穿那种东西走在街上？ A: Yes, I do. At least, some people certainly can. They wear high-fashion clothes to show off their sense of style and wealth. 是的，我这样认为。至少，有人一定会。他们穿着时尚的衣服展示他们的时尚感和财富。 B: Well. I still think they're dumb. It makes more sense to spend the money on more practical purposes.我仍然认为他们是愚蠢的。把更多的钱花在更有意义的地方比较实际。 A: So you think it's bad if I wear it?所以你认为我穿成这样很不好吗？ B: If you wear it I must speak nice! 如果你穿成这样我一定说它很好看！ A: I know you will say that.我就知道你会这样说。 B: Only you know me!知我者非你莫属！
大学英语口语课程介绍 课程背景： 为进一步推动我国大学英语教学，适应国家改革开放的需要，使大学生更加重视英语口语学习，获得更强的英语口语交际能力。 课程概况： 通过该课程的学习，使学生通过形式多样的语言技能训练，能就日常生活中常用的话题用比较简单的英语准确地表达自己的思想。以达到在具体的社会语境中得体地使用语言的目的。并通过法律环境下的语言教学来提高用英语从事涉外业务的水平，内容包括公共道德、人际关系、生活习惯、校园生活、旅游观光、环保购物、留学求职等。另根据学生的专业、兴趣和未来职业需求，增加外交礼仪，运用英语处理涉外警务活动等内容。 课程性质与任务： 本课程为一门重要的必修的基础技能课程，主要通过课堂口语实践，充分唤起学生学习英语的兴趣。使学生通过吸收丰富的预料，快速提高听说能力，深入了解各国文化和社会知识，训练学生用英语进行思考，有效培养他们的英语交际能力。 教师要根据我校应用型人才培养目标，对课程进行认真研究、深入挖掘。结合课程特点、教学内容和当前形势及现实生活，对学生进行人生观、世界观、价值观和思想道德品质教育，努力使学生在获得知识的同时学会做人的道理。 课程的目的与基本要求： 本课程的教学目的是通过大量的口语练习和实践，逐步培养和提高学生用英语进行口头交际的能力。同时帮助学生了解主要英语国家的文化和生活习俗。通过本课程的学习，学生应能就日常生活中的一般情景进行恰当的交谈，能就社会生活中的一般性话题进行连贯的发言，能比较准确地表达自己的思想，做到语音准确、语调自然、语法基本正确，语言运用基本得体。将语言技能、语言知识、文化意识等要素有机地结合起来，相互促进、循序渐进，帮助学生最终实现交际的目的。 本课程要求学生能够运用已掌握的词汇、句型和常用表达法，结合个人实际，较为流畅地就一些常见话题发表个人见解。大多数学生应能通过本门课程的学习
我会做家务500字作文：3篇 我会做家务500字作文篇一：以前喝的豆浆都是妈妈在市场买的，不知道豆浆是怎样做出来的，也没有想过要学做豆浆。但是今天，我学会了，喝起来比市场买的甜多了，香多了，心里很有成就感。 早上的时候，婶婶问我会不会打豆浆?想不想学打豆浆?诗诗姐姐会打豆浆，如果我想学的话，让诗诗姐姐教我，我连忙说好。 终于到了打豆浆的时候了，我兴奋地把诗诗姐姐叫来，让她教我打豆浆。准备的工具有：装豆浆的大杯子、黄豆、一双筷子、白沙糖，最后还有一样很重要的东西豆浆机。 首先，黄豆用水清干净，装进装豆浆的大杯子里，之后把豆浆机清理干净，把黄豆装进豆浆机里，盖上盖子，插上电源，大约20人分钟就可以了。就在等待的时候，把一勺白糖倒进装豆浆的杯子里，20分钟过去了，豆浆弄好了，把热腾腾的豆浆装进大杯子里搅拌均匀，一杯香甜的豆浆就出来了。 自己亲手做的豆浆果然很好喝，我学会了自己做豆浆，真开心! 我会做家务500字作文篇二：在寒假期间，我帮外婆做了一件家务事。 我在外婆家度寒假。有一天，外公外婆不在家。我做完作业，想起了在街上听到的大人们的议论声：“现在的孩子吃的好，穿的
好，都不爱劳动，个个像小公主、小王子一样!”我可不想做小公主，我也要学着帮外婆做些事。我想：“我能帮外婆做点什么事呢?”我开始四处寻找，忽然，目光落到了玻璃窗上，只见玻璃上有许多灰尘。于是，我决定擦玻璃。 首先，我准备好抹布、旧报纸，再用脸盆盛满水。一切准备就绪，我就开始擦玻璃。 我看着窗户上的灰尘，心想：这些灰尘，用干抹布、湿抹布擦都不行，用什么呢?我忽然看见了阳台上浇花的喷壶，何不先用喷壶喷喷水呢?说干就干，我双手紧握着喷壶，用力的喷向玻璃，玻璃上落满了水雾。一会儿，灰尘都湿透了，我就把抹布浸湿，拧干，在玻璃上一上一下按顺序擦。湿抹布擦完以后，灰尘虽然没有了，可玻璃上却留下了一道道水迹。我赶紧拿来报纸，全面认真地干擦一遍。干擦完之后，水迹就完全消失了。 玻璃终于被我擦得干干净净，看着自己的劳动成果，心里特别高兴。外婆回来后，看见屋里的玻璃，都被擦得干干净净，也表扬了我。 家务劳动虽是一件小事，却能够体现我们敬老爱老的思想。今后，我要做出更多敬老爱老的实际行动来! 我会做家务500字作文篇三：星期天早上，爸爸上班去了，妈妈出去买菜，我一个人呆在家里挺无聊，心想：我还从来没做过家务，今天学着做吧。 我先在水龙头下接了半盆水，把毛巾浸湿，再拧干，就开始擦了。先擦客厅的擦几，电视机电视柜，再擦饭厅的饭桌。然后，又把毛巾洗干净擦卧室，我把爸爸妈妈房间里的床头，床头柜，
1、SPORTS 【英】A：Hello, 【Name】, it's a sunny day, right? B：Yes. 【Name】, what are you going to do? You know, such a good weather, what a pity if only stay at home. A: I can't agree with you more. I'm going to play badminton, and you? B: Badminton? Well, I feel poor in this kind of game. A: Don't worry. The beginning is always the hardest. I can teach you. B: I'd be glad to. But I perfect table tennis. A: Table tennis? I really don't know you can play table tennis. B: Actually, there are lots of things you don't know. A: What else you can play? B: I have wide of interests, such as reading, swimming, table tennis and so on. A: Well, I love swimming. I think swimming can help me making a good shape. B: And you can improve your stamina, and…anyway, lots of benefits. A: So, how about going to swim? B: Are you kidding? This is winter! It's too cold to jump into the water.
大学英语口语课堂游戏 游戏一：让一个同学在教室外面待着，然后教室里大家想出一个人，比如Obama、Harry potter，任何一个大家都熟知的名人，这个人就是外面那个同学，然后让外面那个人进来猜他自己是谁，差不多都是问Am I dead? Am I male or female?等问题，限时间猜出，猜出有奖，猜不出惩罚。 游戏二：把全班几十个人平均分成四组，每组负责一次party，要求很简单，只能用英语说，要包括一个由组员自己表演的幽默小品，还要有由其他同学参与的游戏等，要让每一个同学都加入进来，想出很多有创意的节目和游戏，让气氛活跃，每次20分钟，剩下十分钟由老师来点评给分。 游戏三：角色扮演（Role play）, 就是让学生学习表演性较强的课文片段，然后分角色表演出来。课堂上把学生分成几组，然后每组选派一名或二名代表进行表演，当然表演的过程都是用全英文表达，其余的同学欣赏，打分，并用英语进行简单的评价，得分最高者可获得教师事先准备好的小礼物。这种全班性互动教学，能调动起学生的运动记忆和表演欲，提高学生的表演才能，可以让学生记忆更加深刻，并营造良好的英语语言学习环境。 游戏四：将班级分成几组，先在组内讨论并商定好下步要谈论的内容，如：怎样做饭烧菜，煮咖啡等，然后让组内每个同学讲一步操作程序，在讲的过程中犯错误最少的组将获胜。 游戏五：口语接龙编故事，每人轮流用英语讲一句故事，讲不下去编不下去的同学就输了，罚唱一首歌。 游戏六：猜词游戏就是绝大多数学生自由发言来形容要猜的单词，通过游戏起到练习英语口语的目的。 单词游戏可以用接词尾：rain_ night_two_over_read_dear_room_mind 单词接龙 将全班分成若干组，每组来一个学生在黑板上写出一个以某字母为词首的单词，前一个单词的词尾字母作下一个单词的词首字母。在规定时间内哪一组接的词最多为优胜。如：pen-nice-eight-tea-an-no-or-right-teacher-radio-on-nor等 口语操练游戏可以用鹦鹉学舌Polly says:Put up your https://www.doczj.com/doc/7017060867.html,b your hair.由老师发出指令Polly says，让学生照鹦鹉说的话做，如果没有Polly says，而是直接说Put up your hands，学生照做了，就犯规，不得玩游戏，要下场，由其它同学替换．这是练习祈使句． 传话每一纵排为一组,全班分成若干组。老师分别发给每一组最后一排的学生一张纸,上面写一句话。在老师说“开始”后,最后一排的学生即用耳语把纸上的话告诉前面的学生，这位学生再把听到的话告诉前面的学生……这样依次进行下去。最后第一排的学生把所传的话写到黑板上或说出来。传得最快,最准确的组获胜。
口语对话 1赶时髦(go after fashion) A:Fashion show is around the corner,I’m so excited!时装表演即将来临，我很兴奋！ B:Are there any good!这有什么好的！ A:I didn't see anything wrong with the clothes;they looked pretty nice to me.I think you don’t like it!Why?我没看出衣服有任何问题；在我看来它们都很不错。我觉得你不喜欢！为什么？ B:It was dumb.I think it's stupid for women to wear clothes like that.这是愚蠢的。我认为女人们穿成那样是很愚蠢的。 A:The benevolent see benevolence and the wise see wisdom.仁者见仁，智者见智。B:Do you really think people can wear that stuff and walk around the streets?你真的认为人们可以穿那种东西走在街上？ A:Yes,I do.At least,some people certainly can.They wear high-fashion clothes to show off their sense of style and wealth.是的，我这样认为。至少，有人一定会。他们穿着时尚的衣服展示他们的时尚感和财富。 B:Well.I still think they're dumb.It makes more sense to spend the money on more practical purposes.我仍然认为他们是愚蠢的。把更多的钱花在更有意义的地方比较实际。 A:So you think it's bad if I wear it?所以你认为我穿成这样很不好吗？ B:If you wear it I must speak nice!如果你穿成这样我一定说它很好看！ A:I know you will say that.我就知道你会这样说。 B:Only you know me!知我者非你莫属！
作文网小编为你精选的作文： 记叙文 | 议论文 | 说明文 | 应用文 | 散文 | 美文 每个人都有着难忘的第一次，而我难忘的就是第一次做家务。下面是由小编为大家整理的“第一次做家务500字作文”欢迎阅读，仅供参考，希望对你有所帮助。 篇一：第一次做家务500字作文 每个人都经历过许多的事情，开心的，烦恼的，难忘的…… 在我经历的事情中，就像天上的繁星时时闪耀在我心头，伴我度过每一个快乐的日子。在这些事情中，让我对我印象最深刻的是我在读一年级的时候。 那是一个星期天，吃完了中午饭，妈妈对我说：“孩子，你能帮妈妈扫一次地吗？”我一听是扫地，我高兴得跳了起来，因为我早就想帮妈妈做家务了，再说扫地太简单了，就爽快的答应了妈妈。 我拿来扫把，开始在客厅里扫，我拿起扫帚扫过去扫过来，不知扫了多久，发现把这里扫干净了，其它地方脏了，把其它地方扫干净了，另外的地方又脏了，扫得我汗流浃背，不知怎么回事？这是妈妈走了过来笑眯眯地说：“孩子，像你这样扫地是扫不干净的。”“不就是扫过去扫过来嘛！”我还很不服气的说。“来，我教你，要从房间的四周往中间扫，不能把扫帚扬得太高，不能用太大的力，要不然你扫这里的时候会把灰尘扫到其它地方，扫其它地方的时候同样如此。”妈妈耐心的说道。 我听了还很不耐烦的样子，扫地还有那么多的讲究？我半信半疑地按照妈妈说的方法去扫，结果真的把房间扫得干干净净的，而且一点儿也不累。妈妈看了我的成果，开心地笑了，还连声夸奖我是一个聪明的孩子。我为能帮妈妈做好家务感到开心。 从这一次扫地，让我明白一个道理：做任何事情都要讲究方法，讲究技巧，只有这样，做每一件事情才能成功。 篇二：第一次做家务500字作文 从小到大，我一直是家里的小公主，从来不干家务活。可是今天，我长大了，所以，我决定做家务。 一个阳光灿烂的星期天，我刚从床上爬起来，就闻到被子里有一股汗骚味，妈妈说了，被套盖久了不洗就会发臭。我却很高兴，因为我有活干了。 我先把棉被套取下，那被套可真难取，要拉这边取那边的。我以前也见过妈妈洗被套，我便学着妈妈的样子，把被套放进洗澡盆里，装上一盆清水，然后打着赤脚踩进盆里。哎呀，这水可真凉！凉得我有从盆里跳出来。不过我觉得这也挺有意思的，于是再踏了进去。我扶着墙壁，像踩脚踏车似的踩被套。这被套真有趣，我踩左边，它右边就会鼓起一个包；我踩
大学英语口语课程教学大纲 一、层次与学制：本科，四年制 二、教学时数：134学时，第一、二、三、四学期开设；周学时 2 三、适用专业：非外语专业 四、课程性质与任务 本课程为一门重要的必修的基础技能课程，主要通过课堂口语实践，充分唤起学生学 习英语的兴趣，使学生通过吸收丰富的预料，快速提高听说能力，深入了解各国文化和社 会知识，训练学生用英语进行思考，有效培养他们的英语交际能力。 教师要根据我校应用型人才培养目标，对课程进行认真研究、深入挖掘，结合课程特 点、教学内容和当前形势及现实生活，对学生进行人生观、世界观、价值观和思想道德品 质教育，努力使学生在获得知识的同时学会做人的道理。 五、课程的目的与基本要求 本课程的教学目的是通过大量的口语练习和实践，逐步培养和提高学生用英语进行口 头交际的能力，同时帮助学生了解主要英语国家的文化和生活习俗。通过本课程的学习， 学生应能就日常生活中的一般情景进行恰当的交谈；能就社会生活中的一般性话题进行连 贯的发言；能比较准确地表达自己的思想，做到语音准确、语调自然、语法基本正确，语 言运用基本得体。将语言技能、语言知识、文化意识等要素有机地结合起来，相互促进、 循序渐进，帮助学生最终实现交际的目的。 本课程要求学生能够运用已掌握的词汇、句型和常用表达法，结合个人实际，较为流 畅地就一些常见话题发表个人见解。大多数学生应能通过本门课程的学习具备用英语介绍 信息、发表意见等基本交际能力。与此同时，本课程还将进一步强调学生的语言准确性和 表达多样性，使他们逐步脱离母语的负面影响。 六、教材与教学参考书 教材：《流畅英语口语教程》《Inside Out》第一、二、三、四册，Sue Kay & Vaughan Jones, Jon Hird，上海外语教育出版社（2007年4月）（特色教材） 教学参考书：每册包括学生用书、教师用书、拓展练习册、video CD录像、CD－ROM 多媒体光盘、测试光盘。 七、教学主要内容与学时分配 第一学期：每周2学时，共14周，28学时，完成第一册的13个单元，其中讲授学时 13学时，讨论训练学时 6.5学时，习题课、测验学时 6.5学时，期末考试2学时。
大学生英语口语对话短文 【篇一】大学生英语口语对话短文 Max： This is it has finally been on. I love it. 终于上映了.我太喜欢了. Jessie： Hello? You love Michael Jackson? He was pedophiliac. Wasn’t he sick? 不是吧.你喜欢迈克杰克逊? 他有恋童癖的.他变态的,好吧. Max：That’s not true. As the king of pop, he had always been the center of rumors. He loved children. 这不是真的.作为音乐教父,他一直都是各种谣言的中心.他喜欢小孩子. Jessie： He loved to sexually abuse them. 他喜欢性虐待小孩吧. Max：Hey, don’t ever criticize my idol. He had a very bad and lonely childhood. So he wanted to do what he can to help those unhappy children. However, some bastards used his kindness. 不要这样批评我的偶像. 他自己的童年很不好,很孤独.所以他想给协助那些不开心的孩子. 但是有些居心不良的人利用他的善良. Jessie： You are saying that he was blackmailed. Then what about the being white thing? 你是说他们勒索他. 那皮肤变白的事情呢?
为父母做家务心得感想作文500字_做家务心得体会作文 劳动有利于调节我们的大脑，让大脑得到休息。劳动有利于我们心灵手巧，提高 我们的创新能力。大家平时帮父母做家务有什么心得体会呢?以下是小编为大家准备的为父母做家务心得感想作文，欢迎大家前来参阅。 为父母做家务心得1 每天妈妈起早贪黑的上班，还要接送我和妹妹，真是很辛苦呀!今天晚上妈妈下班 回来，给我们烧的都是我和妹妹爱吃的菜，我和妹妹吃的津津有味。吃好了饭，我和 妈妈说：“让我来洗碗吧!你出去放松放松。”妈妈和爸爸去散步了。 我和妹妹开始行动起来。我先在水槽里放满了水，再把碗和筷子统统放进水槽里，在洗碗球上滴一些洗洁精，擦洗一遍。一旁的妹妹就在那里玩，我把水龙头打开，把 碗里放满水，里面的泡泡可遭了殃，手放碗里搅拌，泡泡一个个地爆炸了。我愤怒地说：“我辛辛苦苦地洗碗，你竟然在旁边玩!”妹妹委屈地说：“哥哥我现在不玩了，我们 俩一起好好的洗碗。” 妹妹吸取了教训，开始好好洗碗。我和妹妹一个人拿一个碗。我的碗就像一个调 皮的小毛孩，一下子从我的手里溜走了，掉进了水槽里，溅我一身水。一旁的妹妹偷 偷地嘲笑我，结果她自己的碗也掉进了水槽里，溅到了自己的脸上。我说妹妹你还笑 不笑了!”我们把水倒掉，再接一槽水，在里面清洗。碗里冲一冲，碗底冲一冲。不一 会儿，我们俩把碗和筷子洗得干干净净。 妈妈回来了，看见我们把碗洗的干干净净，用微笑的面孔表扬了我们，妈妈说：“你们真是妈妈的宝贝，是个乖巧懂事的好孩子。”
为父母做家务心得2 今天是星期天，我睡了个懒觉，九点钟才起来。下床一看，发现爸爸妈妈都出去了。吃完父母留给我的早餐后，望着脏兮兮的屋子，决定抓住这个机会锻炼一下自己。 我看了看灰尘很多的房间，准备先打扫房间。我拿来扫帚和撮箕，装着很在行的 样子，一甩一甩的，把灰尘都扬起来了。我呛得受不了，连忙打开窗户，灰尘全部随 着风飘出去了。 我又拿来拖把，把它弄得湿漉漉的，我专心致志地拖起来。因为地面太滑，我一 个踉跄，摔了个“狗啃泥”。过了一个小时，我坐在沙发上欣赏我的“杰作”：地板被打扫 的一尘不染，还闪着光呢。 这时，时钟已经指向了十一点，我肚子饿得咕咕叫，该煮饭了。我先淘米，把米 和水混在一起，不停地用手搅拌。淘干净后在电饭煲内放好水，插上插头，按上按钮。然后我从冰箱里拿出豆角，洗干净后，小心翼翼的切好放到锅里。我又往锅里倒了点 油和盐，炒起来。炒完豆角，我看了时间，急忙三步并两步跑到电饭煲旁，拔掉插头，慢慢的揭开锅盖，长出一口气，说：“还好没焦!” 等我做完菜，爸爸妈妈也回来了，他们看见我收拾好的房间，和桌子上的饭菜， 不停地说：“我们儿子长大了!”“哈哈哈……”全家沉浸在笑声当中。 这个星期天我做了一些力所能及的家务活，既能减轻父母的负担，又锻炼了我的 生活自理能力。做家务的感觉真好! 为父母做家务心得3 小时候，我们看着爸爸、妈妈在家里做家务活。那时，我又怎么会知道做家务是 如此不易啊! 有一天，我对爸爸、妈妈说道：“今天，由我来帮你们做家务吧!”爸爸、妈妈欣慰 地看着我，笑着说：“我们的儿子长大了。”首先我从客厅开始搞卫生。我拿起扫把把 客厅里的地扫干净，然后又用抹布把地板擦得一尘不染。 换过新的水之后，我又把桌子擦了一遍，那叫一个亮啊!搞完客厅，现在该打扫房 间以及阳台和厨房了。我心里想：天啊!光打扫一个客厅都这么费劲，想想爸爸、妈妈 是有多么不容易啊。