Matthias Nickles, Michael Rovatsos, Marco Schmitt, Wilfried Brauer, Felix Fischer, Thomas Malsch, Kai Paetow and Gerhard Weiss (2007)
The Empirical Semantics Approach to Communication Structure Learning and Usage: Individualistic Vs. Systemic Views
Journal of Artificial Societies and Social Simulation
vol. 10, no. 1
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Received: 20-Jan-2006 Accepted: 05-Aug-2006 Published: 31-Jan-2007
Note: This article is accompanied by a glossary of the most important technical terms used in the context of our approach and Distributed Artificial Intelligence in general. The glossary is available by clicking on the underlined red terms.
|Table 1. A grammar for event nodes of ENs, generating the language (the language of concrete actions, starting with ).|
with is the set of nodes,
are the edges of . is a tree called expectation tree. shall have a unique root node called which corresponds to the first ever observed action. The following condition should hold:
is the action language. As defined in table 1, actions can be symbolic () or physical actions (). While we take the existence and the meaning of the latter in terms of resulting observer expectations for granted and assume it is domain-dependent, the former will be described in detail later. Physical actions could be assigned an empirical semantics also (being their expected consequences in terms of subsequent events).
is the action label function for nodes, with
(where shall be iff its arguments are syntactically unifiable. (Cf. Nickles et al 2005 for the use of variables in ENs),
returns the edges' expectabilities. For convenience, we define .
Paths starting with are called states (of the communication process).
is the formal language used for agent actions (according to table 1),
is the expectations update function that transforms any expectation
to a new network upon experience of an action
returns the so-called initial EN, transformed by the observation of
This initial EN can be used for the pre-structuring of the social system using
given e.g. social norms or other a priori knowledge which can not be
Any ENs resulting from an application of
are called Social Interaction Structures.
As a non-incremental variant we define to be
is the list of all actions observed until time t. The subindices of the impose a linear order on the actions corresponding to the times they have been observed,
is a duration greater of equal to the expected life span of the SIS. We require this to calculate the so-called spheres of communication (see below). If the life time is unknown, we set . Although a sphere of communication denotes the ultimate boundaries of trustability for a single communication, even with initially certain limits of trustability and sincerity become visible in empirical semantics by means of the extrapolation of interaction sequences. Suppose, e.g., a certain agent takes opinion in discourses with agent , but opinion in all interactions with agent . Since this "opinion switching" shows regularities, our algorithm will reveal it.
We refer to events and EN nodes as past, current or
future depending on their timely position (or the timely position of
their corresponding node, respectively) before, at or after
We refer to
as the current EN from the semantics observer's point of view, if the
semantics observer has observed exactly the sequence
of events so far.
The intuition behind our definition of is that a social interaction system can be characterised by how it would update an existing expectation network upon newly observed actions . The EN within can thus be computed through the sequential application of the structures update function for each action within , starting with a given expectation network which models the observers' a priori knowledge. is called the context (or precondition) of the action observed at time .
To simplify the following formalism, we demand that an EN ought to be implicitly complete, i.e., to contain all possible paths, representing all possible event sequences (thus the EN within an interaction system is always infinite and represents all possible world states, even extremely unlikely ones). If the semantics observer has no a priori knowledge about a certain branch, we assume this branch to represent uniform distribution and thus a very low probability for every future decision alternative (), if the action language is not trivially small.
Note that any part of an EN of an SIS does describe exactly one time period, i.e., each node within the respective EN corresponds to exactly one moment on the time scale in the past or the future of observation or prediction, respectively, whereas this is not necessarily true of ENs in general. For simplicity, and to express the definiteness of the past, we will define the update function such that the a posteriori expectabilities of past events (i.e., observations) become 1 (admittedly leading to problems if the past is unknown or contested, or we would like to allow contested assertive ECAs about the past). There shall be exactly one path in the current EN leading from start node leading to a node such that and . The node and the ECA are said to correspond to each other.
for all . The denote single event labels along , i.e., (for analogously).
GoalStates chooses, using an EN path (without expectabilities), the (possibly infinite) set of states of the expectation network the uttering agent is expected to strive for. The uttered GoalStates path must match with a set of paths within the EN such that the last node of each match is a node of an EN branch that has a condition node from Conditions as its root. Both in Conditions and GoalStates paths, wildcards "?" for single actions are allowed.
|Figure 1. An EN with projections and a sphere of communication|
Capturing these intentions, and given the set of projections for each ECA
`uttered by an agent
we calculate the semantics of ECAs using the following two principles.
roles held by the interacting parties and relationships between them,
trajectories that describe the observable surface structure of the interaction, and
context and belief conditions that need to be fulfilled for the respective frame to be enacted.
Interpreting the current interaction situation in terms of a perceived frame and matching it against the normative model of the active frame which determines what the interaction should look like.
Assessing the current active frame (based on whether its conditions are currently met, whether its surface structure resembles the perceived interaction sequence, and whether it serves the agent's own goals).
Deciding on whether to retain the current active frame or whether to re-frame (i.e. to retrieve a more suitable frame from one's frame repository or to adjust an existing frame model to match the current interaction situation and the agent's current needs) on the grounds of the previous assessment phase.
Using the active frame to determine one's next (communicative/social) action, i.e. apply the active frame as a prescriptive model of social behaviour in the current interaction encounter.
only considers two-party turn-taking interaction episodes called encounters which have clear start and termination conditions. In other words, agents interact by initiating a "conversation" with a single other agent, exchanging a number of messages in a strictly turn-taking fashion, and can unambiguously determine when this conversation is finished.
All social reasoning activity is conducted within the horizon of the current encounter. This implies that subsequent agent dialogues are not related to each other, which is equivalent to limiting the EN that would result from constructing an expectation structure from interaction experience to a certain depth.
ECAs are equivalent to elementary utterances, i.e. each utterance is considered an independent and self-contained communicative action (there are no composite ECAs) that is taken as a primitive in forming expectations.
The trajectory of every frame is a sequence of message patterns (i.e. speech-act like messages which may contain variables for sender, receiver and (parts of their) content) that describes the surface structure of a particular class of interaction encounters. Thus, an individual frame trajectory corresponds to a path on an EN, and while the whole set of frames (or frame repository) an agent may dispose of is equivalent to a tree, whenever the agent activates a single frame he disregards all other possible paths of execution and reasons only about the degrees of freedom provided by a single frame (at least until the next re-framing process). This helps to greatly reduce the complexity of the expectation structure reasoned about at least as long as the currently active frame can be upheld (e.g. it is not applicable/desirable anymore).
In addition to its trajectory model, each frame keeps track of the number of encounters that matched (prefixes of) that trajectory (which is the simplest possible method to derive transition probabilities in the EN view), and lists of corresponding variable substitutions/logical conditions to record the values variables had in previous enactments of the frame and the conditions that held true at the time of enactment.
Agents maintain a set of these frames instead of an EN but the size of this frame repository is implicitly bounded because agents apply generalisation techniques (which make use of heuristics from the area of cluster validation) to represent similar encounters by a single, (by virtue of replacing instance values by variables) more abstract frame whenever this seems appropriate (Fischer 2003). What this means is that agents are not allowed to grow arbitrarily large ENs and are instead forced to coerce their experience into a frame repository of manageable size.
The SIS initialisation and update mechanism is fairly simple. Agents start out with a frame repository specified a priori by the human designer, and simply add every new encounter they experience to this repository in the form of a new frame unless it can be subsumed under an existing frame as a new substitution/condition pair or the generalisation methods mentioned above suggest abstracting from some existing frame conception to accommodate the new observation (in which case it also simply becomes a substitution/condition pair in the newly created, more abstract frame). While it is possible in theory to observe third-party encounters in which one is not directly involved as a participant this method is not used at present.
We assume that each agent can assess the usefulness of any sequence of messages and physical action (i.e. ground instance of any frame trajectory) using a real-valued utility function. This facilitates the application of decision-theoretic principles (note, however, that this utility estimate need not be equivalent to the rewards received from the environment and is just thought to provide the agent with hints as to which possible future interaction sequences to prefer).
The agent's decision-making process is modelled as a two-level Markov Decision Process (MDP) (Puterman 1994). At the frame selection level, agents pick the most appropriate frame according to its long-term utility and the current state. For this purpose, we apply the options framework (Precup 2000) for hierarchical reinforcement learning to interpret encounter sequences as macro-actions in the MDP sense and to approximate the value of each frame in each state through experience. We assume that the rewards received from the environment after an interaction encounter depend only on the physical (i.e. environment-manipulating) actions that were performed during that encounter (however, non-physical actions are assigned a small negative utility to prevent endless conversations that do not result in physical action). At the "lower" action selection level, the agent seeks to optimise her choices given the degrees of freedom that the current frame still offers. These are defined by the variables contained (and still unbound by the encounter so far) in the remaining steps of the trajectory model of the active defined. Here, using a (domain-dependent) similarity measure over messages and considering past cases as stored in the substitution/condition lists of the active frame, we are able to derive probabilities for the possible outcomes of the frame (and for the moves the other party might make within it). Together with the utility estimates for each of these predictions, agents can then choose the action that maximises the expected utility of the encounter to be performed in the next step.
This frame reflects the following interaction experience: asked five times to perform (physical) action , out of which actually did so in three instances. In two of successful instances, it was who asked and who headed the request, and the action was to pay $100. In both cases, held true. In the third case, roles were swapped between and and the amount remains unspecified (which does not mean that it did not have a concrete value, but that this was abstracted away in the frame). Note that in such frames it is neither required that the reasoning agent is either of or , nor that all the trajectory variables are substituted by concrete values. Also, trajectories may be specified at different levels of abstraction. Finally, any frame will only give evidence of successful completions of the trajectory, i.e. information about the three requests that were unsuccessful have to be stored in a different frame.
In the reasoning cycle, the reasoning agent enters the framing loop whenever sub-social (e.g. BDI) reasoning processes generate a goal that requires actions to be taken that cannot perform herself. (If is already engaged in an ongoing conversation, this step is skipped.) From all those frames contained in her frame repository she then picks the frame that (1) achieves the goal, (2) is executable in the current state of affairs and (3) has proven reliable and utile in the past (this is done using the reinforcement learning methods described above). Let us assume that frame is the example frame used above. In a decision-making step that does not mark the initiation of a new encounter (i.e. if the interaction has already started), would also have to ensure that the frames considered for selection match the current "encounter prefix", i.e. the initial sequence of messages already uttered.
Once the frame has been selected, has to make an optimal choice regarding the specific choices for variables the frame may contain. In the case of this is trivial, because if is already instantiated with the action wants to perform, then the frame leaves no further degrees of freedom. However, if, for example, the frame contained additional steps and/or variables(e.g. an exchange of arguments before actually agrees to perform ), would compute probabilities and utility estimates for each ground instance of the "encounter postfix" (the steps still to be executed along the current frame trajectory) to be able to chose that next action to perform which maximises the expected "utility-to-go" of the encounter.
The process of reasoning about specific action choices within the bounds of a single frame of course involves reasoning about the actions the other party will perform, i.e. it has to be borne in mind that some of the variables in the postfix sequence message patterns will be "selected" by the opponent.
As the encounter unfolds, either of the two parties (or both) may find that the current active frame is not appropriate anymore, and that there is a need to re-frame. Three different reasons may lead to re-framing which spawns a process that is similar to frame selection at the start of an encounter:
The other party has made an utterance that does not match the message pattern that was expected according to the active frame.
At least one of the physical actions along the postfix sequence is not executable anymore because some of its pre-conditions are not fulfilled (and not expected to become true until they are needed).
No ground instance of the remaining trajectory steps seems desirable utility-wise.
While the first two cases are straightforward in the sense that they clearly necessitate looking for an alternative frame, the last step largely depends on the "social attitude" of the agent, and is closely related to issues of social order as discussed in section 2. Obviously, if agents were only to select frames that provide some positive profit to them, cooperation would be quite unlikely, and also they would also be prone to missing opportunities for cooperation because they do not "try out" frames to see how profitable they are in practice.
To balance social expectations as captured by the current set of frames with the agent's private needs, we have developed an entropy-based heuristics for trading off framing utility against framing reliability (Rovatsos et al 2003). Using this heuristics, the agent will occasionally consider frames that do not yield an immediate profit, if this is considered useful to increase mutual trust in existing expectations.
Finally, agents terminate the encounter when the last message on the trajectory of the active frame has been executed (unless the other party sends another message, in which we have to re-frame again). Whenever no suitable frame can be found in the trajectory that matches the perceived message sequence, this sequence is stored as a new frame, i.e. agents are capable of learning frames that are new altogether.
Note that in these simulations, agents have the possibility to reject any proposal, so in principle they can avoid any undesirable agreement. However, this does not imply that they will adhere to the frames, because they might be insincere and not execute actions they have committed themselves because their private desirability considerations suggest different utility values from those expected when the agreement was reached (or simply because they calculated that lying is more profitable than keeping one's promises).
Add link from agent1's site to agent2's site with weight w
--> do(addLink(agent1, agent2, w))
Remove link from agent1's site to agent2's site
--> do(deleteLink(agent1, agent2))
Modify the weight of an existing link
--> do(modifyRating(agent1, agent2, w))
agent1 asks agent2 to perform act(...)
--> project(agent1, agent2, do(act(...))
agent1 agrees to perform act(...)
--> project(agent1, agent1, act(...)) I.e., "accept" means to project the fulfilment of a previously requested action (a request of agent1 to do something by herself, so to say). This can also be done implicitely by performing the requested action.
agent1 rejects a request or proposal
--> project(agent1, agent2, not act(...))
agent1 proposes to perform by herself act(...)
--> project(agent1, agent2, do(act(...))
request(agent2, agent1, addLink(agent1,agent2,4))
request(agent1, agent2, addLink(agent2,agent1,3))
request(agent1, agent2, addLink(agent2,agent1,3))
In order to estimate the performance of the EN-based prediction algorithm applied to action sequences like this, we performed several experiments. ENs of both types were retrieved from prefixes of the whole protocol as empirical evidence, and then parts of the rest of the protocols (i.e., the actual continuations of the conversation) were compared with paths within the ENs to yield an estimation for the prediction achievement. The comparisons were performed automatically, since the resulting ENs were mostly to complex to evaluate them manually.
|Figure 2. Prediction of communication sequences (example 1)|
|Figure 3. Prediction of non-symbolic actions (example 1)|
|Figure 4. Prediction of communication sequences (example 2)|
|Figure 5. Prediction of non-symbolic actions (example 2)|
|Figure 6. Prediction of non-symbolic actions (example 3)|
request(agent1, agent2,addLink(agent2,agent1,3))by accepting the counter proposal (
request(agent2, agent1, addLink(agent1,agent2,4))) .
addLink(agent2,agent1,3)within this EN, the maximum expectation is taken and plotted on the y-axis. This is done both purely-empirically (green curve) and empirically-rational (cyan curve). Figure 3 thus shows the overall probability of fulfilling agent 1's request. This yields roughly the same result as above (Figure 2), but is for technical reasons much easier to compute in case the predicted sequence (the 20% at the end of the example sequence here) is very long. Figure 4 and 5 show such a case (rather extreme, for clearness), obtained from a protocol of 236 LIESON actions. Here, Figure 5 shows a good predictability, whereas Figure 4 predicts the protocol remainder only if at least 80% of the protocol is given as evidence, i.e., if the predicted sequence and the evidence overlap.
2 Of course, the (expectation of) triggered behaviour can trigger (the expectation of) other agent's behaviour and so on.
3 Actually, two different paths can have the same semantics in terms of their expected continuations, a fact which could be used to reduce the size of the EN by making them directed graphs with more than one path leading to a node instead of trees as in this work.
4 To be precise, a single utterance might be split into several so-called elementary communication acts, each corresponding to a dedicated EN node.
5We assume a discrete time scale with , and that no pair of actions is performed at the same time (quasi-parallel events achieved through a highly fine grained time scale), and that the expected action time corresponds with the depth of the respective node within in the EN.
6A future version of our framework might allow the utterance of whole ENs as projections, in order to freely project new expectabilities or even introduce novel event types not found in the current EN.
7This time span of projection trustability can be very short though — think of joke questions.
8This probability distribution must also cover projected events and assign them a (however low) probability even if these events are beyond the spheres of communication, because otherwise it would be impossible to calculate the rational hull.
9We regard the identification of the necessity of combining these two (admittedly closely related) theories to be able to produce adequate computational models of reasoning about interaction as one of the major insights of our research that sociologists can benefit from. This nicely illustrates the bi-directional benefits of transdisciplinary collaboration in the Socionics research programme.
10In particular, Rovatsos et al (2004) describes both models and their theoretical foundations in detail and includes an account of an extensive experimental validation of the approach.
11In an AI planning sense, agents will also activate frames that achieve sub-goals towards some more complex goal, but we ignore this case here for simplicity.
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