Production systems The Production System Cycle Conflict resolution Thermostat’s input-output behaviour Passenger input-output behaviour on the underground Fox’s input-output behaviour Confusion between production rules and logical implications:

The world Agent ObservationsActions forward chaining The production system view of the relationship between an agent and the world ???

Production systems Declarative memory consisting of atomic sentences, and Procedures consisting of condition-action rules: If conditions C, then do actions A. look like logical implications, but do not have a logical semantics. Production system cycle: read a current input, use forward chaining to match the input against the conditions C of production rules, perform conflict-resolution to choose a single rule if more than one rule is satisfied, and execute the associated actions A.

Production systems Condition-action rules are similar to the behavioural psychologist’s descriptions of behaviour. but are used instead to generate behaviour. Conclusions are often expressed in the imperative, rather than in the declarative mood: If conditions then do actions. Hence the need for “conflict resolution”. (But they can also be expressed as recommendations for actions.)

Conflict resolution Several conflicting actions can be derived at the same time. For example: If someone attacks me, then attack them back. If someone attacks me, then get help. If someone attacks me, then try to escape. The agent needs to use “conflict resolution” to decide what to do. Decision strategies can use: Order in which rules are written Priority to more specific rules over more general rules Decision theoretic computation of utilities and probabilites, choosing the action(s) that have highest expected utility.

Thermostat’s input-output behaviour can be described in condition-action terms: If current temperature is T degrees and target temperature is T’ degrees and T < T’ - 2° then the thermostat turns on the heat. If current temperature is T degrees and target temperature is T’ degrees and T > T’ + 2° then the thermostat turns off the heat.

Thermostat’s input-output behaviour generated by condition-action rules: If current temperature is T degrees and target temperature is T’ degrees and T < T’ - 2° then turn on the heat. If current temperature is T degrees and target temperature is T’ degrees and T > T’ + 2° then turn off the heat.

Passenger behaviour can be described in condition-action terms: If a passenger observes an emergency on the underground, then the passenger presses the alarm signal button. Passenger behaviour can be generated by condition-action rules: If I observe an emergency on the underground, then I press the alarm signal button.

Fox’s behaviour can described in condition-action terms: If the fox sees that the crow has cheese, then the fox praises the crow. If the fox is near the cheese, then the fox picks up the cheese. Fox’s behaviour can be generated by condition-action rules: If I see that the crow has cheese, then the I praise the crow. If I am near the cheese, then I pick up the cheese.

Three kinds of production rules Logical rules that are used to reason forward (modus ponens). Reactive rules that implement stimulus-response associations. Pro-active rules that simulate goal-reduction: If goal G and conditions C then add H as a sub-goal. Production rules have an operational, but not a logical semantics.

Reactive rules typically have implicit goals (i.e. emergent goals) If it’s raining, then carry an umbrella. Implicit goal:Stay dry If it’s clear ahead, then step forward. If there’s an obstacle ahead, then turn right. Implicit goal: Explore? If a car is rushing towards you, then jump out of its way. Implicit goal: Avoid an accident

The use of production systems to simulate goal-reduction The fox’s reduction of the goal of having an object can be simulated by the condition-action rule: If I want to have an object then add to my beliefs that I want to be near the object and pick up the object. The simulation approach looses the connection with the belief: I have an object if I am near the object and I pick up the object.

Forward chaining with pro-active rules is ad-hoc and potentially incomplete does not distinguish between conjunction and disjunction of sub-goals. Production rules have an operational, but not a logical semantics. Abductive logic programming (ALP) gives a logical semantics to production systems.

Example (Thagard, page 45) “Unlike logic, rule-based systems can also easily represent strategic information about what to do”: If you want to go home and you have the bus fare, then you can catch a bus. Forward reasoning with the rule simulates backward reasoning with the belief: You go home if you have the bus fare and you catch a bus.